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 Labeler	Label images for computer vision applications
Video Labeler	Label video for computer vision applications

Functions

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Load Training Data

`combine`	Combine data from multiple datastores (Since R2019a)
`countEachLabel`	Count occurrence of pixel or box labels
`groundTruth`	Ground truth label data
`imageDatastore`	Datastore for image data
`pixelLabelDatastore`	Datastore for pixel label data
`pixelLabelTrainingData`	Create training data for semantic segmentation from ground truth

Augment and Preprocess Training Data

`balancePixelLabels`	Balance pixel labels by oversampling block locations in large images (Since R2020a)
`imwarp`	Apply geometric transformation to image
`imcrop`	Crop image
`imresize`	Resize image
`transform`	Transform datastore (Since R2019a)
`randomAffine2d`	Create randomized 2-D affine transformation (Since R2019b)
`randomWindow2d`	Randomly select rectangular region in image (Since R2021a)
`centerCropWindow2d`	Create rectangular center cropping window (Since R2019b)

Design Semantic Segmentation Deep Learning Networks

`deeplabv3plusLayers`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (Since R2019b)
`dicePixelClassificationLayer`	Create pixel classification layer using generalized Dice loss for semantic segmentation (Since R2019b)
`fcnLayers`	Create fully convolutional network layers for semantic segmentation
`pixelClassificationLayer`	Create pixel classification layer for semantic segmentation
`segnetLayers`	Create SegNet layers for semantic segmentation
`unetLayers`	Create U-Net layers for semantic segmentation
`unet3dLayers`	Create 3-D U-Net layers for semantic segmentation of volumetric images (Since R2019b)
`focalCrossEntropy`	Compute focal cross-entropy loss (Since R2020b)

Segment Images Using Deep Learning

semanticseg Semantic image segmentation using deep learning

Visualize Segmentation

`labeloverlay`	Overlay label matrix regions on 2-D image
`volshow`	Display volume
`insertObjectMask`	Insert masks in image or video stream (Since R2020b)

Evaluate Segmentation Results

`evaluateSemanticSegmentation`	Evaluate semantic segmentation data set against ground truth
`bfscore`	Contour matching score for image segmentation
`dice`	Sørensen-Dice similarity coefficient for image segmentation
`generalizedDice`	Generalized Sørensen-Dice similarity coefficient for image segmentation (Since R2021a)
`jaccard`	Jaccard similarity coefficient for image segmentation
`segmentationConfusionMatrix`	Confusion matrix of multi-class pixel-level image segmentation (Since R2020b)
`semanticSegmentationMetrics`	Semantic 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.
Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.