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We use two methods for visual-tracking, one based on optical flow and one based on color information.
For example, in some cases the kernel based on color information is more informative than a texture based kernel.
One of the efficient methods in the field of object tracking based on color information is mean-shift algorithm.
Both filters work based on color information and give more robust results compared to the scalar approaches presented in[19, 38].
The algorithm in[16] is based on color information under the assumption that the surface shading and transmission functions are locally uncorrelated.
The methods based on color information are compatible with all local geometric properties expressed above: I(t + 1) = I t) + τ1∂I t)/∂t, where τ1 is an adapting time step.
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In this paper a multi-classifier system based on color and texture information is proposed for face image segmentation.
For the automatic method, we segment the image based on color and region information and detect the lip's contour and the position of the points P from the segmentation.
Importantly, the visual signatures of images are based on color, shape and texture information and are estimated by using the method proposed in [12].
The segmentation of the video frames is performed by the fuzzy region growing segmentation followed by matching of the segments in consecutive frames based on color, texture, and geometry information.
According to the analysis above, we define the stereo contrast measure S i, j) between a pair of superpixels i and j based on color, disparity, and spatial information: Sleft i,jright)=left(frac{Cleft i,jright)}{1+ctimes Lleft i,jright)}right times {omega}_j (2).
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