Computer Vision System Toolbox
Object detection and recognition are used to locate, identify, and categorize objects in images and video. Computer Vision System Toolbox provides a comprehensive suite of algorithms and tools for object detection and recognition.
You can detect or recognize an object in an image by training an object classifier using pattern recognition algorithms that create classifiers based on training data from different object classes. The classifier accepts image data and assigns the appropriate object or class label.
Face detection using Viola-Jones algorithm
Using a cascade of classifiers to detect faces
Detecting people using pretrained support vector machine(SVM) with histogram of oriented gradient (HOG) features
Text detection and optical character recognition (OCR)
Recognizing text in natural images
Classifying digits using support vector machines (SVM) and HOG feature extraction
Motion-based object detection algorithms use motion extraction and segmentation techniques such as optical flow and Gaussian mixture model (GMM) foreground detection to locate moving objects in a scene. Blob analysis is used to identify objects of interest by computing the blob properties from the output of a segmentation or motion extraction algorithm such as background subtraction.
Feature points are used for object detection by detecting a set of features in a reference image, extracting feature descriptors, and matching features between the reference image and an input. This method of object detection can detect reference objects despite scale and orientation changes and is robust to partial occlusions.
Training is the process of creating an object detector or classifier to detect or recognize a specific object of interest. The training process utilizes:
The system toolbox provides an app to select and assign regions of interest (ROI) and label training images.