Adaptive-Scale Filtering and Feature Detection Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edges as Outliers: Anisotropic Smoothing Using Local Image Statistics
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Adaptive Smoothing via Contextual and Local Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Tensor scale: An analytic approach with efficient computation and applications
Computer Vision and Image Understanding
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A novel local scale controlled piecewise linear diffusion for selective smoothing and edge detection is presented. The diffusion stops at the place and time determined by the minimum reliable local scale and a spatial variant, anisotropic local noise estimate. It shows nisotropic, nonlinear diffusion equation using diffusion coefficients/tensors that continuously depend on the gradient is not necessary to achieve sharp, undistorted, stable edge detection across many scales. The new diffusion is anisotropic and asymmetric only at places it needs to be i.e., at significant edges. It not only does not diffuseacross significant edges, but also enhances edges. It advances geometry-driven diffusion because it is piecewise linear model rather than a full nonlinear model, thus it is simple to implement and analyze, and avoids the difficulties and problems associated with nonlinear diffussion. It advances local scale control by introducing spatial variant, anisotropic local noise estimation, and local stopping of diffusion. The original local scale control was based on the unrealistic assumption of uniformly distributed noise independentof the image signal. The local noise estimate significantly improves local scale control.