Image and Video Processing

<!DOCTYPE html> <html lang="en"> <head> <title>Image and Video Processing </title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link 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entries per page: <a href=/list/eess.IV/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>New submissions (showing 4 of 4 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2502.12180" title="Abstract" id="2502.12180"> arXiv:2502.12180 </a> [<a href="/pdf/2502.12180" title="Download PDF" id="pdf-2502.12180" aria-labelledby="pdf-2502.12180">pdf</a>, <a href="https://arxiv.org/html/2502.12180v1" title="View HTML" id="html-2502.12180" aria-labelledby="html-2502.12180" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12180" title="Other formats" id="oth-2502.12180" aria-labelledby="oth-2502.12180">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+X">Xinpeng Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhou,+R">Rong Zhou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xie,+H">Han Xie</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tang,+X">Xiaoying Tang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=He,+L">Lifang He</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yang,+C">Carl Yang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) </div> <p class='mathjax'> Multimodal Federated Learning (MFL) has emerged as a promising approach for collaboratively training multimodal models across distributed clients, particularly in healthcare domains. In the context of brain imaging analysis, modality incompleteness presents a significant challenge, where some institutions may lack specific imaging modalities (e.g., PET, MRI, or CT) due to privacy concerns, device limitations, or data availability issues. While existing work typically assumes modality completeness or oversimplifies missing-modality scenarios, we simulate a more realistic setting by considering both client-level and instance-level modality incompleteness in this study. Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness. Specifically, ClusMFL utilizes the FINCH algorithm to construct a pool of cluster centers for the feature embeddings of each modality-label pair, effectively capturing fine-grained data distributions. These cluster centers are then used for feature alignment within each modality through supervised contrastive learning, while also acting as proxies for missing modalities, allowing cross-modal knowledge transfer. Furthermore, ClusMFL employs a modality-aware aggregation strategy, further enhancing the model's performance in scenarios with severe modality incompleteness. We evaluate the proposed framework on the ADNI dataset, utilizing structural MRI and PET scans. Extensive experimental results demonstrate that ClusMFL achieves state-of-the-art performance compared to various baseline methods across varying levels of modality incompleteness, providing a scalable solution for cross-institutional brain imaging analysis. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2502.12181" title="Abstract" id="2502.12181"> arXiv:2502.12181 </a> [<a href="/pdf/2502.12181" title="Download PDF" id="pdf-2502.12181" aria-labelledby="pdf-2502.12181">pdf</a>, <a href="https://arxiv.org/html/2502.12181v1" title="View HTML" id="html-2502.12181" aria-labelledby="html-2502.12181" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12181" title="Other formats" id="oth-2502.12181" aria-labelledby="oth-2502.12181">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> 3D ReX: Causal Explanations in 3D Neuroimaging Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Navaratnarajah,+M">Melane Navaratnarajah</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Martin,+S+A">Sophie A. Martin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kelly,+D+A">David A. Kelly</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Blake,+N">Nathan Blake</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chocker,+H">Hana Chocker</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) </div> <p class='mathjax'> Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first causality-based post-hoc explainability tool for 3D models. 3D ReX uses the theory of actual causality to generate responsibility maps which highlight the regions most crucial to the model's decision. We test 3D ReX on a stroke detection model, providing insight into the spatial distribution of features relevant to stroke. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2502.12512" title="Abstract" id="2502.12512"> arXiv:2502.12512 </a> [<a href="/pdf/2502.12512" title="Download PDF" id="pdf-2502.12512" aria-labelledby="pdf-2502.12512">pdf</a>, <a href="/format/2502.12512" title="Other formats" id="oth-2502.12512" aria-labelledby="oth-2502.12512">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Local Flaw Detection with Adaptive Pyramid Image Fusion Across Spatial Sampling Resolution for SWRs </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=You,+S">Siyu You</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Gou,+H">Huayi Gou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yang,+L">Leilei Yang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+Z">Zhiliang Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zuo,+M">Mingjian Zuo</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to IEEE Sensors Journal for possible publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> The inspection of local flaws (LFs) in Steel Wire Ropes (SWRs) is crucial for ensuring safety and reliability in various industries. Magnetic Flux Leakage (MFL) imaging is commonly used for non-destructive testing, but its effectiveness is often hindered by the combined effects of inspection speed and sampling rate. To address this issue, the impacts of inspection speed and sampling rate on image quality are studied, as variations in these factors can cause stripe noise, axial compression of defect features, and increased interference, complicating accurate detection. We define the relationship between inspection speed and sampling rate as spatial sampling resolution (SSR) and propose an adaptive SSR target-feature-oriented (AS-TFO) method. This method incorporates adaptive adjustment and pyramid image fusion techniques to enhance defect detection under different SSR scenarios. Experimental results show that under high SSR scenarios, the method achieves a precision of 92.54% and recall of 98.41%. It remains robust under low SSR scenarios with a precision of 94.87% and recall of 97.37%. The overall results show that the proposed method outperforms conventional approaches, achieving state-of-the-art performance. This improvement in detection accuracy and robustness is particularly valuable for handling complex inspection conditions, where inspection speed and sampling rate can vary significantly, making detection more robust and reliable in industrial settings. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2502.12735" title="Abstract" id="2502.12735"> arXiv:2502.12735 </a> [<a href="/pdf/2502.12735" title="Download PDF" id="pdf-2502.12735" aria-labelledby="pdf-2502.12735">pdf</a>, <a href="https://arxiv.org/html/2502.12735v1" title="View HTML" id="html-2502.12735" aria-labelledby="html-2502.12735" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12735" title="Other formats" id="oth-2502.12735" aria-labelledby="oth-2502.12735">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Cao,+Z">Zijian Cao</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+H">Hua Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liang,+L">Le Liang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+H">Haotian Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Jin,+S">Shi Jin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+G+Y">Geoffrey Ye Li</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardware, the detection task is sometimes deployed on remote computing devices or the cloud to execute complex algorithms, which brings massive data transmission overhead. In response, this paper proposes an optical flow-driven semantic communication framework for the stereo-vision 3D object detection task. The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy. Specifically, we develop an optical flow-driven module to jointly extract and recover semantics from the left and right images to reduce the loss of the left-right photometric alignment semantic information and improve the accuracy of depth inference. Then, we design a 2D semantic extraction module to identify and extract semantic meaning around the objects to enhance the transmission of semantic information in the key areas. Finally, a fusion network is used to fuse the recovered semantics, and reconstruct the stereo-vision images for 3D detection. Simulation results show that the proposed method improves the detection accuracy by nearly 70% and outperforms the traditional method, especially for the low signal-to-noise ratio regime. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 1 of 1 entries)</h3> <dt> <a name='item5'>[5]</a> <a href ="/abs/2502.12412" title="Abstract" id="2502.12412"> arXiv:2502.12412 </a> (cross-list from cs.LG) [<a href="/pdf/2502.12412" title="Download PDF" id="pdf-2502.12412" aria-labelledby="pdf-2502.12412">pdf</a>, <a href="https://arxiv.org/html/2502.12412v1" title="View HTML" id="html-2502.12412" aria-labelledby="html-2502.12412" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12412" title="Other formats" id="oth-2502.12412" aria-labelledby="oth-2502.12412">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Incomplete Graph Learning: A Comprehensive Survey </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+R">Riting Xia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Huibo Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+A">Anchen Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+X">Xueyan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yan Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chunxu Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+B">Bo Yang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV) </div> <p class='mathjax'> Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related <a href="http://fields.We" rel="external noopener nofollow" class="link-external link-http">this http URL</a> developed an online resource to follow relevant research based on this review, available at <a href="https://github.com/cherry-a11y/Incomplete-graph-learning.git" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 1 of 1 entries)</h3> <dt> <a name='item6'>[6]</a> <a href ="/abs/2408.14453" title="Abstract" id="2408.14453"> arXiv:2408.14453 </a> (replaced) [<a href="/pdf/2408.14453" title="Download PDF" id="pdf-2408.14453" aria-labelledby="pdf-2408.14453">pdf</a>, <a href="/format/2408.14453" title="Other formats" id="oth-2408.14453" aria-labelledby="oth-2408.14453">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reconstructing physiological signals from fMRI across the adult lifespan </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S">Shiyu Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+Z">Ziyuan Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lochard,+L+M">Laurent M. Lochard</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Yamin Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+J">Jiawen Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J+E">Jingyuan E. Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huo,+Y">Yuankai Huo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mather,+M">Mara Mather</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bayrak,+R+G">Roza G. Bayrak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chang,+C">Catie Chang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV); Signal Processing (eess.SP) </div> <p class='mathjax'> Interactions between the brain and body are of fundamental importance for human behavior and health. Functional magnetic resonance imaging (fMRI) captures whole-brain activity noninvasively, and modeling how fMRI signals interact with physiological dynamics of the body can provide new insight into brain function and offer potential biomarkers of disease. However, physiological recordings are not always possible to acquire since they require extra equipment and setup, and even when they are, the recorded physiological signals may contain substantial artifacts. To overcome this limitation, machine learning models have been proposed to directly extract features of respiratory and cardiac activity from resting-state fMRI signals. To date, such work has been carried out only in healthy young adults and in a pediatric population, leaving open questions about the efficacy of these approaches on older adults. Here, we propose a novel framework that leverages Transformer-based architectures for reconstructing two key physiological signals - low-frequency respiratory volume (RV) and heart rate (HR) fluctuations - from fMRI data, and test these models on a dataset of individuals aged 36-89 years old. Our framework outperforms previously proposed approaches (attaining median correlations between predicted and measured signals of r ~ .698 for RV and r ~ .618 for HR), indicating the potential of leveraging attention mechanisms to model fMRI-physiological signal relationships. We also evaluate several model training and fine-tuning strategies, and find that incorporating young-adult data during training improves the performance when predicting physiological signals in the aging cohort. Overall, our approach successfully infers key physiological variables directly from fMRI data from individuals across a wide range of the adult lifespan. </p> </div> </dd> </dl> <div class='paging'>Total of 6 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/eess.IV/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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