A Three-Module Strategy for Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Localization Performance Measure and Optimal Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Edge Detectors for Ramp Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
"On the localization performance measure and optimal edge detection"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reply to "On the Localization Performance Measure and Optimal Edge Detection"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Edge Detection using Expansion Matching and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bootstrap based cooperative processes in computer vision
Bootstrap based cooperative processes in computer vision
Reliable Determination of Object Pose from Line Features by Hypothesis Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Σynergos—Synergetic VisionResearch
Real-Time Systems
Statistical Characterization of Morphological Operator Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Measuring the Self-Consistency of Stereo Algorithms
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
IEEE Transactions on Software Engineering
Error analysis of pattern recognition systems: the subsets bootstrap
Computer Vision and Image Understanding
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Use of Error Propagation for Statistical Validation of Computer Vision Software
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Performance Modeling and Algorithm Characterization for Robust Image Segmentation
International Journal of Computer Vision
Investigation of domain effects on software
Proceedings of the 47th Annual Southeast Regional Conference
Bootstrap resampling for image registration uncertainty estimation without ground truth
IEEE Transactions on Image Processing
Quantitative error measures for edge detection
Pattern Recognition
| Hi-index | 0.14 |
A new performance evaluation paradigm for computer vision systems is proposed. In real situation, the complexity of the input data and/or of the computational procedure can make traditional error propagation methods infeasible. The new approach exploits a resampling technique recently introduced in statistics, the bootstrap. Distributions for the output variables are obtained by perturbing the nuisance properties of the input, i.e., properties with no relevance for the output under ideal conditions. From these bootstrap distributions, the confidence in the adequacy of the assumptions embedded into the computational procedure for the given input is derived. As an example, the new paradigm is applied to the task of edge detection. The performance of several edge detection methods is compared both for synthetic data and real images. The confidence in the output can be used to obtain an edgemap independent of the gradient magnitude.