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Learn Segmentation Computer Vision Tutorial, validate concepts with Segmentation Computer Vision MCQ Questions, and prepare interviews through Segmentation Computer Vision Interview Questions and Answers.
Segmentation Overview MCQ
Partitioning images into meaningful regions: cues, energy minimization, oversegmentation, and bridges to semantic/instance segmentation.
Regions
Group pixels
Watershed
Flooding
Graph cuts
Energy min
Clustering
k-means etc.
Image segmentation overview
Segmentation assigns pixels to coherent regions or objects. Classical methods use intensity/color continuity, edges, or optimize an energy balancing data fidelity and smoothness.
Beyond thresholding
Global thresholds fail under complex scenes—region growing, mean-shift, watershed, and graph cuts add spatial coupling.
Methods
Region growing
Start seeds, merge pixels similar to region statistics—sensitive to seeds and thresholds.
Watershed
Treat gradient magnitude as topography; markers reduce oversegmentation.
Graph cuts
Pixels as nodes, pairwise smoothness + unary data terms; min-cut finds a partition.
Deep segmentation
FCN, U-Net, Mask R-CNN move toward semantic/instance tasks with learned features.
Goal
Pixels → regions or object masks with consistent labels