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
More filters
Journal ArticleDOI
An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring
Kaibo Liu,Yajun Mei,Jianjun Shi +2 more
TL;DR: A monitoring scheme of using the sum of top-r local CUSUM statistics is developed and named as “TRAS” (top-r based adaptive sampling), which is scalable and robust in detecting a wide range of possible mean shifts in all directions, when each data stream follows a univariate normal distribution.
Journal ArticleDOI
Detection and segmentation of moving objects in complex scenes
Aurélie Bugeau,Patrick Pérez +1 more
TL;DR: Experiments and comparisons to other motion detection methods on challenging sequences demonstrate the performance of the proposed method for video analysis in complex scenes.
Journal ArticleDOI
Saliency-Guided Deep Neural Networks for SAR Image Change Detection
TL;DR: A novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image.
Proceedings ArticleDOI
Frugal following: power thrifty object detection and tracking for mobile augmented reality
Kittipat Apicharttrisorn,Xukan Ran,Jiasi Chen,Srikanth V. Krishnamurthy,Amit K. Roy-Chowdhury +4 more
TL;DR: This work develops a novel software framework called MARLIN, which only uses a DNN as needed, to detect new objects or recapture objects that significantly change in appearance, and shows that MARLIN compares favorably in terms of accuracy while saving energy significantly.
Journal ArticleDOI
Change Detection Between SAR Images Using a Pointwise Approach and Graph Theory
TL;DR: Experimental results performed on real SAR images show the effectiveness of the proposed algorithm, in terms of detection performance and computational complexity, compared to classical methods.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
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.
Journal ArticleDOI
Fundamentals of statistical signal processing: estimation theory
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.