Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space Scale Localization, Blur, and Contour-Based Image Coding
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Edge Detection and Ridge Detection with Automatic Scale Selection
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Noise reduction and edge detection via kernel anisotropic diffusion
Pattern Recognition Letters
Nonlinear Scale Space with Spatially Varying Stopping Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Image enhancement and denoising by complex diffusion processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations
IEEE Transactions on Image Processing
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A novel nonlinear scale space framework is proposed for the purpose of multi-scale image representation. The scale space decomposition problem is formulated as a general Bayesian least-squares estimation problem. A quasi-random density estimation approach is introduced for estimating the posterior distribution between consecutive scale space realizations. In addition, the application of the proposed nonlinear scale space framework for edge detection is proposed. Experimental results demonstrate the effectiveness of the proposed scale space framework for constructing scale space representations with significantly better structural localization across all scales when compared to state-of-the-art scale space frameworks such as anisotropic diffusion, regularized nonlinear diffusion, complex nonlinear diffusion, and iterative bilateral scale space methods, especially under scenarios with high noise levels.