Image Processing and Computer Vision

Accelerate image processing, computer vision, and medical imaging applications with parallel computing

Use parallel computing to accelerate image processing, computer vision, and medical imaging applications by using Parallel Computing Toolbox™ together with Image Processing Toolbox™, Computer Vision Toolbox™, and Medical Imaging Toolbox™.

Apps

Image Batch Processor

Apply function to multiple images

Topics

Image Processing

Parallel Block Processing on Large Image Files (Image Processing Toolbox)
If you have a Parallel Computing Toolbox license, you can take advantage of multiple processor cores on your machine to improve the performance of blockproc.
Image Processing on a GPU (Image Processing Toolbox)
Take advantage of graphics processing unit (GPU) acceleration for complicated image processing workflows.
Process Large Set of Images Using MapReduce Framework and Hadoop (Image Processing Toolbox)
This example shows how to execute a cell counting algorithm on a large number of images using Image Processing Toolbox™ with MATLAB® MapReduce and datastores.

Computer Vision

Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
This example shows how to segment an image using a semantic segmentation network.
Multiclass Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox)
Train a YOLO v2 multiclass object detector and evaluate object detector performance across selected classes and overlap thresholds. (Since R2024b)
Automatically Label Ground Truth Using Segment Anything Model (Computer Vision Toolbox)
This example shows how to produce pixel labels for semantic segmentation using the Segment Anything Model (SAM) in the Image Labeler (Computer Vision Toolbox) app. (Since R2024b)
Detect Defects on Printed Circuit Boards Using YOLOX Network (Computer Vision Toolbox)
Detect, localize, and classify defects in printed circuit boards (PCBs) using a you only look once version X (YOLOX) deep learning network.

Medical Imaging

Breast Tumor Segmentation from Ultrasound Using Deep Learning (Medical Imaging Toolbox)
This example shows how to perform semantic segmentation of breast tumors from 2-D ultrasound images using a deep neural network.
Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network (Medical Imaging Toolbox)
Segment 2-D cardiac MRI images using U-Net, and explore predictions using Grad-CAM explainability maps.
Detect Nuclei in Large Whole Slide Images Using Cellpose (Medical Imaging Toolbox)
This example shows how to detect cell nuclei in whole slide images (WSIs) of tissue stained using hematoxylin and eosin (H&E) by using Cellpose.
Train Custom Cellpose Model (Medical Imaging Toolbox)
This example shows how to train a custom Cellpose model, using new training data, to detect noncircular shapes.

Related Information

Functions with gpuArray Support (Image Processing Toolbox)
Functions with Automatic Parallel Support (Image Processing Toolbox)
Functions with gpuArray Support (Computer Vision Toolbox)
Functions with Automatic Parallel Support (Computer Vision Toolbox)
Functions with gpuArray Support (Medical Imaging Toolbox)

Featured Examples

Perform Pixel-Based Operations on GPU

Perform pixel-based operations on a GPU by using functions that send both the data and the operations to the GPU for processing. This method is most effective for element-wise operations that require two or more data sets.

(Image Processing Toolbox)

Perform Thresholding and Morphological Operations on GPU

Perform image processing operations on a GPU. The example uses filtering to highlight the watery areas in an aerial photograph.

(Image Processing Toolbox)

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