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Overview: This book recognizes digital images and video use in fields of surveillance, manufacturing, and agriculture. Organized in two parts, a variety of readers learn about communication, automation, and beginning a career in research and innovation. The goal of the book is to provide research that addresses broad challenges in both theoretical and application aspects of soft computing and Machine Learning in image processing and computer vision. A computer’s vision to comprehend and analyze visual input is achieved via the application of algorithms, methods, and models. Image capture, preprocessing, feature extraction, and classification are some of the procedures that are taken to accomplish this. Using cameras, sensors, or other imaging equipment, image acquisition includes acquiring visual data. The captured pictures are subsequently cleaned up and improved using preprocessing techniques, such as reducing noise, fixing distortions, and altering the color balance. Feature extraction includes detecting essential properties or aspects of the photos, such as edges, corners, textures, or form. CNNs, which are intended to learn from and extract characteristics from enormous volumes of visual input, are frequently used for this purpose. By classifying photos based on their attributes, Machine Learning techniques are used. Support vector machines (SVMs), decision trees, or Deep Learning algorithms like CNNs can all be used for this. In general, the topic of computer vision is complicated, involving a variety of methods and models, and it is ever-evolving as new studies are undertaken and new applications are created.
Genre: Non-Fiction > Tech & Devices
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