Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Defects in Finite-Difference Edge Finders
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Analysis with Application to Assistive Vision Technology
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Reference independent moving object detection: an edge segment based approach
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
Shape extraction via heat flow analogy
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Low level moving-feature extraction via heat flow analogy
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Anti-geometric diffusion for adaptive thresholding and fast segmentation
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 | 0.10 |
In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving-edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction.