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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 151–200 of 371 papers

TitleStatusHype
Power-SLIC: Fast Superpixel Segmentations by Diagrams—0
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation—0
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering—0
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse EncoderCode0
Explaining Deep Neural Networks—0
Visual Object Tracking by Segmentation with Graph Convolutional Network—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed—0
Extract and Merge: Superpixel Segmentation with Regional Attributes—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—0
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing—0
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images—0
Non-parametric spatially constrained local prior for scene parsing on real-world data—0
SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVsCode0
Warping Residual Based Image Stitching for Large Parallax—0
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical ImagesCode0
Co-occurrence Background Model with Superpixels for Robust Background Initialization—0
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching—0
Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
Superpixel-Based Background Recovery from Multiple Images—0
Resolution-independent meshes of super pixels—0
Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological DataCode0
Superpixel Tensor Pooling for Visual Tracking using Multiple Midlevel Visual Cues Fusion—0
Dynamic Spectral Residual Superpixels—0
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations—0
Bayesian Adaptive Superpixel SegmentationCode0
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances—0
View-Consistent 4D Light Field Superpixel SegmentationCode0
Learning Propagation for Arbitrarily-structured Data—0
A superpixel-driven deep learning approach for the analysis of dermatological wounds—0
Real-time Scalable Dense Surfel MappingCode0
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks—0
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing—0
Gradient Weighted Superpixels for Interpretability in CNNs—0
Recognizing Image Objects by Relational Analysis Using Heterogeneous Superpixels and Deep Convolutional Features—0
Image Classification with Hierarchical Multigraph NetworksCode0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
Unsupervised skin tissue segmentation for remote photoplethysmography—0
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing—0
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation—0
Segmentation-Aware Hyperspectral Image Classification—0
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches—0
SCALP: Superpixels with Contour Adherence using Linear Path—0
Robust superpixels using color and contour features along linear path—0
Superpixel-based Color Transfer—0
Superpixel Contracted Graph-Based Learning for Hyperspectral Image ClassificationCode0
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