Object Recognition

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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, learning, or pattern recognition 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 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

For more information, see MATLAB®, Image Processing Toolbox, Computer Vision System Toolbox, Statistics Toolbox, and Neural Network Toolbox.

Examples and How To

Software Reference

See also: Steve on Image Processing, image recognition, image and video processing, object detection, face recogniton, MATLAB and OpenCV, Feature Extraction, Stereo Vision, Optical Flow, ransac, pattern recognition