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In this paper we propose a new salient object detection method via structured label prediction. By learning appearance features in rectangular regions, our structural region representation encodes the local saliency distribution with a matrix of binary labels. We show that the linear combination of structured labels can well model the saliency distribution in local regions. Representing region saliency...
The depth image has greatly broadened various applications of computer vision, however, it is seldom explored in the field of salient object detection. In this paper, we propose a learning-based approach for extracting saliency from RGB-D images. For best fitting the contrast-based stimulus that guides the saliency search in human vision system, massive visual attributes that are extracted from several...
We present a new segment-based method for saliency detection based on multi-size superpixels that combines local and global saliency cues. We extract superpixels at several scales and represent each superpixel with a normal distribution in CIE-Lab space estimated from its associated pixels. Global saliency is computed by grouping similar superpixels to estimate the spatial distribution of colors,...
Medical image registration is a critical step in medical image processing. In this paper, a mixed-type image registration approach is presented, which combines the segmentation-based and voxel-based registration. Firstly, the experimental images are preprocessed, including digital imaging and communication of medicine (DICOM) format conversion, denoising, and segmentation. Then mutual information...
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