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Image Processing Toolbox
Image Processing Toolbox™ provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large data sets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
You can accelerate your algorithms by running them on multicore processors and GPUs. Many toolbox functions support C/C++ code generation for desktop prototyping and embedded vision system deployment.
Exploration and Discovery
Use apps and functions to acquire, visualize, analyze, and process images in many data types.
Acquiring and Importing Data
Import images and video generated by a wide range of devices, including webcams, digital cameras, satellite and airborne sensors, medical imaging devices, microscopes, telescopes, and other scientific instruments.
Support for a number of specialized image file formats. For medical images, it supports DICOM files, including associated metadata, as well as the Analyze 7.5 and Interfile formats.
Display High Dynamic Range Image
Image Preprocessing
Increase the signal-to-noise ratio and accentuate image features using custom or predefined filters.
Image Analysis
Extract meaningful information from images, such as finding shapes, counting objects, identifying colors, or measuring object properties.
Edge Detection
Identify object boundaries in an image using pre-built algorithms. These algorithms include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian methods.
Image Region Analyzer App
Hough Transform, Statistical Functions, and Color Space Conversions
Find line segments, line endpoints, and circles. Statistical functions let you analyze the characteristics of an image. Color-space conversion accurately represents color independently from devices.
Detect and Measure Circular Objects in an Image
Volume Segmenter App
Segment 3-D grayscale or RGB volumetric images
Deep Learning
Resize 2-D and 3-D input layers and spatial dimensions of deep learning arrays by a scale factor or to a specified size
Image Quality Metrics
Measure image color using X-Rite ColorChecker test chart
Color Error
Calculate color differences using CIE76, CIE94, or CIEDE2000 standard
DICOM-RT Contours
Create volumetric mask from contour data
See release notes for details on any of these features and corresponding functions.
MATLAB for Deep Learning
With just a few lines of MATLAB code, you can apply deep learning techniques to your work whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems.
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