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A Learning Approach for Adaptive Image Segmentation

https://doi.org/10.1109/icvs.2006.4

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Interleaving Object Categorization and Segmentation

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Inducing Semantic Segmentation from an Example

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Non-parametric Model for Background Subtraction

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Yet Another Survey on Image Segmentation: Region and Boundary Information Integration

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Experience in Integrating Image Processing Programs

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Learning to Segment

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Towards ontology-based cognitive vision

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Real-time foreground–background segmentation using codebook model

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Strategies for image segmentation combining region and boundary information

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A Tutorial on Support Vector Machines for Pattern Recognition

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Efficient Graph-Based Image Segmentation

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A Simplex Method for Function Minimization

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Mean shift: a robust approach toward feature space analysis

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Borg: a knowledge-based system for automatic generation of image processing programs

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Knowledge-directed vision: control, learning, and integration

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Delayed reinforcement learning for adaptive image segmentation and feature extraction

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Adaptive image segmentation using genetic and hybrid search methods

inferredcites →https://doi.org/10.1109/cvpr.1999.784637openalex-snapshot

Adaptive background mixture models for real-time tracking

inferredcites →https://doi.org/10.1109/cvpr.2004.1315268openalex-snapshot

Learning to segment images using region-based perceptual features

inferredcites →https://doi.org/10.1109/cvpr.2006.164openalex-snapshot

Large-scale Learning with SVM and Convolutional for Generic Object Categorization

inferredcites →https://doi.org/10.1109/cvpr.2006.279openalex-snapshot

Shape-Based Approach to Robust Image Segmentation using Kernel PCA

inferredcites →https://doi.org/10.1109/iccv.2003.1238308openalex-snapshot

Learning a classification model for segmentation

inferredcites →https://doi.org/10.1109/icip.2002.1038966openalex-snapshot

Extraction of semantic objects from still images

inferredcites →https://doi.org/10.1109/icpr.1998.711240openalex-snapshot

Automatic identification of perceptually important regions in an image

inferredcites →https://doi.org/10.1109/icpr.2000.905328openalex-snapshot

Learning based interactive image segmentation

inferredcites →https://doi.org/10.1109/tip.2005.852204openalex-snapshot

Adaptive perceptual color-texture image segmentation

inferredcites →https://doi.org/10.1109/tip.2005.852462openalex-snapshot

Automatic selection of parameters for vessel/neurite segmentation algorithms

inferredcites →https://doi.org/10.1109/tpami.2003.1190572openalex-snapshot

A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification

inferredcites →https://doi.org/10.1109/tpami.2004.110openalex-snapshot

Statistical region merging

inferredcites →https://doi.org/10.1109/tpami.2004.1273918openalex-snapshot

Learning to detect natural image boundaries using local brightness, color, and texture cues

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Survey over image thresholding techniques and quantitative performance evaluation

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Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval

inferredcites →https://doi.org/10.1137/1.9781611975604openalex-snapshot

Practical Optimization

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Comparing images using color coherence vectors

inferredcites →https://doi.org/10.5244/c.17.78openalex-snapshot

Interleaved Object Categorization and Segmentation

inferredcites →https://doi.org/10.5555/1005332.1016789openalex-snapshot

Image Categorization by Learning and Reasoning with Regions

inferredcites →https://doi.org/10.5555/1005332.1016791openalex-snapshot

Probability Estimates for Multi-class Classification by Pairwise Coupling

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Color Image Segmentation

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Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers

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A cognitive vision approach to early pest detection in greenhouse crops

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Biologically-inspired robust motion segmentation using mutual information

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Human–computer interaction for the generation of image processing applications

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Pest and Disease Detection from Plant Leaves using Enhanced AlexNet Model

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Automated Pest Classification and Infestation Detection using CNN and Transfer Learning Techniques

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A Cognitive Vision Approach for Image Segmentation Thresholding Images of Historical Documents with Back-to-Front Interference

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A robust markerless vision-based human gait analysis system

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AN ONTOLOGY-BASED MODEL FOR REPRESENTING IMAGE PROCESSING APPLICATION OBJECTIVES

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Task and Context aware Performance Evaluation of Computer Vision Algorithms

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A Tuning Method for Diatom Segmentation Techniques

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Adaptive Image Thresholding of Yellow Peppers for a Harvesting Robot

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Spodoptera Litura Damage Severity Detection and Classification in Tomato Leaves

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A Learning Approach for Adaptive Image Segmentation

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Detection and Classification of Pests in Greenhouse Using Image Processing

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