Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure and Motion from Line Segments in Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge landmarks in monocular SLAM
Image and Vision Computing
Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer
Intelligent Service Robotics
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This paper presents a task-oriented evaluation methodology for edge detectors. Performance is measured based on the task of structure from motion. Eighteen real image sequences from 2 different scenes varying in the complexity and scenery types are used. The task-level ground truth for each image sequence is manually specified in terms of the 3D motion and structure. An automated tool computes the accuracy of the motion and structure achieved using the set of edge maps. Parameter sensitivity and execution speed are also analyzed. Four edge detectors are compared. All implementations and data sets are publicly available.