A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
The Use of Active Shape Models for Locating Structures in Medical Images
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
A Markov random field approach to multi-scale shape analysis
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A general framework for low level vision
IEEE Transactions on Image Processing
A Markov random field approach to multi-scale shape analysis
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Hand gesture tracking and recognition system for control of consumer electronics
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Australian sign language recognition using moment invariants
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Traditionally, image blurring by diffusion is done in Euclidean space, in an image-based coordinate system. This will blur edges at object boundaries, making segmentation difficult. Geometry-driven diffusion [1] uses a geometric model to steer the blurring, so as to blur along the boundary (to overcome noise) but edge-detect across the object boundary. In this paper, we present a scale-space on image profiles taken about the object boundary, in an object-intrinsic coordinate system. The profiles are sampled from the image in the fashion of Active Shape Models [2], and a scale-space is constructed on the profiles, where diffusion is run only in directions tangent to the boundary. Features from the scale-space are then used to build a statistical model of the image structure about the boundary, trained on a population of images with corresponding geometric models. This statistical image match model can be used in an image segmentation framework. Results are shown in 2D on synthetic and real-world objects; the methods can also be extended to 3D.