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pattern recognition</title> <meta name="description" content="Search results for: intuitive pattern recognition"> <meta name="keywords" content="intuitive pattern recognition"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="intuitive pattern recognition"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4215</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: intuitive pattern recognition</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4215</span> An Integrated Cognitive Performance Evaluation Framework for Urban Search and Rescue Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antonio%20D.%20Lee">Antonio D. Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Steven%20X.%20Jiang"> Steven X. Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A variety of techniques and methods are available to evaluate cognitive performance in Urban Search and Rescue (USAR) applications. However, traditional cognitive performance evaluation techniques typically incorporate either the conscious or systematic aspect, failing to take into consideration the subconscious or intuitive aspect. This leads to incomplete measures and produces ineffective designs. In order to fill the gaps in past research, this study developed a theoretical framework to facilitate the integration of situation awareness (SA) and intuitive pattern recognition (IPR) to enhance the cognitive performance representation in USAR applications. This framework provides guidance to integrate both SA and IPR in order to evaluate the cognitive performance of the USAR responders. The application of this framework will help improve the system design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20performance" title="cognitive performance">cognitive performance</a>, <a href="https://publications.waset.org/abstracts/search?q=intuitive%20pattern%20recognition" title=" intuitive pattern recognition"> intuitive pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=situation%20awareness" title=" situation awareness"> situation awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20search%20and%20rescue" title=" urban search and rescue"> urban search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/42030/an-integrated-cognitive-performance-evaluation-framework-for-urban-search-and-rescue-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4214</span> Handwriting Recognition of Gurmukhi Script: A Survey of Online and Offline Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravneet%20Kaur">Ravneet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character recognition is a very interesting area of pattern recognition. From past few decades, an intensive research on character recognition for Roman, Chinese, and Japanese and Indian scripts have been reported. In this paper, a review of Handwritten Character Recognition work on Indian Script Gurmukhi is being highlighted. Most of the published papers were summarized, various methodologies were analysed and their results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmukhi%20character%20recognition" title="Gurmukhi character recognition">Gurmukhi character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=online" title=" online"> online</a>, <a href="https://publications.waset.org/abstracts/search?q=offline" title=" offline"> offline</a>, <a href="https://publications.waset.org/abstracts/search?q=HCR%20survey" title=" HCR survey"> HCR survey</a> </p> <a href="https://publications.waset.org/abstracts/46337/handwriting-recognition-of-gurmukhi-script-a-survey-of-online-and-offline-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4213</span> Pattern Recognition Search: An Advancement Over Interpolation Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahpar%20Yilmaz">Shahpar Yilmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasir%20Nadeem"> Yasir Nadeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Mehdi"> Syed A. Mehdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching for a record in a dataset is always a frequent task for any data structure-related application. Hence, a fast and efficient algorithm for the approach has its importance in yielding the quickest results and enhancing the overall productivity of the company. Interpolation search is one such technique used to search through a sorted set of elements. This paper proposes a new algorithm, an advancement over interpolation search for the application of search over a sorted array. Pattern Recognition Search or PR Search (PRS), like interpolation search, is a pattern-based divide and conquer algorithm whose objective is to reduce the sample size in order to quicken the process and it does so by treating the array as a perfect arithmetic progression series and thereby deducing the key element’s position. We look to highlight some of the key drawbacks of interpolation search, which are accounted for in the Pattern Recognition Search. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=array" title="array">array</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=sorting" title=" sorting"> sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=space" title=" space"> space</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/142819/pattern-recognition-search-an-advancement-over-interpolation-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142819.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">243</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4212</span> Improved Dynamic Bayesian Networks Applied to Arabic On Line Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/7319/improved-dynamic-bayesian-networks-applied-to-arabic-on-line-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4211</span> A Weighted Approach to Unconstrained Iris Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authentication" title="authentication">authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=adaboost" title=" adaboost"> adaboost</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a> </p> <a href="https://publications.waset.org/abstracts/3876/a-weighted-approach-to-unconstrained-iris-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4210</span> Efficient Feature Fusion for Noise Iris in Unconstrained Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/17027/efficient-feature-fusion-for-noise-iris-in-unconstrained-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17027.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4209</span> Pattern Identification in Statistical Process Control Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Pramila%20Devi">M. Pramila Devi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20V.%20N.%20Indra%20Kiran"> N. V. N. Indra Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart%20pattern%20recognition" title="control chart pattern recognition">control chart pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalization" title=" generalization"> generalization</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20stopping" title=" early stopping"> early stopping</a> </p> <a href="https://publications.waset.org/abstracts/6307/pattern-identification-in-statistical-process-control-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4208</span> Defect Localization and Interaction on Surfaces with Projection Mapping and Gesture Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Wang">Qiang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=MingRong%20Lai"> MingRong Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for accurately localizing and interacting with known surface defects by overlaying patterns onto real-world surfaces using a projection system. Given the world coordinates of the defects, we project corresponding patterns onto the surfaces, providing an intuitive visualization of the specific defect locations. To enable users to interact with and retrieve more information about individual defects, we implement a gesture recognition system based on a pruned and optimized version of YOLOv6. This lightweight model achieves an accuracy of 82.8% and is suitable for deployment on low-performance devices. Our approach demonstrates the potential for enhancing defect identification, inspection processes, and user interaction in various applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defect%20localization" title="defect localization">defect localization</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20mapping" title=" projection mapping"> projection mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title=" gesture recognition"> gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv6" title=" YOLOv6"> YOLOv6</a> </p> <a href="https://publications.waset.org/abstracts/165856/defect-localization-and-interaction-on-surfaces-with-projection-mapping-and-gesture-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4207</span> Printed Thai Character Recognition Using Particle Swarm Optimization Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phawin%20Sangsuvan">Phawin Sangsuvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition%20techniques" title=" pattern recognition techniques "> pattern recognition techniques </a> </p> <a href="https://publications.waset.org/abstracts/25613/printed-thai-character-recognition-using-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4206</span> Auto Classification of Multiple ECG Arrhythmic Detection via Machine Learning Techniques: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ng%20Liang%20Shen">Ng Liang Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Hau%20Yuan%20Wen"> Hau Yuan Wen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=QRS" title=" QRS"> QRS</a> </p> <a href="https://publications.waset.org/abstracts/58871/auto-classification-of-multiple-ecg-arrhythmic-detection-via-machine-learning-techniques-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4205</span> An Improved Face Recognition Algorithm Using Histogram-Based Features in Spatial and Frequency Domains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiu%20Chen">Qiu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Koji%20Kotani"> Koji Kotani</a>, <a href="https://publications.waset.org/abstracts/search?q=Feifei%20Lee"> Feifei Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadahiro%20Ohmi"> Tadahiro Ohmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20vector%20quantization%20%28BVQ%29" title="binary vector quantization (BVQ)">binary vector quantization (BVQ)</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT%20coefficients" title="DCT coefficients">DCT coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20patterns%20%28LBP%29" title=" local binary patterns (LBP)"> local binary patterns (LBP)</a> </p> <a href="https://publications.waset.org/abstracts/44892/an-improved-face-recognition-algorithm-using-histogram-based-features-in-spatial-and-frequency-domains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4204</span> Pattern Recognition Based on Simulation of Chemical Senses (SCS)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nermeen%20El%20Kashef">Nermeen El Kashef</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20Fouad"> Yasser Fouad</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Mahar"> Khaled Mahar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=biocybernetics" title=" biocybernetics"> biocybernetics</a>, <a href="https://publications.waset.org/abstracts/search?q=gustatory%20system" title=" gustatory system"> gustatory system</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language%20recognition" title=" sign language recognition"> sign language recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=taste%20sense" title=" taste sense"> taste sense</a> </p> <a href="https://publications.waset.org/abstracts/40814/pattern-recognition-based-on-simulation-of-chemical-senses-scs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4203</span> Advances in Artificial intelligence Using Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20phonetic" title=" acoustic phonetic"> acoustic phonetic</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models%20%28HMM%29" title=" hidden markov models (HMM)"> hidden markov models (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20models%20of%20speech%20recognition" title=" statistical models of speech recognition"> statistical models of speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20machine%20performance" title=" human machine performance"> human machine performance</a> </p> <a href="https://publications.waset.org/abstracts/26319/advances-in-artificial-intelligence-using-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4202</span> Video Based Automatic License Plate Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ganoun">Ali Ganoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Wesam%20Algablawi"> Wesam Algablawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wasim%20BenAnaif"> Wasim BenAnaif </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain trafﬁc and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=license%20plate%20recognition" title="license plate recognition">license plate recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition" title=" recognition"> recognition</a> </p> <a href="https://publications.waset.org/abstracts/9958/video-based-automatic-license-plate-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4201</span> Optimized Dynamic Bayesian Networks and Neural Verifier Test Applied to On-Line Isolated Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Redouane"> Redouane</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Kouninef"> Belkacem Kouninef</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks "> networks </a> </p> <a href="https://publications.waset.org/abstracts/34593/optimized-dynamic-bayesian-networks-and-neural-verifier-test-applied-to-on-line-isolated-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">618</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4200</span> A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salim%20Ouchtati">Salim Ouchtati</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Sequeira"> Jean Sequeira</a>, <a href="https://publications.waset.org/abstracts/search?q=Mouldi%20Bedda"> Mouldi Bedda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=handwritten%20word%20recognition" title="handwritten word recognition">handwritten word recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction "> features extraction </a> </p> <a href="https://publications.waset.org/abstracts/29848/a-neural-approach-for-the-offline-recognition-of-the-arabic-handwritten-words-of-the-algerian-departments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29848.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4199</span> Grid Pattern Recognition and Suppression in Computed Radiographic Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Igor%20Belykh">Igor Belykh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grid" title="grid">grid</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20radiography" title=" computed radiography"> computed radiography</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a> </p> <a href="https://publications.waset.org/abstracts/7833/grid-pattern-recognition-and-suppression-in-computed-radiographic-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7833.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4198</span> A Communication Signal Recognition Algorithm Based on Holder Coefficient Characteristics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hui%20Zhang">Hui Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Tian"> Ye Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Ye"> Fang Ye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziming%20Guo"> Ziming Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20signal" title="communication signal">communication signal</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=Holder%20coefficient" title=" Holder coefficient"> Holder coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20cloud%20model" title=" improved cloud model"> improved cloud model</a> </p> <a href="https://publications.waset.org/abstracts/101463/a-communication-signal-recognition-algorithm-based-on-holder-coefficient-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101463.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4197</span> Foot Recognition Using Deep Learning for Knee Rehabilitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rakkrit%20Duangsoithong">Rakkrit Duangsoithong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jermphiphut%20Jaruenpunyasak"> Jermphiphut Jaruenpunyasak</a>, <a href="https://publications.waset.org/abstracts/search?q=Alba%20Garcia"> Alba Garcia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=foot%20recognition" title="foot recognition">foot recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=knee%20rehabilitation" title=" knee rehabilitation"> knee rehabilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a> </p> <a href="https://publications.waset.org/abstracts/105495/foot-recognition-using-deep-learning-for-knee-rehabilitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105495.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4196</span> Gesture-Controlled Interface Using Computer Vision and Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Vardhan%20Rathour">Vedant Vardhan Rathour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anant%20Agrawal"> Anant Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20tracking" title=" hand tracking"> hand tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/193844/gesture-controlled-interface-using-computer-vision-and-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193844.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">12</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4195</span> High Speed Image Rotation Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hee-Choul%20Kwon">Hee-Choul Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyungjin%20Cho"> Hyungjin Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Heeyong%20Kwon"> Heeyong Kwon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20speed%20rotation%20operation" title="high speed rotation operation">high speed rotation operation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20rotation" title=" image rotation"> image rotation</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation%20matrix" title=" transformation matrix"> transformation matrix</a> </p> <a href="https://publications.waset.org/abstracts/25258/high-speed-image-rotation-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25258.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4194</span> Application of Pattern Recognition Technique to the Quality Characterization of Superficial Microstructures in Steel Coatings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Gonzalez-Rivera">H. Gonzalez-Rivera</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Palmeros-Torres"> J. L. Palmeros-Torres</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the application of traditional computer vision techniques as a procedure for automatic measurement of the secondary dendrite arm spacing (SDAS) from microscopic images. The algorithm is capable of finding the lineal or curve-shaped secondary column of the main microstructure, measuring its length size in a micro-meter and counting the number of spaces between dendrites. The automatic characterization was compared with a set of 1728 manually characterized images, leading to an accuracy of −0.27 µm for the length size determination and a precision of ± 2.78 counts for dendrite spacing counting, also reducing the characterization time from 7 hours to 2 minutes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dendrite%20arm%20spacing" title="dendrite arm spacing">dendrite arm spacing</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure%20inspection" title=" microstructure inspection"> microstructure inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20regression" title=" polynomial regression"> polynomial regression</a> </p> <a href="https://publications.waset.org/abstracts/184692/application-of-pattern-recognition-technique-to-the-quality-characterization-of-superficial-microstructures-in-steel-coatings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">46</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4193</span> Implementing Search-Based Activities in Mathematics Instruction, Grounded in Intuitive Reasoning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhanna%20Dedovets">Zhanna Dedovets</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fostering a mathematical style of thinking is crucial for cultivating intellectual personalities capable of thriving in modern society. Intuitive thinking stands as a cornerstone among the components of mathematical cognition, playing a pivotal role in grasping mathematical truths across various disciplines. This article delves into the exploration of leveraging search activities rooted in students' intuitive thinking, particularly when tackling geometric problems. Emphasizing both student engagement with the task and their active involvement in the search process, the study underscores the importance of heuristic procedures and the freedom for students to chart their own problem-solving paths. Spanning several years (2019-2023) at the Physics and Mathematics Lyceum of Dushanbe, the research engaged 17 teachers and 78 high school students. After assessing the initial levels of intuitive thinking in both control and experimental groups, the experimental group underwent training following the authors' methodology. Subsequent analysis revealed a significant advancement in thinking levels among the experimental group students. The methodological approaches and teaching materials developed through this process offer valuable resources for mathematics educators seeking to enhance their students' learning experiences effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=teaching%20of%20mathematics" title="teaching of mathematics">teaching of mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=intuitive%20thinking" title=" intuitive thinking"> intuitive thinking</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20procedures" title=" heuristic procedures"> heuristic procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20problem" title=" geometric problem"> geometric problem</a>, <a href="https://publications.waset.org/abstracts/search?q=students." title=" students."> students.</a> </p> <a href="https://publications.waset.org/abstracts/185467/implementing-search-based-activities-in-mathematics-instruction-grounded-in-intuitive-reasoning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">46</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4192</span> An Approach for Reducing Morphological Operator Dataset and Recognize Optical Character Based on Significant Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashis%20Pradhan">Ashis Pradhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohan%20P.%20Pradhan"> Mohan P. Pradhan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pattern Matching is useful for recognizing character in a digital image. OCR is one such technique which reads character from a digital image and recognizes them. Line segmentation is initially used for identifying character in an image and later refined by morphological operations like binarization, erosion, thinning, etc. The work discusses a recognition technique that defines a set of morphological operators based on its orientation in a character. These operators are further categorized into groups having similar shape but different orientation for efficient utilization of memory. Finally the characters are recognized in accordance with the occurrence of frequency in hierarchy of significant pattern of those morphological operators and by comparing them with the existing database of each character. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20image" title="binary image">binary image</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20patterns" title=" morphological patterns"> morphological patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20count" title=" frequency count"> frequency count</a>, <a href="https://publications.waset.org/abstracts/search?q=priority" title=" priority"> priority</a>, <a href="https://publications.waset.org/abstracts/search?q=reduction%20data%20set%20and%20recognition" title=" reduction data set and recognition"> reduction data set and recognition</a> </p> <a href="https://publications.waset.org/abstracts/30867/an-approach-for-reducing-morphological-operator-dataset-and-recognize-optical-character-based-on-significant-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4191</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4190</span> Integrated Gesture and Voice-Activated Mouse Control System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dev%20Pratap%20Singh">Dev Pratap Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Harshika%20Hasija"> Harshika Hasija</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashwini%20S."> Ashwini S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computers using hand gestures and voice commands. The system leverages advanced computer vision techniques using the Media Pipe framework and OpenCV to detect and interpret real-time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the speech recognition library allows for seamless execution of tasks like web searches, location navigation, and gesture control in the system through voice commands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20tracking" title=" hand tracking"> hand tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20assistant" title=" voice assistant"> voice assistant</a> </p> <a href="https://publications.waset.org/abstracts/193896/integrated-gesture-and-voice-activated-mouse-control-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">10</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4189</span> GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Cheng">Lin Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zijiang%20Yang"> Zijiang Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=program%20synthesis" title="program synthesis">program synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20chart" title=" flow chart"> flow chart</a>, <a href="https://publications.waset.org/abstracts/search?q=specification" title=" specification"> specification</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20recognition" title=" graph recognition"> graph recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/124641/grcnn-graph-recognition-convolutional-neural-network-for-synthesizing-programs-from-flow-charts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4188</span> Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yalong%20Jiang">Yalong Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheru%20Chi"> Zheru Chi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=capsule%20network" title=" capsule network"> capsule network</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20optimization" title=" capacity optimization"> capacity optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title=" character recognition"> character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a> </p> <a href="https://publications.waset.org/abstracts/95551/optimizing-the-capacity-of-a-convolutional-neural-network-for-image-segmentation-and-pattern-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4187</span> Improved Feature Extraction Technique for Handling Occlusion in Automatic Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadijat%20T.%20Bamigbade">Khadijat T. Bamigbade</a>, <a href="https://publications.waset.org/abstracts/search?q=Olufade%20F.%20W.%20Onifade"> Olufade F. W. Onifade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20facial%20expression%20analysis" title="automatic facial expression analysis">automatic facial expression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=LBP-HOG" title=" LBP-HOG"> LBP-HOG</a>, <a href="https://publications.waset.org/abstracts/search?q=occlusion%20detection" title=" occlusion detection"> occlusion detection</a> </p> <a href="https://publications.waset.org/abstracts/105048/improved-feature-extraction-technique-for-handling-occlusion-in-automatic-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105048.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4186</span> A Comparative Study of k-NN and MLP-NN Classifiers Using GA-kNN Based Feature Selection Method for Wood Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uswah%20Khairuddin">Uswah Khairuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubiyah%20Yusof"> Rubiyah Yusof</a>, <a href="https://publications.waset.org/abstracts/search?q=Nenny%20Ruthfalydia%20Rosli"> Nenny Ruthfalydia Rosli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wood%20recognition%20system" title=" wood recognition system "> wood recognition system </a> </p> <a href="https://publications.waset.org/abstracts/25573/a-comparative-study-of-k-nn-and-mlp-nn-classifiers-using-ga-knn-based-feature-selection-method-for-wood-recognition-system" class="btn btn-primary 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