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Deep Learning, Object Detection and Recognition

Deep learning, object detection, recognition, block matching, background estimation, bag of features

Computer Vision System 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

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.

Object Detection and Recognition

Local Feature Detection and Extraction

Featured Examples

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