La traduzione di questa pagina non è aggiornata. Fai clic qui per vedere l'ultima versione in inglese.

Segmentazione semantica

Segmentazione semantica dell'immagine

La segmentazione semantica associa a ogni pixel di un'immagine un'etichetta di classe, come fiore, persona, strada, cielo o automobile. Utilizzare le applicazioni Image Labeler e Video Labeler per etichettare i pixel in modo interattivo ed esportare i dati delle etichette per l'addestramento di una rete neurale.

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

App

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

Funzioni

espandi tutto

Caricamento dei dati di addestramento

`combine`	Combine data from multiple datastores
`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

Aumento e pre-elaborazione dei dati di addestramento

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

Progettazione di reti di Deep Learning con segmentazione semantica

`unet`	Create U-Net convolutional neural network for semantic segmentation (Da R2024a)
`unet3d`	Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images (Da R2024a)
`deeplabv3plus`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (Da R2024a)
`focalCrossEntropy`	Compute focal cross-entropy loss (Da R2020b)

Segmentazione delle immagini utilizzando il Deep Learning

`semanticseg`	Semantic image segmentation using deep learning
`segmentAnythingModel`	Pretrained Segment Anything Model (SAM) for semantic segmentation (Da R2024a)

Visualizzazione della segmentazione

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

Valutazione dei risultati della segmentazione

`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 (Da R2021a)
`jaccard`	Jaccard similarity coefficient for image segmentation
`segmentationConfusionMatrix`	Confusion matrix of multi-class pixel-level image segmentation (Da R2020b)
`semanticSegmentationMetrics`	Semantic segmentation quality metrics

Argomenti

Come iniziare

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 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.

Creazione di dati di addestramento per la segmentazione semantica

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.