Object recognition methods in computer vision
What Is Object Recognition?
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.
Object recognition is useful in applications such as video stabilization, advanced driver assistance systems (ADAS), and disease identification in bioimaging. Common techniques include deep learning based approaches such as convolutional neural networks, and feature-based approaches using edges, gradients, histogram of oriented gradients (HOG), Haar wavelets, and linear binary patterns.
Object Recognition Techniques
You can recognize objects using a variety of models, including:
- Feature extraction and machine learning models
- Deep learning models such as CNNs
- Bag-of-words models with features such as SURF and MSER
- Gradient-based and derivative-based matching approaches
- The Viola-Jones algorithm, which can be used to recognize a variety of objects, including faces and upper bodies
- Template matching
- Image segmentation and blob analysis
Object Recognition: Deep Learning and Machine Learning with MATLAB
You can also download demo code used in the presentation.
For more information, see MATLAB®, Image Processing Toolbox™, Computer Vision Toolbox™, Statistics and Machine Learning Toolbox™, and Deep Learning Toolbox™.
Examples and How To
- Deep Learning with MATLAB - Video Series
- Image Recognition Using Machine Learning (7:49) - Video
- What Is Computer Vision Toolbox? (1:42) - Video
- Detect and Track Moving Objects Using Gaussian Mixture Models - Example
- Face Recognition with MATLAB (7:40) - Video
- Traffic Sign Detection - Example
- Deep Learning Toolbox - Product Features
- Feature Detection and Extraction - Documentation
- Cascade Object Detector - Documentation
- Computer Vision Toolbox - Documentation
- Object Tracking and Motion Estimation with Computer Vision Toolbox - Product Features
See also: Deep Learning, Steve on Image Processing, image recognition, image processing and computer vision, object detection, MATLAB and OpenCV, feature extraction, stereo vision, optical flow, RANSAC, pattern recognition, point cloud, deep learning