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Overview: The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, Artificial Intelligence (AI) has been revolutionized by Machine Learning and Big Data approaches, outperforming human prediction on a wide range of problems. In particular, Deep Learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. Computer vision concerns itself with computationally achieving a high-level perception and understanding (approximating the performance of human cognition) of digital images and videos. It attempts the automation of tasks naturally accomplished by the human visual apparatus. Computer vision encompasses scientific methods for the acquisition, processing, analysis, and interpretation of digital images and extraction of multidimensional data from the physical world with a view to produce numerical or symbolic information. For any computer vision applications, the input data is an image or a video, and the objective is to understand the image and its contents. If the goal is to create an application of human vision—like object recognition, defect detection, etc.—then it may be called computer vision. Texture analysis is a technique for visualizing regions in an image by analyzing the texture information of those regions.
Genre: Non-Fiction > Tech & Devices
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