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Introduction to Computer Vision and Basic Concepts of Image Formation
Published in Manas Kamal Bhuyan, Computer Vision and Image Processing, 2019
As illustrated in Figure 1.41, texture can be used as a monocular cue to recover the three-dimensional information from two-dimensional images [47]. Texture is a repeated pattern on a surface. The basic texture elements are called “textons,” which are either identical or follow from some statistical distributions. Shape from texture comes from looking at deformation of individual textons or from distribution of textons on a surface. For this, the region having uniform texture has to be segmented from the image, and subsequently the surface normals are estimated. During segmentation, the intensity values are assumed to vary smoothly (isotropic texture). Hence, the actual shape information cannot be estimated.
Methods of Digital Analysis and Interpretation
Published in Victor Raizer, Optical Remote Sensing of Ocean Hydrodynamics, 2019
Texture synthesis is a common computer graphics technique to create large textures from small texture samples. Texture synthesis is used for texture mapping in surface or scene rendering applications. A synthetic texture should differ from input texture samples, but should have perceptually identical texture characteristics. In computer vision, texture synthesis increases texture coherence providing superior image quality and performance. Compared to texture classification and segmentation, texture synthesis poses a challenge on texture analysis because it requires a more detailed texture description. Applications of texture synthesis include image editing, image completion, video synthesis, and computer animations (Magnenat-Thalmann and Thalmann 1987). Algorithms of texture synthesis use the following methods: (1) Pixel-based sampling, (2) Block sampling, (3) Multiresolution sampling, (4) Mosaic-based synthesis, (5) Markov-Gibbs random field, (6) Pyramided-based synthesis, (7) Cut-Primed Smart Copying. Most of synthesis approaches are based on Markov-Gibbs random field models of textures (Li 2009). A few recent algorithms designed for texture synthesis on 3D surfaces.
Image Classification and Retrieval
Published in R. Suganya, S. Rajaram, A. Sheik Abdullah, Big Data in Medical Image Processing, 2018
R. Suganya, S. Rajaram, A. Sheik Abdullah
Some of the image features include color, texture, shape, etc. Most medical images are grayscale and so color features are not commonly used in medical image retrieval. Texture refers to the spatial arrangement of the pixels or intensities in an image. In the medical field, texture features serve as an important tool for diagnosis because they reflect details within an image structure. Shape features include visual characteristics such as curves, surfaces, contour and so on. In medical images only a few pixels bear pathological information. Hence it is mandatory to extract the appropriate features that would assist the diagnosis of pathological regions in the image. This would guide the medical expert to carry out the diagnosis with minimal effort.
Image registration for varicose ulcer classification using KNN classifier
Published in International Journal of Computers and Applications, 2018
R. R. Bhavani, G. Wiselin Jiji
Texture is about the spatial arrangement of color or intensities in an image or selected region of an image. Image textures can be assumed in natural scenes captured in an image. Image texture is used to help in segmentation of images [32].Contrast is to find the intensity between a pixel and its neighbor over the whole image. It is also used for enhancing the image quality. Contrast is the variation in color that makes an object distinct. Visually, contrast is identified by the difference in the color and brightness of the object and other objects within the same field of view.
Automatic texture mapping mega-projects
Published in Journal of Spatial Science, 2020
Texture mapping aims to map the actual texture that appears in the images onto each triangulated face of the geometric 3D model of the object. Textures within each triangulated face are then stretched using projective transformation and warped. Afterward, the textures and the 3D model can be visualized in virtual reality. Accordingly, a rendered photorealistic 3D model has the same view of a photograph taken from the same point of view. The procedure of texture mapping includes three main steps, which are; (1) projecting model triangulated faces on all images; (2) detecting occluded triangulated faces in each image; (3) deciding the most suitable image (texture) for each triangulated face.