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We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene. Particularly in indoor videos such as captured by robotic platforms or handheld and bodyworn RGBD cameras, nearby video frames provide diverse viewpoints...
Active Shape Model (ASM) is considered as a high level image processing algorithm. Typical applications include image segmentation and interpretation. A major challenge in ASMs is to repeatedly move model points towards true boundaries. It is a crucial step in the algorithm which fails in cases of low contrast images. In this paper, we present a new search algorithm for ASM to tackle segmentation...
In this paper, we propose a novel method for pose estimation and body segmentation. We estimate the partial configuration of adjacent parts instead of detecting each single part, which makes our method more robust and accurate. Further, we develop a general model to calculate the partial configuration. Besides, we present a tree-based hierarchical probabilistic method to derive the global optimal...
In this paper, we propose a multi-level resolution semantic modeling for automatic scene recognition. The basic idea of the semantic modeling is to classify local image regions into semantic concept classes such as water, sunset, or sky, and use occurrence frequency of local region's semantic concepts for global image representation. However, how to decide size of the local image regions is a trial...
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