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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, 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 Segmentation
Semantic image segmentation
Object Detection
Perform classification, object detection, transfer learning using convolutional neural networks (CNNs, or ConvNets), create customized detectors
Keypoint Detection
Detect keypoints in objects using convolutional neural networks (CNNs)
Text Detection and Recognition
Detect and recognize text using image feature detection and description, deep learning, and OCR
Image Category Classification
Create vision transformer or bag of visual words image classifier
Video Classification
Perform video classification and activity recognition using deep learning
Automated Visual Inspection
Automate quality control tasks using anomaly detection and localization methods

Featured Examples

Multiclass Object Detection Using YOLO v2 Deep Learning

Multiclass Object Detection Using YOLO v2 Deep Learning

Train a multiclass object detector using a YOLO v2 deep learning technique.

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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

Import pretrained YOLO v2 object detector from ONNX deep learning framework.

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Semantic Segmentation Using Deep Learning

Semantic Segmentation Using Deep Learning

Segment an image using a semantic segmentation network.

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Estimate Body Pose Using Deep Learning

Estimate Body Pose Using Deep Learning

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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Train Object Detectors in Experiment Manager

Train Object Detectors in Experiment Manager

Use the Experiment Manager app to find optimal training options for object detectors.

Perform Instance Segmentation Using Mask R-CNN

Perform Instance Segmentation Using Mask R-CNN

Segment individual instances of people and cars using a multiclass mask region-based convolutional neural network (R-CNN).

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