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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 301–350 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
Dynamic Spectral Residual Superpixels—0
Efficient 3D Room Shape Recovery From a Single Panorama—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges—0
Egocentric Hand Detection Via Dynamic Region Growing—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
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels—0
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances—0
Fast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering—0
Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification—0
Focal Loss Analysis of Peripapillary Nerve Fiber Layer Reflectance for Glaucoma Diagnosis—0
Foreground Clustering for Joint Segmentation and Localization in Videos and Images—0
ForestSplats: Deformable transient field for Gaussian Splatting in the Wild—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
From Superpixel to Human Shape Modelling for Carried Object Detection—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
Fuzzy Superpixel-based Image Segmentation—0
Generating Object Segmentation Proposals using Global and Local Search—0
Generating Superpixels for High-resolution Images with Decoupled Patch Calibration—0
Generating superpixels using deep image representations—0
Geodesic Distance Histogram Feature for Video Segmentation—0
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