Publisher's Synopsis
This book develops algorithms, functions, and apps for designing and simulating computer vision and video processing systems. Algorithms are available as MATLAB functions, System objects, and Simulink blocks. You can perform feature detection, extraction, and matching, as well as object detection and tracking. Local features and their descriptors are the building blocks of many computer vision algorithms. Their applications include image registration, object detection and classification, tracking, and motion estimation. These algorithms use local features to better handle scale changes, rotation, and occlusion. Computer Vision System Toolbox algorithms include the FAST, Harris, and Shi & Tomasi corner detectors, and the SURF, KAZE, and MSER blob detectors. The toolbox includes the SURF, FREAK, BRISK, LBP, and HOG descriptors. You can mix and match the detectors and the descriptors depending on the requirements of your application. You can also extract features using a pretrained convolutional neural network which applies techniques from the field of deep learning. Computer Vision System Toolbox supports several approaches for image classification, object detection, and recognition, including: -Deep learning and Convolutional neural networks (CNNs) -Bag of features -Template matching -Blob analysis -Viola-Jones algorithm -Interactive app for ground truth labeling
