Object Recognition

Object recognition methods in computer vision

Object recognition is a process for identifying a specific object in a digital image or video. Object recognition algorithms rely on matching or learning algorithms using appearance-based or feature-based techniques. Common techniques include edges, gradients, Histogram of Oriented Gradients (HOG), Haar wavelets, and linear binary patterns. Object recognition is useful in applications such as video stabilization, automated vehicle parking systems, and cell counting in bioimaging.

You can recognize objects in MATLAB^® with Image Processing Toolbox^™, Computer Vision System Toolbox^™, Statistics Toolbox^™, and Neural Network Toolbox^™ using a variety of models, including:

• Extracted features and boosted learning algorithms
• Bag of words models with features such as SURF and MSER
• Gradient-based and derivative-based matching approaches
• Viola-Jones algorithm
• Template matching
• Image segmentation and blob analysis

Examples and How To

• Detect and Track Moving Objects Using Gaussian Mixture Models (Example)
• Track an Object Using Correlation (Example)
• Abandoned Object Detection (Example)
• Face Detection and Tracking (Example)
• Pattern Matching (Example)
• Face Detection with MATLAB (Blog)
• Traffic Warning Sign Recognition (Example)

Software Reference

• Feature Detection, Extraction, and Matching (Documentation)
• Object Detector (System Object)
• Computer Vision System Toolbox (Documentation)
• Object Detection, Motion Estimation, and Tracking (Product Information)

See also: Steve on Image Processing, image recognition, image and video processing, object detection, face recogniton, MATLAB and OpenCV