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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 326–350 of 371 papers

TitleStatusHype
Image Segmentation Using Hierarchical Merge TreeCode0
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling—0
Deep convolutional networks for pancreas segmentation in CT imaging—0
CRF Learning with CNN Features for Image Segmentation—0
Superpixelizing Binary MRF for Image Labeling Problems—0
Boosting Convolutional Features for Robust Object Proposals—0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—0
Weakly Supervised Learning for Salient Object Detection—0
Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks—0
What is a salient object? A dataset and a baseline model for salient object detection—0
Feedforward semantic segmentation with zoom-out featuresCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
Deep Deconvolutional Networks for Scene Parsing—0
Visual Chunking: A List Prediction Framework for Region-Based Object Detection—0
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans—0
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm—0
A Context-aware Delayed Agglomeration Framework for Electron Microscopy SegmentationCode0
Generating Object Segmentation Proposals using Global and Local Search—0
Tell Me What You See and I will Show You Where It Is—0
Learning Optimal Seeds for Diffusion-based Salient Object Detection—0
Discrete-Continuous Depth Estimation from a Single Image—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels—0
Segmentation-aware Deformable Part Models—0
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