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Recognition, Object Detection, and Semantic Segmentation

Recognition, classification, semantic image segmentation, object detection using features, and deep learning object detection using CNNs, YOLO v2, and SSD

Computer Vision Toolbox™ supports several approaches for image classification, object detection, semantic segmentation, and recognition, including:

  • Deep learning and convolutional neural networks (CNNs)

  • Bag of features

  • Template matching

  • Blob analysis

  • Viola-Jones algorithm

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.

Featured Examples