Computer Vision Toolbox


Computer Vision Toolbox

Design and test computer vision, 3D vision, and video processing systems

Capabilities, Documentation, and Examples

Computer Vision is a set of techniques for extracting and interpreting information from images, videos, or point clouds. This includes classical and deep learning methods for image recognition, object detection and tracking, feature detection and extraction, segmentation, 3D pose estimation, simultaneous localization and mapping, and camera calibration. Applications include robotics, automated driving, industrial visual inspection, and 3D scene reconstruction.

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 or use pretrained deep learning and machine learning based object detection and segmentation networks. Evaluate the performance of these networks and deploy them using C/C++ or CUDA® code.

Automated Visual Inspection

Use the Automated Visual Inspection Library in Computer Vision Toolbox to identify anomalies or defects to assist and improve quality assurance processes in manufacturing.

Camera Calibration

Estimate the intrinsic, extrinsic, and lens-distortion parameters of monocular and stereo cameras using the camera calibration and stereo camera calibration 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. Features matched across images can be used for registration, object classification, or in complex workflows such as SLAM.

Object Tracking and Motion Estimation

Estimate motion and track objects in video and image sequences.

Code Generation and Third Party Support

Use the toolbox for rapid prototyping, deploying, and verifying computer vision algorithms. Integrate OpenCV-based projects and functions into MATLAB and Simulink.

“From data annotation to choosing, training, testing, and fine-tuning our deep learning model, MATLAB had all the tools we needed—and GPU Coder enabled us to rapidly deploy to our NVIDIA GPUs even though we had limited GPU experience.”

Valerio Imbriolo, Drass Group

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