Image Processing and Computer Vision
With image processing and computer vision products from MathWorks®, you can perform end-to-end processing workflows from data acquisition and preprocessing, to enhancement and analysis, through deployment onto embedded vision systems.
These products enable a large variety of workflows for image, video, point cloud, lidar, and hyperspectral data. Using these products, you can:
Interactively visualize, explore, label, and process data using apps.
Enhance and analyze data algorithmically.
Perform semantic segmentation, object detection, classification, and image-to-image translation using deep learning.
Interface with hardware for image acquisition, algorithm acceleration, desktop prototyping, and embedded vision system deployment.
Products for Image Processing and Computer Vision
Topics
Label and Preprocess Data
- Choose an App to Label Ground Truth Data (Computer Vision Toolbox)
Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, Signal Labeler, or Medical Image Labeler. - Get Started with Image Preprocessing and Augmentation for Deep Learning (Image Processing Toolbox)
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping. - Medical Image Preprocessing (Medical Imaging Toolbox)
Learn common preprocessing steps used in medical image analysis. - Choose an Image Registration Technique (Image Processing Toolbox)
Register images using the Registration Estimator app, or select from techniques including automated feature matching, intensity-based registration, or control point registration.
Detect Objects and Features
- Get Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection using deep learning neural networks such as YOLOX, YOLO v4, and SSD. - Local Feature Detection and Extraction (Computer Vision Toolbox)
Learn the benefits and applications of local feature detection and extraction. - Object Detection in Point Clouds Using Deep Learning (Lidar Toolbox)
Detect 3-D bounding boxes for objects in a point cloud.
Segment Images and Point Clouds
- Get Started with Image Segmentation (Image Processing Toolbox)
Get started with tools for image segmentation, including Segment Anything Model, classical segmentation techniques, and deep learning-based semantic and instance segmentation. - Segment Objects Using Segment Anything Model (SAM) in Image Segmenter (Image Processing Toolbox)
Interactively segment objects or automatically segment the entire image using the Segment Anything Model (SAM) in the Image Segmenter app. (Since R2024b) - Semantic Segmentation in Point Clouds Using Deep Learning (Lidar Toolbox)
Assign class labels to each point inside a point cloud using deep learning.
Enhance Images
- Contrast Enhancement Techniques (Image Processing Toolbox)
Adjust the contrast of grayscale and color images using intensity value mapping, histogram equalization, and contrast-limited adaptive histogram equalization. - Noise Removal (Image Processing Toolbox)
Remove image noise by using techniques such as averaging filtering, median filtering, and adaptive filtering based on local image variance.
Perform Simultaneous Localization and Mapping
- Choose SLAM Workflow Based on Sensor Data (Computer Vision Toolbox)
Choose the right simultaneous localization and mapping (SLAM) workflow and find topics, examples, and supported features.
Acquire and Calibrate Data
- Get Started with Image Acquisition Explorer (Image Acquisition Toolbox)
Use the Image Acquisition Explorer to preview, configure, acquire, and save image data. - Using the Single Camera Calibrator App (Computer Vision Toolbox)
Prepare camera calibration images and estimate camera intrinsic parameters. - What Is Lidar-Camera Calibration? (Lidar Toolbox)
Fuse lidar and camera data.
Deploy on Hardware
- Code Generation for Image Processing (Image Processing Toolbox)
Learn how to generate C code from Image Processing Toolbox™ functions using MATLAB® Coder™. - GPU Code Generation Workflow (GPU Coder)
Design, implement, and verify generated CUDA MEX for acceleration and standalone CUDA code for deployment. - Integrate YOLO v2 Vehicle Detector System on SoC (Vision HDL Toolbox)
Simulate a YOLO v2 vehicle detection algorithm that contains FPGA and ARM sections for deployment to an SoC device.
Featured Examples
Resize an Image
Change the size of an image by specifying a multiplicative scale factor or by specifying the desired output size of the image.
Detect Nuclei in Large Whole Slide Images Using Cellpose
Detect cell nuclei in whole slide images (WSIs) of tissue stained using hematoxylin and eosin (H&E) by using Cellpose.
Detect Image Anomalies Using Explainable FCDD Network
Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.
Detect, Classify, and Track Vehicles Using Lidar
Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle.
Performant and Deployable Monocular Visual SLAM
Use visual inputs from a camera to perform vSLAM and generate multi-threaded C/C++ code.
Segment CT Scan Using MONAI Label
Apply deep learning models from the Medical Open Network for AI (MONAI) Label platform to segment a CT scan.
Videos
What is Computer Vision?
Discover how computer vision extends image processing to enable a wide
variety of application areas such as object detection, tracking, and
recognition.
What is Lidar Toolbox?
Discover capabilities of Lidar Toolbox™ to work with point cloud data for perception and navigation
applications.
What is Medical Imaging?
Discover capabilities of Medical Imaging Toolbox™, including visualization, registration, segmentation, and
labeling.
