Scale-Space and Edge Detection Using Anisotropic Diffusion
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
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
Shape extraction via heat flow analogy
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Image and volume segmentation by water flow
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Water flow based complex feature extraction
ACIVS'06 Proceedings of the 8th 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
IEEE Transactions on Image Processing
Anti-geometric diffusion for adaptive thresholding and fast segmentation
IEEE Transactions on Image Processing
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There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision.