Visual reconstruction
Design of Perimeter Estimators for Digitized Planar Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple connectivity is not locally computable for connected 3D images
Computer Vision, Graphics, and Image Processing
A common framework for image segmentation
International Journal of Computer Vision
An existence theorem and lattice approximations for a variational problem arising in computer vision
Signal processing Part I
Continuous Skeletons from Digitized Images
Journal of the ACM (JACM)
A note on minimal length polygonal approximation to a digitized contour
Communications of the ACM
Problems of computational and information complexity in machine vision and learning
Problems of computational and information complexity in machine vision and learning
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction
Journal of Mathematical Imaging and Vision
A Mumford-Shah model on lattice
Image and Vision Computing
Discrete curvature calculation for fast level set segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Considers the problem of computing the length of a curve from digitized versions of the curve using parallel computation. The authors' aim is to study the inherent parallel computational complexity of this problem as a function of the digitization level. Precise formulations for the digitization, the parallel computation, and notions of local and nonlocal computations are given. It is shown that length cannot be computed locally from digitizations on rectangular tessellations. However, for a random tessellation and appropriate deterministic ones, the authors show that the length of straight line segments can be computed locally. Implications of the authors' results for a method for image segmentation and a number of open problems are discussed.