Image change detection algorithms: a systematic survey

By Richard J. Radke, Srinivas Andra, Omar Al-Kofahi, B. Roysam. IEEE Transactions on Image Processing, 2005

2,150 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/a72ccbf4ccfa39388abd233344321679cc91d90a

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Citations in the corpus

A Survey of Methods for Time Series Change Point Detection (2016, neutral): “Here, the observation at each time point is the digital encoding of an image [47].”
A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure (2008, methodology): “The choice of this comparison algorithm was carried out as it is very stable and is widely used in the literature [4], and, like the proposed method, is automatic and unsupervised.”
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, background): “Extensive overviews on this topic can be found in [1]–[4].”
Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation (2008, methodology): “Keywords: Change detection; Multispectral imagery; Iteratively reweighted multivariate alteration detection (IR-MAD); Canonical correlation analysis (CCA); Radiometric”
A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images (2012, background): “In the past, many unsupervised approaches have been developed for exploiting the information present in XD [3], [8], [9].”
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, neutral): “First, postclassification comparison, which means separately classifying the two SAR images and then comparing their classification results to achieve the changed and unchanged regions [2], [4].”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, background): “Two closely related problems to background subtraction are change detection [210] and salient motion detection [66].”
DIARETDB 1 diabetic retinopathy database and evaluation protocol (2007, background): “Most automatic systems approach the detection directly using shape, color, and domain knowledge of diabetic retinopathy findings, but the abnormalities can also be found indirectly by detecting changes between two fundus images taken from the same eye in different time moment [11, 17].”
Graph based anomaly detection and description: a survey (2014, background): “In addition, survey and special issue journal articles that address anomaly, event, and change detection include [Chandola et al., 2012, Margineantu et al., 2010, Radke et al., 2005].”
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, background): “A detailed general survey of image change algorithms can be found in [45].”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, neutral): “Accordingly, changes on the earth’s surface can be identified by using image change detection techniques [2].”
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection (2008, background): “intrinsically, very similar problems, the second one could require less effort [1].”
Street-view change detection with deconvolutional networks (2016, background): “Image-based change detection [30] is an important problem in computer vision and robotics, since identifying the areas of change in a scene is the ﬁrst step towards many common tasks.”
A Mixed Markov model for change detection in aerial photos with large time differences (2008, background): “Therefore, ‘simple frame differencing’ [2, 7] techniques cannot be used, since the observed pixel levels may be significantly different even in the unchanged areas.”
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications (2008, background): “I. INTRODUCTION VISUAL surveillance is a very active research area in com-puter vision thanks to the rapidly increasing number of surveillance cameras that leads to a strong demand for automatic processing methods for their output.”
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering (2012, background): “I MAGE change detection is a process that analyzes images of the same scene taken at different times in order to identify changes that may have occurred between the considered acquisition dates [1].”