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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 51–100 of 371 papers

TitleStatusHype
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos—0
Superpixel Cost Volume Excitation for Stereo Matching—0
Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data—0
SP ^3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation—0
Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection—0
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem—0
A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network—0
How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests—0
Lagrangian Motion Fields for Long-term Motion Generation—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Persistence Image from 3D Medical Image: Superpixel and Optimized Gaussian CoefficientCode0
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
Deep Spherical SuperpixelsCode0
Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image AnalysisCode0
Context Propagation from Proposals for Semantic Video Object Segmentation—0
Leveraging Activations for Superpixel Explanations—0
Focal Loss Analysis of Peripapillary Nerve Fiber Layer Reflectance for Glaucoma Diagnosis—0
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation—0
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover ClassificationCode0
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral ImagesCode0
SPFormer: Enhancing Vision Transformer with Superpixel Representation—0
Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation—0
SLICE: Stabilized LIME for Consistent Explanations for Image ClassificationCode0
Perceptual Group Tokenizer: Building Perception with Iterative Grouping—0
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical ImagesCode0
Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification—0
Depth-guided Free-space Segmentation for a Mobile Robot—0
An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation—0
Pixel-Level Clustering Network for Unsupervised Image Segmentation—0
Superpixel Semantics Representation and Pre-training for Vision-Language Task—0
Superpixel Transformers for Efficient Semantic Segmentation—0
Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms—0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
Learning Semantic Segmentation with Query Points Supervision on Aerial ImagesCode0
Superpixels algorithms through network community detectionCode0
TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo—0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT SegmentationCode0
A differentiable Gaussian Prototype Layer for explainable Segmentation—0
SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks—0
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch—0
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)—0
Semi-Automated Segmentation of Geoscientific Data Using Superpixels—0
Fuzzy Superpixel-based Image Segmentation—0
Unsupervised Superpixel Generation using Edge-Sparse Embedding—0
MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging—0
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks—0
Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)—0
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