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Superpixels

Superpixel techniques segment an image into regions based on similarity measures that utilize perceptual features, effectively grouping pixels that appear similar. The motivation behind this approach is to generate regions that provide meaningful descriptions while significantly reducing the data volume compared to using every individual pixel. By decreasing the number of primitives, these techniques reduce redundancy and simplify the complexity of recognition tasks. Superpixels replace the rigid structure of individual pixels with delineated regions that preserve meaningful content in the image, thereby aiding the interpretation of the scene’s structure and simplifying subsequent processing tasks. Generally, superpixel techniques rely on measures that evaluate color similarities and the shapes of regions, incorporating edges or significant changes in intensity to define these regions.

Papers

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Showing 76–100 of 371 papers

TitleStatusHype
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
Capturing global spatial context for accurate cell classification in skin cancer histology—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
Boosting Convolutional Features for Robust Object Proposals—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
A learning-based approach for automatic image and video colorization—0
ForestSplats: Deformable transient field for Gaussian Splatting in the Wild—0
A Weighted Sparse Coding Framework for Saliency Detection—0
Dynamic Spectral Residual Superpixels—0
A Higher-Order CRF Model for Road Network Extraction—0
Efficient 3D Room Shape Recovery From a Single Panorama—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges—0
Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions—0
Egocentric Hand Detection Via Dynamic Region Growing—0
Cascaded Scene Flow Prediction using Semantic Segmentation—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
Explaining Deep Neural Networks—0
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch—0
Extract and Merge: Superpixel Segmentation with Regional Attributes—0
Fast and Accurate Depth Estimation from Sparse Light Fields—0
Discrete-Continuous Depth Estimation from a Single Image—0
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances—0
Depth-guided Free-space Segmentation for a Mobile Robot—0
A Video Representation Using Temporal Superpixels—0
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