Chernoff-Hoeffding bounds for applications with limited independence
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Extracting Meaningful Curves from Images
Journal of Mathematical Imaging and Vision
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
A Unified Framework for Detecting Groups and Application to Shape Recognition
Journal of Mathematical Imaging and Vision
Image segmentation by a contrario simulation
Pattern Recognition
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Adapted Windows Detection of Moving Objects in Video Scenes
SIAM Journal on Imaging Sciences
A Statistical Approach to the Matching of Local Features
SIAM Journal on Imaging Sciences
An A-Contrario Approach for Subpixel Change Detection in Satellite Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Beyond Independence: An Extension of the A Contrario Decision Procedure
International Journal of Computer Vision
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A parameter free technique for finding changes between images is discussed. The technique uses the Helmholtz principle to find changes in an input image in situations where only one or two images are available to build a background model. The Helmholtz principle locates image regions that are unlikely to occur due to an a priori random image generation model, and it assigns a confidence level to each changed region. All previous algorithms based on the Helmholtz principle assumed that the image was generated using independent and identically distributed (IID) random variables. The IID assumption is replaced with the weaker exchangeable assumption, and a simple exchangeable random variable model based on the hypergeometric distribution is investigated. Furthermore, practical calculation methods are discussed. The calculation methods require new nonasymptotic bounds to the hypergeometric distribution, and a major contribution of this paper is the novel proof of this bound and its relationship to large deviation theory. The calculations also incorporate the fast level set transform tree structure of the image to create shape boundaries of changed regions. The algorithm is applied to the problems of change detection and coherent motion. We also illustrate that the exchangeable random variable model yields more consistent results than an equivalent IID random variable model.