Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Efficient Image Segmentation Using a Generic and Non-Parametric Approach
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An optimization algorithm using orthogonal arrays in discrete design space for structures
Finite Elements in Analysis and Design
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
Alternating scheme for supervised parameter learning with application to image segmentation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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This paper employs the methods from the design of experiments for supervised parameter learning in image segmentation. We propose to use orthogonal arrays in order to keep the number of experiments small and several algorithms are formulated. Analysis of means is applied to estimate the optimal parameter settings. In addition, a combination of orthogonal arrays and genetic algorithm is used to further improve the performance. The proposed algorithms are experimentally validated based on two segmentation algorithms and the Berkeley image database. A comparison with exhaustive search, an alternating scheme and a Monte-Carlo approach is also provided.