• documentation
main content

recognition, object detection, and semantic segmentation -凯发k8网页登录

recognition, classification, semantic image segmentation, object detection using features, and deep learning object detection using cnns, yolo, 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.

highlighted topics

categories


  • semantic image segmentation

  • perform classification, object detection, transfer learning using convolutional neural networks (cnns, or convnets), create customized detectors

  • detect and recognize text using image feature detection and description, deep learning, and ocr

  • create bag of visual words for image classification and content-based image retrieval (cbir) systems

  • perform video classification and activity recognition using deep learning
  • automated visual inspection
    automate quality assurance tasks using anomaly detection and classification techniques
网站地图