International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Branch Points in One-Dimensional Gaussian Scale Space
Journal of Mathematical Imaging and Vision
Image Editing in the Contour Domain
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
What Do Features Tell about Images?
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Image Editing in the Contour Domain
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Technical Section: Hyper-Resolution: Image detail reconstruction through parametric edges
Computers and Graphics
Diffusion constraints for vector graphics
NPAR '10 Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering
Quasi-random nonlinear scale space
Pattern Recognition Letters
Sketch-based aesthetic product form exploration from existing images using piecewise clothoid curves
Journal of Visual Languages and Computing
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We have recently proposed a scale-adaptive algorithm for reliable edge detection and blur estimation. The algorithm produces a contour code which consists of estimates of position, brightness, contrast and blur for each edge point in the image. Here we address two questions: 1. Can scale adaptation be used to achieve precise localization of blurred edges? 2. How much of the perceptual content of an image is carried by the 1-D contour code? We report an efficient algorithm for subpixel localization, and show that local scale control allows excellent precision even for highly blurred edges. We further show how local scale control can quantitatively account for human visual acuity of blurred edge stimuli. To address the question of perceptual content, we report an algorithm for inverting the contour code to reconstruct an estimate of the original image. While reconstruction based on edge brightness and contrast alone introduces significant artifact, restitution of the local blur signal is shown to produce perceptually accurate reconstructions.