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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 51–75 of 371 papers

TitleStatusHype
Action recognition in still images by latent superpixel classification—0
Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions—0
Composite Statistical Inference for Semantic Segmentation—0
Complexity-Adaptive Distance Metric for Object Proposals Generation—0
A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network—0
Complementary Segmentation of Primary Video Objects with Reversible Flows—0
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception—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
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling—0
An Iterative Spanning Forest Framework for Superpixel Segmentation—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—0
Adaptive strategy for superpixel-based region-growing image segmentation—0
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation—0
An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation—0
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans—0
3D Based Landmark Tracker Using Superpixels Based Segmentation for Neuroscience and Biomechanics Studies—0
Dynamic Spectral Residual Superpixels—0
Classifier Based Graph Construction for Video Segmentation—0
Cascaded Scene Flow Prediction using Semantic Segmentation—0
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos—0
Capturing global spatial context for accurate cell classification in skin cancer histology—0
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