Extended wedgelets: geometrical wavelets in efficient image coding
Machine Graphics & Vision International Journal
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy approach to performance evaluation of edge detectors
SSIP'07 Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing
Improving BTC image compression using a fuzzy complement edge operator
Signal Processing
Resolution analysis for Gradient Direction Matching of object model edges to overhead images
Computer Vision and Image Understanding
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
Complex wavelet structural similarity: a new image similarity index
IEEE Transactions on Image Processing
A new methodology for evaluation of edge detectors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Theory of a probabilistic-dependence measure of dissimilarity among multiple clusters
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
JCURVE: multiscale curve coding via second order beamlets
Machine Graphics & Vision International Journal - Special issue on Image Databases
How to select microscopy image similarity metrics?
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Quantitative error measures for edge detection
Pattern Recognition
Comparing image objects using tree-based approach
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Nested Partitions Properties for Spatial Content Image Retrieval
International Journal of Digital Library Systems
Hi-index | 0.14 |
The performance of several discrepancy measures for the comparison of edge images is analyzed and a novel similarity metric aimed at overcoming their problems is proposed. The algorithm finds an optimal matching of the pixels between the images and estimates the error produced by this matching. The resulting Pixel Correspondence Metric (PCM) can take into account edge strength as well as the displacement of edge pixel positions in the estimation of similarity. A series of experimental tests shows the new metric to be a robust and effective tool in the comparison of edge images when a small localization error of the detected edges is allowed.