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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 301–325 of 371 papers

TitleStatusHype
Complementary Segmentation of Primary Video Objects with Reversible Flows—0
Complexity-Adaptive Distance Metric for Object Proposals Generation—0
Composite Statistical Inference for Semantic Segmentation—0
Context Propagation from Proposals for Semantic Video Object Segmentation—0
Contour-Constrained Superpixels for Image and Video Processing—0
Co-occurrence Background Model with Superpixels for Robust Background Initialization—0
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing—0
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images—0
CRF Learning with CNN Features for Image Segmentation—0
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study—0
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—0
Deep convolutional networks for pancreas segmentation in CT imaging—0
Deep Deconvolutional Networks for Scene Parsing—0
DeepFH Segmentations for Superpixel-based Object Proposal Refinement—0
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks—0
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation—0
Deep Superpixel Cut for Unsupervised Image Segmentation—0
Delving Deep into Semantic Relation Distillation—0
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks—0
Depth-guided Free-space Segmentation for a Mobile Robot—0
Discrete-Continuous Depth Estimation from a Single Image—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions—0
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