Computer Vision Toolbox



Computer Vision Toolbox

Design and test computer vision systems

Image and Video Ground Truth Labeling

Automate labeling for object detection, semantic segmentation, instance segmentation, and scene classification using the Video Labeler and Image Labeler apps.

Deep Learning and Machine Learning

Train machine learning models and deep learning networks—or use pretrained networks—for object detection and segmentation. Evaluate the performance of these networks and deploy them by generating C/C++ or CUDA® code.

Automated Visual Inspection

Use the Automated Visual Inspection Library to automatically identify anomalies or defects as part of a manufacturing quality assurance process.

Camera Calibration

Estimate the intrinsic, extrinsic, and lens-distortion parameters of monocular and stereo cameras using the Camera Calibrator and Stereo Camera Calibrator apps.

Visual SLAM and 3D Vision

Extract the 3D structure of a scene from multiple 2D views. Estimate camera position and orientation with respect to its surroundings. Refine pose estimates using bundle adjustment and pose graph optimization.

Lidar and 3D Point Cloud Processing

Segment, cluster, downsample, denoise, register, and fit geometrical shapes with lidar or 3D point cloud data. Lidar Toolbox provides additional functionality to design, analyze, and test lidar processing systems.

Feature Detection, Extraction, and Matching

Detect, extract, and match features such as blobs, edges, and corners, across multiple images. Use the matched features for registration, object classification, or in complex workflows such as SLAM.

Multi-Object Tracking and Motion Estimation

Estimate motion and track multiple objects in video and image sequences.

Code Generation and Third-Party Support

Generate code from your computer vision algorithms for rapid prototyping, deployment, and verification. Integrate OpenCV-based projects and functions into MATLAB and Simulink.

“We can access machine learning capabilities with a few lines of MATLAB code. Then, using code generation, engineers can deploy their trained classifier into the machine without manual intervention or delays in the process.”

Larry Mianzo, Caterpillar

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