Segmentation Analysis
TopicOverview »
The Wolfram Language includes a variety of low- and high-level image segmentation techniques from clustering, watershed and region growing to semantic and object segmentation using neural networks. Segmentation functions can be used together with a rich set of functions for pre- and post-processing for more robust results as well as analysis functions performed on the result of the segmentation.
Foreground-Background Separation
RemoveBackground — separate the background from the foreground of the image
ImageSegmentationFilter — segment an object or region in an image
Binary Segmentation
Binarize — segmentation by thresholding pixel intensities
MorphologicalBinarize ▪ LocalAdaptiveBinarize ▪ RegionBinarize ▪ ChanVeseBinarize
Multi-Component Segmentation
ImageSegmentationComponents — high-level segmentation using neural networks
ClusteringComponents — segmentation based on cluster analysis
WatershedComponents — segmentation based on watershed methods
GrowCutComponents — segmentation using cellular automata evolution
MorphologicalComponents — find morphologically connected components
ImageForestingComponents — segmentation based on pixel graph connectivity
ArrayComponents — find identical components
Object Segmentation
ImageCases, ImageContents — detect, recognize and segment objects in an image
Semantic Segmentation
Neural networks for semantic segmentation.
"YOLO V8 Segment Trained on MS-COCO Data" ▪ "Ademxapp Model A1 Trained on Cityscapes Data" ▪ "Dilated ResNet-22 Trained on Cityscapes Data" ▪ …
Component Analysis
ComponentMeasurements — shape and color analysis
SelectComponents ▪ DeleteSmallComponents ▪ DeleteBorderComponents
Colorize — color every segment differently
HighlightImage — highlight region of interest
Image Preparation
ColorQuantize — reduce the number of distinct colors in an image
FillingTransform — reduce noise to create smooth regions in an image
GradientFilter, RangeFilter — create an edge map from an image