Journal ArticleDOI
Image change detection algorithms: a systematic survey
TLDR
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.Abstract:
Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents 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. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.read more
Citations
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Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images
TL;DR: This thesis addresses the development of methods for novelty detection and one-class classification with few or no labeled information and proposes a method seeking a sparse and low-rank representation of the data mapped in a non-linear feature space.
Proceedings ArticleDOI
Image enhancement and color constancy for a vehicle-mounted change detection system
Marco Tektonidis,David Monnin +1 more
TL;DR: This work has developed an approach for image enhancement and color constancy based on the center/surround Retinex model and the Gray World hypothesis that has been successfully applied to image sequences acquired on outdoor itineraries at different time points.
Proceedings ArticleDOI
Change detection between multi-band images using a robust fusion-based approach
TL;DR: A robust fusion-based strategy to detect changes between two multi-band optical images with different spatial and spectral resolutions, e.g., a multispectral high spatial resolution image and a hyperspectral low spatialresolution image is proposed.
Proceedings ArticleDOI
Background modeling through dictionary learning
TL;DR: This work builds a model of the background based on dictionary learning as a sparse linear combination of patch prototypes learnt from the image stream and updated when necessary to take into account stable variations.
Proceedings: 2nd Annual Symposium on Graduate Research and Scholarly Projects
David M. Eichhorn,Susan Kovar +1 more
TL;DR: Wichita State University graduate student research papers presented at the 2nd Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 28, 2006 as mentioned in this paper.
References
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TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
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TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Journal ArticleDOI
Image registration methods: a survey
Barbara Zitová,Jan Flusser +1 more
TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.