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Meta-Evaluation of Image Segmentation Using Machine Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0165-1684(93)90107-lopenalex-snapshot
Image thresholding: Some new techniques
inferredcites →https://doi.org/10.1016/0734-189x(88)90022-9openalex-snapshot
A survey of thresholding techniques
inferredcites →https://doi.org/10.1016/j.sigpro.2005.09.016openalex-snapshot
Scalable multiresolution color image segmentation
inferredcites →https://doi.org/10.1016/s0167-8655(98)00052-xopenalex-snapshot
Quantitative evaluation of color image segmentation results
inferredcites →https://doi.org/10.1023/a:1019956318069openalex-snapshot
A Perspective View and Survey of Meta-Learning
inferredcites →https://doi.org/10.1109/34.1000236openalex-snapshot
Mean shift: a robust approach toward feature space analysis
inferredcites →https://doi.org/10.1109/34.297949openalex-snapshot
Multiresolution color image segmentation
inferredcites →https://doi.org/10.1109/34.868688openalex-snapshot
Normalized cuts and image segmentation
inferredcites →https://doi.org/10.1109/cvpr.2004.465openalex-snapshot
Towards Perceptually Driven Segmentation Evaluation Metrics
inferredcites →https://doi.org/10.1109/cvpr.2005.284openalex-snapshot
Quantitative Evaluation of a Novel Image Segmentation Algorithm
inferredcites →https://doi.org/10.1109/icassp.2000.859280openalex-snapshot
Genetic fusion: application to multi-components image segmentation
inferredcites →https://doi.org/10.1109/icassp.2004.1326614openalex-snapshot
The use of visible color difference in the quantitative evaluation of color image segmentation
inferredcites →https://doi.org/10.1109/iccv.2001.937655openalex-snapshot
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
inferredcites →https://doi.org/10.1109/icpr.2004.1334206openalex-snapshot
Unsupervised evaluation of image segmentation application to multi-spectral images
inferredcites →https://doi.org/10.1109/tip.2002.807355openalex-snapshot
Objective evaluation of video segmentation quality
inferredcites →https://doi.org/10.1109/tip.2004.828427openalex-snapshot
Performance Measures for Video Object Segmentation and Tracking
inferredcites →https://doi.org/10.1109/tpami.1985.4767640openalex-snapshot
Dynamic Measurement of Computer Generated Image Segmentations
inferredcites →https://doi.org/10.1109/tpami.2003.1217608openalex-snapshot
A method for objective edge detection evaluation and detector parameter selection
inferredcites →https://doi.org/10.1109/tsmc.1978.4310038openalex-snapshot
Threshold Evaluation Techniques
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1117/12.527167openalex-snapshot
An entropy-based objective evaluation method for image segmentation
inferredcites →https://doi.org/10.1117/12.604213openalex-snapshot
A co-evaluation framework for improving segmentation evaluation
inferredcites →https://doi.org/10.5281/zenodo.37595openalex-snapshot
Estimation Of Video Object Relevance
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Enhanced Reinforcement Learning Model for Extraction of Objects in Complex Imaging
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A Data Fusion-Based Framework for Image Segmentation Evaluation
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Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps
inferred← citeshttps://doi.org/10.1007/s10462-020-09830-9openalex-snapshot
Image segmentation evaluation: a survey of methods
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Performance Modeling and Algorithm Characterization for Robust Image Segmentation
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Image segmentation evaluation: A survey of unsupervised methods
inferred← citeshttps://doi.org/10.1016/j.imavis.2013.08.002openalex-snapshot
A new evaluation measure for color image segmentation based on genetic programming approach
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Automating image segmentation verification and validation by learning test oracles
inferred← citeshttps://doi.org/10.1016/j.media.2020.101895openalex-snapshot
Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model
inferred← citeshttps://doi.org/10.1016/j.media.2023.102773openalex-snapshot
Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer
inferred← citeshttps://doi.org/10.1109/access.2025.3625584openalex-snapshot
nnHCCNeXt: A Lightweight Algorithm for Hepatocellular Carcinoma Segmentation in 3D Venous Phase CT Imaging
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Unsupervised estimation of segmentation quality using nonnegative factorization
inferred← citeshttps://doi.org/10.1109/icsip55141.2022.9886900openalex-snapshot
Research on Evaluation Method of Aerial Image Segmentation Algorithm
inferred← citeshttps://doi.org/10.1109/ieeeconf38699.2020.9389163openalex-snapshot
Reducing Annotation Times: Semantic Segmentation of Coral Reef Survey Images
inferred← citeshttps://doi.org/10.1109/iske.2017.8258832openalex-snapshot
H2: Fusion of HOG and Harris features for image segmentation evaluation
inferred← citeshttps://doi.org/10.1109/jas.2026.125720openalex-snapshot
High-Order Interaction and Low-Order Parallelization of Features Fusion with Novel Mamba-UNet Architecture for Medical Image Segmentation
inferred← citeshttps://doi.org/10.1109/tip.2011.2169268openalex-snapshot
Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization
inferred← citeshttps://doi.org/10.1109/tmi.2017.2665165openalex-snapshot
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
inferred← citeshttps://doi.org/10.22215/etd/2009-09134openalex-snapshot
Learning a test Oracle towards automating image segmentation evaluation
inferred← citeshttps://doi.org/10.22215/etd/2010-09391openalex-snapshot
The development of a semi-automated testing method for medical imaging systems
inferred← citeshttps://doi.org/10.2316/p.2013.798-082openalex-snapshot
Ensemble Evaluation for Image Segmentation using a Semi-Supervised SVM
inferred← citeshttps://doi.org/10.33260/zictjournal.v1i1.12openalex-snapshot
HOSUR: A Novel Measure for Evaluation of Image Segmentation Quality
inferred← citeshttps://doi.org/10.3390/rs9080769openalex-snapshot
On the Objectivity of the Objective Function—Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy
inferred← citeshttps://doi.org/10.48550/arxiv.1509.07244openalex-snapshot
Multi-Region Probabilistic Dice Similarity Coefficient using the Aitchison Distance and Bipartite Graph Matching
inferred← citeshttps://doi.org/10.48550/arxiv.2107.11795openalex-snapshot
Character Spotting Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.5244/c.22.16openalex-snapshot
Parameter Selection for Graph Cut Based Image Segmentation
inferred← citeshttps://doi.org/10.5772/15807openalex-snapshot
Algorithm Selection Based on a Region Similarity Metric for Intracellular Image Segmentation
inferred← citeshttps://doi.org/10.70675/6f691b31z766dz4a4azb504za7b14d4cf213openalex-snapshot
Uncertainty-based Deep Learning Methods for Robust and Reliable Cardiac MRI Segmentation and Analysis
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