Computer Vision, Graphics, and Image Processing
Logarithmic Tapering Graph Pyramid
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric Approach to Vector-Valued Image Segmentation
International Journal of Computer Vision
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Min-Cover Approach for Finding Salient Curves
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pyramid segmentation algorithms revisited
Pattern Recognition
Constructing stochastic pyramids by MIDES: maximal independent directed edge set
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
The construction of bounded irregular pyramids with a union-find decimation process
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
A novel biologically inspired attention mechanism for a social robot
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
A new perception-based segmentation approach using combinatorial pyramids
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
| Hi-index | 0.00 |
The efficiency of a pyramid segmentation approach mainly depends on the graph selected to encode the information within each pyramid level, on the reduction or decimation scheme used to build one graph from the graph below, and on the criteria employed to define if two adjacent regions are similar or not. This paper evaluates three pairwise comparison functions for perceptual grouping into a generic framework for image perceptual segmentation. This framework integrates the low---level definition of segmentation with a domain---independent perceptual grouping. The performance of the framework using the different comparison functions has been quantitatively evaluated with respect to ground-truth segmentation data using the Berkeley Segmentation Dataset and Benchmark providing satisfactory scores.