Deep Learning, Semantic Segmentation, and Detection
Computer Vision Toolbox™ supports several approaches for image classification, object detection, and recognition, including:
Deep learning and Convolutional neural networks (CNNs)
Bag of features
Template matching
Blob analysis
Viola-Jones algorithm
Interactive apps for ground truth labeling
A CNN is a popular deep learning architecture that automatically learns useful feature representations directly from image data. Bag of features encodes image features into a compact representation suitable for image classification and image retrieval. Template matching uses a small image, or template, to find matching regions in a larger image. Blob analysis uses segmentation and blob properties to identify objects of interest. The Viola-Jones algorithm uses Haar-like features and a cascade of classifiers to identify objects, including faces, noses, and eyes. You can train this classifier to recognize other objects.
- Image and Video Labeling
Interactive image and video labeling for object detection, semantic segmentation, and image classification
- Semantic Segmentation
Semantic image segmentation
- Object Detection using Deep Learning
Perform classification, object detection, transfer learning using convolutional neural networks (CNNs, or ConvNets)
- Object Detection Using Features
Detect faces and pedestrians, create customized detectors
- Image Category Classification and Image Retrieval
Create a bag of visual words for image classification and content-based image retrieval (CBIR) systems
- Optical Character Recognition (OCR)
Recognize text using optical character recognition
