| | |
Summary: In Workshop on Statistical and Computational Theories of Vision
at International Conference on Computer Vision, Vancouver, July 13, 2001.
A Generative Model of Curve Images
with a Completely-Characterized
Non-Gaussian Joint Distribution
Jonas August Steven W. Zucker
jonas.august@yale.edu steven.zucker@yale.edu
Center for Computational Vision and Control
Departments of Electrical Engineering and Computer Science
Yale University
51 Prospect St., New Haven, CT 06520
Abstract
What is an ideal edge map? Can one construct a probabilistic, generative model of images
of contours that is tractable? Motivated by these questions, we de ne a prior model for
ideal edge maps by assuming that they are generated by Markov processes via an indicator
function. In this theoretical paper we analyze this curve indicator random eld model both
in the single curve and multiple curve cases. In particular, we derive exact, usable expressions
for this generative model's moment generating functional as well as all of its joint cumulants.
We show that this prior is non-Gaussian, and we outline how it can be combined with
|
