A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Shape Recognition Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planar shape recognition by directional flow-change method
Pattern Recognition Letters
Automatic line matching across views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Part Based Human Tracking In A Multiple Cues Fusion Framework
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Real-time people localization and tracking through fixed stereo vision
Applied Intelligence
A Contour-Based Moving Object Detection and Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Motion detection and object tracking with discrete leaky integrate-and-fire neurons
Applied Intelligence
An adaptive motion segmentation for automated video surveillance
EURASIP Journal on Advances in Signal Processing
Suitability of edge segment based moving object detection for real time video surveillance
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Recognizing Partially Occluded Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An edge-based approach to motion detection
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
MTES: visual programming environment for teaching and research in image processing
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Memory-based cognitive modeling for robust object extraction and tracking
Applied Intelligence
Image annotation by modeling Supporting Region Graph
Applied Intelligence
Hi-index | 0.00 |
Considering the robustness, stability and reduced volume of data, researchers have focused on using edge information in various video processing applications including moving object detection, tracking and target recognition. Though the edge information is more robust compared to intensity, it also exhibits variations in different frames due to illumination change and noise. In addition to this, the amount of variation varies from edge to edge. Thus, without making use of this variability information, it is difficult to obtain an optimal performance during edge matching. However, traditional edge pixel-based methods do not keep structural information of edges and thus they are not suitable to extract and hold this variability information. To achieve this, we represent edges as segments that make use of the structural and relational information of edges to allow extraction of this variability information. During edge matching, existing algorithms do not handle the size, positional and rotational variations to deal with edges of arbitrary shapes. In this paper, we propose a knowledge-based flexible edge matching algorithm where knowledge is obtained from the statistics on the environmental dynamics, and flexibility is to deal with the arbitrary shape and the geometric variations of edges by making use of this knowledge. In this paper, we detailed the effectiveness of the proposed matching algorithm in moving object detection and also indicated its suitability in other applications like target detection and tracking.