Embedding Gestalt Laws in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Good continuations in digital image level lines
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Image Denoising by Statistical Area Thresholding
Journal of Mathematical Imaging and Vision
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
A Unified Framework for Detecting Groups and Application to Shape Recognition
Journal of Mathematical Imaging and Vision
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
A Statistical Approach to the Matching of Local Features
SIAM Journal on Imaging Sciences
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We will describe here how the Helmholtz principle, which is a principle of visual perception, can be translated into a computational tool that can be used for many problems of discrete image analysis. The Helmholtz principle can be formulated as "we immediately perceive whatever has a low likelihood of resulting from accidental arrangement". To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. This is a joint work with Lionel Moisan and Jean-Michel Morel.