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Articles by Parvathy Rajendran
Total Records ( 2 ) for Parvathy Rajendran
  Chor Keat Ong and Parvathy Rajendran
  Visual tracking has become one of the most important components in computer vision as the knowledge in this field can be applied into a wide range of applications in computer vision such as medical imaging, pattern recognition, video surveillance industrial robot, computer-human interaction, etc. A lot of researches have been conducted and many types of state-of-the-art methods and modifications such as sparse representation, online similarity learning, self-expressive, spatial kernel phase correlation filter and others are proposed in order to increase the robustness of the tracking. Despite of many methods has been demonstrated successfully but there are several issues that still need to be addressed. There still have some unsolvable difficulties in which they become a challenging task to track an object effectively and robustly and it will tend to decrease the accuracy of the results and hence. Until now, there are still no perfect algorithm to track the target flawlessly. In order to improve the performance, the main idea proposed is implementing optimization technique on the selected parameters and obtain a better performance. In this research, the tracking is proposed by using the Overlap Ratio (OR) and Centre Location Error (CLE). In our case, our target is to obtain a better accuracy which is higher OR and lower CLE than the result from the algorithms available in public. A simple optimization is used in here where the global best results with respect to the value of the parameters are selected through a range of values defined in our research. Through the optimization, the overall OR is increased to 0.554 and overall CLE is decreased to 19.803 pixels. Thus, the proposed method had increased the accuracy and robustness of the visual tracking on many of the video sequences.
  Ooi Qun Wong and Parvathy Rajendran
  Image segmentation using active contour models to improve image processing enhances object detection. Various segmentation methods have been proposed over in the past to improve the accuracy of segmentation results such as clustering, edge-based, region-based, template matching and hybrid methods. However, the image segmentation results of these methods are not ideal. Therefore, a small improvement in the results will have a huge impact on image processing, particularly for autonomous unmanned aircraft application. Recently, the Chan-Vese Model, a region-based method that uses active contour models, gained considerable research attention because of its improved image segmentation capability. This study presents a model that enhances the Chan-Vese algorithm model. The main idea of the proposed method is to reduce the computational time in image segmentation without affecting the segmentation result. Fitting term is defined as constant in the proposed model and the level set equation of the main domain continues to evolve the curve toward the boundary of the object. A total of 467 images from the Berkeley segmentation database are used to test the proposed method and analyze its performance. Results indicate that the proposed model achieves better segmentation result with low computational time compared with existing image segmentation methods
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