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. 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. 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
- Introduction to Deep Learning - 3 Videos
- Deep Learning with MATLAB - 2 Videos
- Computer Vision System Toolbox Overview (2:13) - Video
- Computer Vision Made Easy (35:43) - Webinar
- Computer Vision with MATLAB for Object Detection and Tracking (46:56) - Webinar
- Recognizing numbers in handwritten text - Example
- Barcode Recognition - Example
- Face Detection and Tracking - Example
- Traffic Warning Sign Recognition - Example
Software Reference
- Feature Detection, Extraction, and Matching - Documentation
- Detect Object Using Viola-Jones Algorithm - System Objects
- Object Detection, Motion Estimation, and Tracking - System Objects
- Locating Image Features - Transforms
See also: image reconstruction, image transform, image enhancement, image segmentation, image processing and computer vision, MATLAB and OpenCV, Face recognition, Object detection, Object recognition, Feature Extraction, Stereo Vision, Optical Flow, ransac, pattern recognition, deep learning
