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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 251–300 of 371 papers

TitleStatusHype
Non-parametric spatially constrained local prior for scene parsing on real-world data—0
Object Detection by Labeling Superpixels—0
Object Scene Flow for Autonomous Vehicles—0
Online Localization and Prediction of Actions and Interactions—0
Optimized clothes segmentation to boost gender classification in unconstrained scenarios—0
Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation—0
PDC: Piecewise Depth Completion utilizing Superpixels—0
Peekaboo - Where are the Objects? Structure Adjusting Superpixels—0
Perceptual Group Tokenizer: Building Perception with Iterative Grouping—0
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images—0
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph—0
Pixel-Level Clustering Network for Unsupervised Image Segmentation—0
Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery—0
Power-SLIC: Fast Superpixel Segmentations by Diagrams—0
Predicting the Where and What of Actors and Actions Through Online Action Localization—0
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow—0
Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation—0
Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation—0
Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection—0
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection—0
"RAPID" Regions-of-Interest Detection In Big Histopathological Images—0
Real-Time Coarse-to-Fine Topologically Preserving Segmentation—0
Recognizing Image Objects by Relational Analysis Using Heterogeneous Superpixels and Deep Convolutional Features—0
Reinforcement Cutting-Agent Learning for Video Object Segmentation—0
Resolution-independent meshes of super pixels—0
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation—0
Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms—0
Rethinking Unsupervised Neural Superpixel Segmentation—0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs—0
Robust Region Grouping via Internal Patch Statistics—0
Robust superpixels using color and contour features along linear path—0
Saliency Detection via Bidirectional Absorbing Markov Chain—0
Saliency Detection via Graph-Based Manifold Ranking—0
Iterative Saliency Enhancement using Superpixel Similarity—0
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering—0
Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach—0
SCALP: Superpixels with Contour Adherence using Linear Path—0
Scene Labeling Using Beam Search Under Mutex Constraints—0
Segmentation-aware Deformable Part Models—0
Segmentation-Aware Hyperspectral Image Classification—0
Semantic 3D Occupancy Mapping through Efficient High Order CRFs—0
Semantic Component Analysis—0
Semantic interpretation for convolutional neural networks: What makes a cat a cat?—0
Semantic Object Parsing with Graph LSTM—0
Semi-Automated Segmentation of Geoscientific Data Using Superpixels—0
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification—0
Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models—0
Single-View 3D Scene Parsing by Attributed Grammar—0
SP ^3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation—0
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