The AETG System: An Approach to Testing Based on Combinatorial Design
IEEE Transactions on Software Engineering
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Medical Image Synthesis via Monte Carlo Simulation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Performance Modeling and Algorithm Characterization for Robust Image Segmentation
International Journal of Computer Vision
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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We propose a method to exponentially enlarge a small dataset of domain specific ground truth segmentation labels to evaluate the performance of segmentation algorithms. Furthermore, we adapt ideas from combinatorial software testing to efficiently infer statistics of segmentation performance by evaluating performance on only a certain subset of the combinatorially generated images. Extensions of this work to optimal sequence for performance testing and algorithm selection are also suggested.