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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)

  • 包的功能

  • 模板匹配

  • 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.

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Featured Examples

Object Detection Using SSD Deep Learning

Object Detection Using SSD Deep Learning

Train an SSD Multibox object detector using a deep learning network architecture.
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对象检测使用YOLO v3迪意思p Learning

对象检测使用YOLO v3迪意思p Learning

Detect objects using you look only once version 3 network.
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Object Detection Using YOLO v2 Deep Learning

Object Detection Using YOLO v2 Deep Learning

使用“唯一看起来”（YOLO）V2深度学习技术训练对象探测器。
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Train Classification Network to Classify Object in 3-D Point Cloud

Train Classification Network to Classify Object in 3-D Point Cloud

Train a classification network to classify objects in a 3-D point cloud.
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Import Pretrained ONNX YOLO v2 Object Detector

Import Pretrained ONNX YOLO v2 Object Detector

从ONNX深度学习框架导入预用的yolo v2对象探测器。
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利用深度学习的语义分割

利用深度学习的语义分割

Train a semantic segmentation network using deep learning.
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使用深度学习估算身体姿势

使用深度学习估算身体姿势

Estimate the body pose of one or more people using the OpenPose algorithm.
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Activity Recognition from Video and Optical Flow Data Using Deep Learning

Activity Recognition from Video and Optical Flow Data Using Deep Learning

Train an inflated-3D (I3D) two-stream convolutional neural network for activity recognition using RGB and optical flow data from videos.
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Computer Vision Toolbox Documentation

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Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

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