Learning-based Edge Detection for Video Sequences
The advancing fields in video technology like that of 3D TV and Free Viewpoint Video essentially requires more information to transmit in addition with the 2D color maps. This information is the depth maps of the accompanying scenes from the videos sequences in order to facilitate rendering of arbitrary viewpoints within it. Soft computing approaches such as consisting of fuzzy logic, neural or evolutionary computation are the emerging fields with the wide applications in edge detection and image segmentation. Such a process of partitioning the pixels regions of the digital images with boundary between two homogenous regions refers to the detection of edges. There were various approaches that have been implemented to achieve the same but their performance and its scope of operation varies widely. In this study, we present a novel algorithm to determine the contours from the given video sequences and form a network of it in the run-time and thereby create a new-data format accompanied by the color scheme with the contour network exhibiting the depth maps and the dynamically adjusting with changes it experienced while the object remain in motion. Preserving the depth maps and portraying its continuity is crucial for the synthesis of high quality view. This study will facilitate the division of features derivable from RGB color visions to extract precise and hidden visual information of superior visual quality with low geometric distortions.
Keywords: video sequences, edge detection, image segmentation, depth map, contours
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