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. Segmentation is essential for image analysis tasks. Semantic segmentation describes theprocess of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car). Applications for semantic segmentation include: Autonomous driving, Industrial inspection, classification of terrain visible in satellite imagery and Medical imaging analysis. You can use the Image Labeler app to interactively label pixels and export the label data for training. The app can also be used to label rectangular regions of interest (ROIs) and scene labels for image classification. Image feature detection is a building block of many computer vision tasks, such as image registration, tracking, and object detection. The Computer Vision System Toolbox includes a variety of functions for image feature detection. These functions return points objects that store information specific to particular types of features, including (x, y) coordinates (in the Location property). You can pass a points object from a detection function to a variety of other functions that require feature points as inputs. The algorithm that a detection function uses determines the type of points object it returns. The optical character recognition (OCR) app trains the ocr function to recognize acustom language or font. You can use this app to label character data interactively for OCR training and to generate an OCR language data file for use with the ocr function. Motion estimation and tracking are key activities in many computer vision applications, including activity recognition, traffic monitoring, automotive safety, and surveillance. Tracking is the process of locating a moving object or multiple objects over time in a video stream. Tracking an object is not the same as object detection. Object detection is the process of locating an object of interest in a single frame. Tracking associates detectionsof an object across multiple frames. Tracking multiple objects requires detection, prediction, and data association. Detection detect objects of interest in a video frame, Prediction predict the object locations in the next frame and Data association use the predicted locations to associate detections across framesto form tracks. For rapid prototyping and embedded system design, the system toolbox supports fixed-point arithmetic and C-code generation.