A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line-Based Recognition Using A Multidimensional Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate object contour tracking based on boundary edge selection
Pattern Recognition
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
We propose a segment based moving edge detection algorithm by building association from multi-frames of the scene. A statistical background model is used to segregate the moving segments that utilize shape and position information. Edge specific knowledge depending upon background environment is computed and thresholds are determined automatically. Statistical background model gives flexibility for matching background edges. Building association within the moving segments of multi-frame enhances the detection procedure by suppressing noisy detection of flickering segments that occurs frequently due to noise, illumination variation and reflectance in the scene. The representation of edge as edge segment allows us to incorporate this knowledge about the background environment. Experiments with noisy images under varying illumination changing situation demonstrates the robustness of the proposed method in comparison with existing edge pixel based moving object detection methods.