Search results for: Saipriya R

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Each species of a wood has its own unique patterns in its bark, which enabled the proposed system to identify it accurately. Automatic wood recognition system has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In our work, a wood recognition system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition, rock classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. The result thus obtained shows a high rate of recognition accuracy proving that the techniques used in suitable to be implemented for commercial purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Correlation" title="Correlation">Correlation</a>, <a href="https://publications.waset.org/search?q=Grey%20Level%20Co-Occurrence%20Matrix" title=" Grey Level Co-Occurrence Matrix"> Grey Level Co-Occurrence Matrix</a>, <a href="https://publications.waset.org/search?q=ProbabilityDensity%20Function" title=" ProbabilityDensity Function"> ProbabilityDensity Function</a>, <a href="https://publications.waset.org/search?q=Wood%20Recognition." title=" Wood Recognition."> Wood Recognition.</a> </p> <a href="https://publications.waset.org/2558/wood-species-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2558/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a 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Search results for: Saipriya R