Rapid and brief communication: Color edge detection using the minimal spanning tree

Authors:
Ch. Theoharatos;G. Economou;S. Fotopoulos
Affiliations:
Electronics Laboratory, Department of Physics, University of Patras, Patras 26500, Greece;Electronics Laboratory, Department of Physics, University of Patras, Patras 26500, Greece;Electronics Laboratory, Department of Physics, University of Patras, Patras 26500, Greece
Venue:
Pattern Recognition
Year:
2005

Citing 4
Cited 4

Edge, Junction, and Corner Detection Using Color Distributions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in multispectral images using the self-organizing map

Pattern Recognition Letters
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
Color edge detection using vector order statistics

IEEE Transactions on Image Processing

Multivariate image similarity in the compressed domain using statistical graph matching

Pattern Recognition
Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation

Journal of Visual Communication and Image Representation
ASOD: Arbitrary shape object detection

Engineering Applications of Artificial Intelligence
A new search mechanism for unstructured peer-to-peer networks

AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this study, the edge detection task in vector-valued images is examined as a clustering problem. Using samples within a data window, the minimal spanning tree (MST) provides the ordering of multivariate observations and facilitates the identification of similar classes. The edge detector parameters like edge strength, type and orientation are subsequently determined from the clustered data. Experiments and comparisons are performed, revealing the enhanced performance of the proposed approach.