Abstract. In this paper, we propose the novel target tracking algorithm based on the visual saliency model. Dynamic object tracking problem of the scene is the current hot issues in the field of machine vision research. The main task of it from the dynamic target detection, recognition and tracking in image sequence, or even able to understand and describe the target object behavior that can be widely used in visual surveillance, human-computer interaction, robot soccer, military industry, etc. In view of the tracking object and the different application scenarios, multiple targets tracking problem has always been a difficulty in research of the target tracking. Tracking methods are divided into point tracking, nuclear track, contour tracking, and mixed scenarios. Our paper combines the concepts of the visual saliency to propose the enhance feature extraction methodologies that help to assist us get better tracking accuracy that is innovative.
Abstract. In this paper, we conduct research on the image semantic understanding pattern based on the sparse coding and wavelet theory. From the perspective of image understanding, according to the different weights of spatial information to give false points pixels, make the false points of pixels in different positions have different importance. Was introduced to the weighted formula of relative distance, foreground and background search distance, in order to obtain evaluation algorithm for image segmentation is scaling invariance conclusion in order to overcome large ratio of segmentation quality evaluation distortion phenomenon, put forward a kind of distortion of punishment, improve the effectiveness of the evaluation algorithm and global. Through applying the algorithm into primary application scenarios, we could test the effectiveness and robustness.
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