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Image super-resolution typically requires prior knowledge to overcome its under-determinedness. While recent generic prior models give impressive results, we note some common artifacts: blurry edges, jagged edges, staircase surfaces, and inconsistent colors. With understanding the underlying causes, we propose an improved generic prior model using isotropic Huber MRFs. In addition, to avoid the oversimplifying...
Shadow detection is crucial for robust and reliable visual surveillance system. The shadow detection method based on gray level and color information will fail when object parts have very similar properties with real shadow. In this paper, we present a novel shadow detection scheme which can conveniently verify detected shadows utilizing the extracted SIFT (Scale Invariant Feature Transform) features...
We propose a method of extracting scallop areas from sand seabed images to assess fish resources, and developed an automatic system that measures their quantities, sizes, and states for fisheries management. In the gravel field, we can see scallops feature, such as color, fluted patterns and shaped like fans. But, it is difficult for the scallop is covered with sand in the sand field. The present...
A novel vehicle localization algorithm based on phase information and contour projection is proposed. Firstly, HSV color model and RGB color model are combined, and contours of the vehicles in image are detected based on phase congruency. Secondly, contour projection method is utilized to get candidate vehicle regions. Finally vehicle region distinguishing algorithm are applied to the candidates,...
The super-resolution (SR) imaging is a process of constructing high resolution image using several shifted low resolution images of the same scene. The process should ideally be fast, and should add sharpness and detail, both at edges and in regions without adding artifacts. SR imaging works in two major phases: registration and interpolation. Existing registration techniques are computationally time...
Suppose we are given a set of generators for a group G of permutations of a colored set A. The color automorphism problem for G involves finding generators for the subgroup of G which stabilizes the color classes. Testing isomorphism of graphs of valence ≤ t is polynomial-time reducible to the color automorphism problem for groups with small simple sections. The algorithm for the latter problem involves...
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