Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from defocus: a spatial domain approach
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sodar image segmentation by fuzzy c-means
Signal Processing
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation with a fuzzy clustering algorithm based on Ant-Tree
Signal Processing
Skin Pores Detection for Image-Based Skin Analysis
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
New neutrosophic approach to image segmentation
Pattern Recognition
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Modified fuzzy c-means algorithm for segmentation of T1-T2-weighted brain MRI
Journal of Computational and Applied Mathematics
A color- and texture-based image segmentation algorithm
Machine Graphics & Vision International Journal
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
A hybrid particle swarm optimisation with differential evolution approach to image segmentation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Segmentation for high-throughput image analysis: watershed masked clustering
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: applications and case studies - Volume Part II
Two-dimensional extension of variance-based thresholding for image segmentation
Multidimensional Systems and Signal Processing
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A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques.