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Guo et al. [5] summarized four primary problems about texture analysis which are respectively image classification based on texture content, image segmentation of homogeneous texture regions, texture synthesis for graphics applications, and shape information acquisition from texture cue.
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The spatial segmentation methods use the edges, region, texture, color, corners, and contours.
This distance is suitable for determining paths in uniform regions whose texture is not characterized by strong gradients.
Changes in the appearance of crowded targets bear a strong resemblance to the filling-in that occurs when regions of texture perceptually complete across either homogeneous target regions or the blind spot [ 42, 43].
An error of a measurement depends on template subset size, search region size texture quality.
For target representation and localization algorithms, tracking a moving object typically involves matching objects in consecutive frames using features such as edge, region, shape, texture, position, and color.
The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties.
Attempt was made to extract wound region using texture analysis [ 6, 9].
Then we further make the local deformation to the deformed textures interactively by replacing the local-texture elements regions from other textures.
Regions with strong texture and quickly changing texture are more likely to represent objects from the environment rather than elephant skin.
As shown in the results reconstructed by the proposed method in Figures17c and18c, it can be seen that texture regions and simple structure regions can be reconstructed successfully.
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