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Semantic Segmentation

Semantic image segmentation

Semantic segmentation associates each pixel of an image with a class label, such as flower, person, road, sky, or car. Use the Image Labeler and the Video Labeler apps to interactively label pixels and export the label data for training a neural network.

Input image of a seascape, then a series of cubes representing a deep learning network and a semantic segmented output image of the input.

Apps

Image LabelerLabel images for computer vision applications
Video LabelerLabel video for computer vision applications

Functions

expand all

combineCombine data from multiple datastores
countEachLabelCount occurrence of pixel or box labels
groundTruthGround truth label data
imageDatastoreDatastore for image data
pixelLabelImageDatastoreDatastore for semantic segmentation networks
pixelLabelDatastoreDatastore for pixel label data
pixelLabelTrainingDataCreate training data for semantic segmentation from ground truth
balancePixelLabelsBalance pixel labels by oversampling block locations in large images
imwarpApply geometric transformation to image
imcropCrop image
imresizeResize image
transformTransform datastore
randomAffine2dCreate randomized 2-D affine transformation
randomWindow2dRandomly select rectangular region in image
centerCropWindow2dCreate rectangular center cropping window
deeplabv3plusLayersCreate DeepLab v3+ convolutional neural network for semantic image segmentation
dicePixelClassificationLayerCreate pixel classification layer using generalized Dice loss for semantic segmentation
fcnLayersCreate fully convolutional network layers for semantic segmentation
pixelClassificationLayerCreate pixel classification layer for semantic segmentation
segnetLayersCreate SegNet layers for semantic segmentation
unetLayersCreate U-Net layers for semantic segmentation
unet3dLayersCreate 3-D U-Net layers for semantic segmentation of volumetric images
focalCrossEntropyCompute focal cross-entropy loss
semanticsegSemantic image segmentation using deep learning
labeloverlayOverlay label matrix regions on 2-D image
labelvolshowDisplay labeled volume
insertObjectMask Insert masks in image or video stream
evaluateSemanticSegmentationEvaluate semantic segmentation data set against ground truth
bfscoreContour matching score for image segmentation
diceSørensen-Dice similarity coefficient for image segmentation
generalizedDiceGeneralized Sørensen-Dice similarity coefficient for image segmentation
jaccardJaccard similarity coefficient for image segmentation
segmentationConfusionMatrixConfusion matrix of multi-class pixel-level image segmentation
semanticSegmentationMetricsSemantic segmentation quality metrics

Topics

Get Started

Label Pixels for Semantic Segmentation

Label pixels for training a semantic segmentation network by using a labeling app.

How Labeler Apps Store Exported Pixel Labels

Learn how the labeling apps store pixel label data.

Choose Function to Visualize Detected Objects

Compare visualization functions.

Getting Started with Mask R-CNN for Instance Segmentation

Perform multiclass instance segmentation using Mask R-CNN and deep learning.

Getting Started with Semantic Segmentation Using Deep Learning

Segment objects by class using deep learning.

Getting Started with Point Clouds Using Deep Learning

Understand how to use point clouds for deep learning.

Create Training Data for Semantic Segmentation

Datastores for Deep Learning (Deep Learning Toolbox)

Learn how to use datastores in deep learning applications.

Training Data for Object Detection and Semantic Segmentation

Create training data for object detection or semantic segmentation using the Image Labeler or Video Labeler.

Featured Examples

Semantic Segmentation Using Dilated Convolutions

Semantic Segmentation Using Dilated Convolutions

Semantic Segmentation Using Dilated Convolutions.

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Semantic Segmentation of Multispectral Images Using Deep Learning

Semantic Segmentation of Multispectral Images Using Deep Learning

Perform semantic segmentation of a multispectral image with seven channels using a U-Net.

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3-D Brain Tumor Segmentation Using Deep Learning

3-D Brain Tumor Segmentation Using Deep Learning

Train a 3-D U-Net neural network and perform semantic segmentation of brain tumors from 3-D medical images.

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Define Custom Pixel Classification Layer with Tversky Loss

Define Custom Pixel Classification Layer with Tversky Loss

Define Custom Pixel Classification Layer with Tversky Loss.

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Generate Image from Segmentation Map Using Deep Learning

Generate Image from Segmentation Map Using Deep Learning

Generate a synthetic image of a scene from a semantic segmentation map.

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Train Simple Semantic Segmentation Network in Deep Network Designer

Train Simple Semantic Segmentation Network in Deep Network Designer

Create and train a simple semantic segmentation network using Deep Network Designer.

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Explore Semantic Segmentation Network Using Grad-CAM

Explore Semantic Segmentation Network Using Grad-CAM

Explore the predictions of a semantic segmentation network using Grad-CAM.

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Computer Vision Toolbox Documentation

Support

Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

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