Edge Detection from Global and Local Views Using an Ensemble of Multiple Edge Detectors

Authors:
Yuchou Chang;Dah-Jye Lee;Yi Hong;James Archibald
Affiliations:
Dept. of Electrical and Computer Eng., Brigham Young University, Provo, USA;Dept. of Electrical and Computer Eng., Brigham Young University, Provo, USA;Dept. of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Dept. of Electrical and Computer Eng., Brigham Young University, Provo, USA
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Year:
2008

Citing 8
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing

Fundamentals of digital image processing
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing

Digital Image Processing
Spectral Segmentation with Multiscale Graph Decomposition

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Animals on the Web

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Combining Information Extraction Systems Using Voting and Stacked Generalization

The Journal of Machine Learning Research
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Edge detectors such as Canny, Sobel, Prewitt, and Laplacian consider only local grayscale transition strength or contrast. From a global point of view, considering the entire image, detection results using these operators either include too many noisy edge points when the threshold value is low, or they fail to include correct edges when the threshold value is high. A novel algorithm for edge detection that incorporates global constraints with local contrast information to address this issue is presented in this paper. We describe explorative research on edge detection using collaborative learning. An evaluation of the proposed algorithm reveals very promising results.