Elements of information theory
Elements of information theory
Clustering Algorithms
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Dot Pattern Processing Using Voronoi Neighborhoods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
A new method for non-spherical and multi-density clustering
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Impact of object extraction methods on classification performance in surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
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We propose an algorithm that groups points similarly to how human observers do. It is simple, totally unsupervised and able to find clusters of complex and not necessarily convex shape. Groups are identified as the connected components of a Reduced Delaunay Graph (RDG) that we define in this paper. Our method can be seen as an algorithmic equivalent of the gestalt law of perceptual grouping according to proximity. We introduce a measure of dissimilarity between two different groupings of a point set and use this measure to compare our algorithm with human visual perception and the k-means clustering algorithm. Our algorithm mimics human perceptual grouping and outperforms the k-means algorithm in all cases that we studied. We also sketch a potential application in the segmentation of structural textures.