Titlebook: Computational Texture and Patterns; From Textons to Deep Kristin J. Dana Book 2018 Springer Nature Switzerland AG 2018

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Texture Synthesis,side to create a larger region. Using identical texture tiles is known to perform poorly because of noticeable seams at the tile boundaries. Algorithms for texture synthesis can be used to extend texture in a seamless way as demonstrated in Figure 5.1.

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Tools for Mining Patterns: Cloud Services and Software Libraries, street view provides visual imagery at multiple scales from satellite to street level. To specifically support automated driving, recent datasets such as BDD-Nexar [146], KITTI [72], Cityscapes [41], and Oxford RobotCar Dataset [145] have detailed images and/or maps of local roads.

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Visual Patterns and Texture,ists [1–3, 13, 25, 26, 81, 194]. Our intrigue with visual patterns has an analogy to the perception of a musical chord; the spatial arrangement of shades and colors gives an appealing perceptual impression. Digital imagery is now abundant in science, medicine, industry, business, and everyday life.

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Texture Segmentation,tput of the segmentation encodes each distinct region with a unique label that is typically illustrated with an image of the segmentation map where each region is colored according to its label. When segmentation is combined with recognition, each labeled region also has a . indicating what object c

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Texture Synthesis, it can create new instances of a texture class. Texture has an element of randomness, and two instances are not spatially identical. The probabilistic distribution of features along with a framework for spatial organization defines the texture class so that a particular image of a texture is an ins

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978-3-031-00695-1Springer Nature Switzerland AG 2018

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Textons in Human and Computer Vision,In this chapter we give a concise tour of the path of scientific research that has grown from classical textons to modern deep learning. We will see that the current deep learning architectures have echos of past research incorporating orientation, scale space, and hierarchical models of processing.