Image Recognition

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Recognition methods in image processing

Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms include:

  • Optical character recognition
  • Pattern and gradient matching
  • Face recognition
  • License plate matching
  • Scene change detection

Specific image recognition applications include classifying digits using HOG features and an SVM classifier (Figure 1).

Figure 1. Digit classification using Histogram of Oriented Gradients (HOG) feature extraction of image (top) and SVMs

Figure 1. Digit classification using Histogram of Oriented Gradients (HOG) feature extraction of image (top) and SVMs. See this example for source code and explanation.

Cross correlation can be used for pattern matching and target tracking as shown in Figure 2.

Figure 2. Using normalized cross-correlation to recognize specific chips on a circuit board

Figure 2. Using normalized cross-correlation to recognize specific chips on a circuit board. See example for details.

An effective approach for image recognition includes using a technical computing environment for data analysis, visualization, and algorithm development.

For more information, see Computer Vision System Toolbox.

Examples and How To

Software Reference

See also: image reconstruction, image transform, image enhancement, image segmentation, image and video processing, MATLAB and OpenCV, Face recognition, Object detection, Object recognition, Feature Extraction, Stereo Vision, Optical Flow, ransac, pattern recognition