Journal ArticleDOI
New likelihood test methods for change detection in image sequences
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TLDR
Results encouraged investigations into modeling the picture as a mosaic of patches where the gray-value function within each patch is described as a second-order bivariate polynomial of the pixel coordinates, facilitating the determination of threshold values related to a priori confidence limits.Abstract:
Modeling the image as a piecewise linear gray-value function of the pixel coordinates considerably improved a change detection test based previously on a piecewise constant gray-value function. These results encouraged investigations into modeling the picture as a mosaic of patches where the gray-value function within each patch is described as a second-order bivariate polynomial of the pixel coordinates. Such a more appropriate model allowed the assumption to be made that the remaining gray-value variation within each patch can be attributed to noise related to the sensing and digitizing devices, independent of the individual image frames in a sequence. This assumption made it possible to relate the likelihood test for change detection to well-known statistical tests ( t test, F test), facilitating the determination of threshold values related to a priori confidence limits.read more
Citations
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Image change detectio algorithms : A systematic survey
TL;DR: A systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling is presented.
Journal ArticleDOI
Image change detection algorithms: a systematic survey
TL;DR: In this paper, the authors present a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling.
Journal ArticleDOI
Background and foreground modeling using nonparametric kernel density estimation for visual surveillance
TL;DR: This paper constructs a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations.
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Computing occluding and transparent motions
TL;DR: A method is presented for detecting and tracking occluding and transparent moving objects, which uses temporal integration without assuming motion constancy, to improve the segmentation of motion analysis and segmentation.
Proceedings ArticleDOI
Image difference threshold strategies and shadow detection
Paul L. Rosin,Tim Ellis +1 more
TL;DR: Two problems associated with the detection and classification of motion in image sequences obtained from a static camera are considered, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets.
References
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Displacement vectors derived from second-order intensity variations in image sequences
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Ramesh Jain,H.-h. Nagel +1 more
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A facet model for image data
TL;DR: A facet model for image data is discussed which has the potential for fitting the form of the real idealized image, and for describing how the observed image differs from the idealized form.
Journal ArticleDOI
Volumetric model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene
TL;DR: A polyhedral approximation for the volumetric description of a moving rigid object from a real-world scene is derived, based on measurements in monocular TV-frame sequences, and is compared to related ones reported in the recent literature.
Journal ArticleDOI
Edge and region analysis for digital image data
TL;DR: In this article, a unified view of edge and region analysis is provided based on the sloped-facet model which assumes that regions of image segments are maximal areas which are sloped planes.