Securing the information flow exchanged between these computing .... Dr. DJAMEL BOUCHAFFRA received the Ph.D. degree in Computer Science from Grenoble ... editorial board and is associate editor of several international journals: ...
PATTERN RECOGNITION THE JOURNAL OF THE PATTERN RECOGNITION SOCIETY
Part Special Issue:
Machine Learning and Pattern Recognition Models in Change Detection
Guest Editors DJAMEL BOUCHAFFRA (LEAD) MOHAMED CHERIET PIERRE-MARC JODOIN DIANE BECK
EDITORIAL
Machine Learning and Pattern Recognition Models in Change Detection Change detection can be roughly defined as the awareness of change within an environment. The ability to detect change is vital in much of our everyday life—for example, noticing an activity change in a heartbeat pulse rate, in a brain EEG, in a vibration part of an electromechanical system, or simply in a highway lane during driving. However, one needs to bear in mind that change detection has a great value because it generates a state of interest — something is happening. Even if change detection is ubiquitous, it still remains a difficult enterprise. In fact, behavioral research suggests that human beings are very poor at detecting change, at least under certain circumstances. In particular, when attention is directed elsewhere a normally obvious change can often go undetected. This failure to detect change can have serious consequences, especially in such circumstances as driving, air traffic control, and medical diagnosis. Thus, machine learning techniques for detection of change can provide invaluable assistance to many human endeavors. Perceiving a change, stating what the change is and pinpointing it (where is it located?) are three activities embedded in the change detection phase. Change detection takes also an essential part of image or video analysis when applied to diverse applications, including remote sensing (e.g., evaluating changes in a forest ecosystems over a long period of time), surveillance (e.g., detecting an abandon objects or a moving object whose behavior deviates from what is normally observed), and medical diagnosis (e.g., inspecting signals from ECG or functional MR images). Detecting changes in a continuous speech or a handwritten script reveal vital discriminative clues that significantly enhance recognition and identification. Our world has never been as highly connected as it is today; cloud computing is emerging as a necessary pathway to information management. Electronic devices such as desktops, smartphones, notebooks, or personal digital assistants and tablets have become necessary and interchangeable means to run human affairs. Securing the information flow exchanged between these computing systems has been a challenge in the past years. Change detection in the information flow contributes significantly to live up to this challenge. However, there are many other scientific areas in which change tracking success is a tremendous achievement. For example, recently epigenetic changes have been proven to be linked to the development and progression of disease such as psychiatric disorders. Detecting these changes will have a profound impact in the prevalence of these diseases. Furthermore, the apprehension of changes in some geographical and physical features allows for a better preparation against natural disasters such as earthquakes and tsunamis. It is just lately that the numerous mathematical paradigms and formalisms attempted to model changes have found an area of agreement. In fact, these techniques started to converge just a while ago because of our deeper understanding of this process. Statistical pattern recognition techniques bring novel and powerful means to address change detection. These techniques allow us to gain insight into the complexity of change detection; their limitations and powers will be thoroughly scrutinized. Time series analysis and identification, model selection, statistical hypothesis testing, statistical
approximate inference such as variational Bayesian methods, density estimation techniques, Bayesian networks and graphical models represent invaluable tools to process sequential data and decide on the change detectability. In fact, time series analysis and identification is one of several investigations that will benefit change detection. The main hypotheses relating to these investigations are that the latent parameters characterizing the data may not be subject to changes or are slowly time-varying. Moreover, many practical problems such as change detection in visual cortex for scene perception, quality control, recognition-oriented signal processing, fault detection and monitoring in industrial plants, can be modeled via statistical models whose parameters are liable to abrupt changes at any unknown point in time. The term abrupt change refers to changes in properties that occur very fast with respect to the measurements sampling period. For example, in the case of brain-machine interface, brain stimuli are expected to be heterogeneous, at the same time the device (e.g., wheel chair used by paraplegic) actuation process should be very fast. The mission of this special issue is twofold: (i) It promotes formalisms that exploit machine learning and pattern recognition state-of-the-art models to detect changes, and (ii) It emphasizes applications and contexts in which change detection is deemed necessary. This special issue offers a comprehensive, consolidated, and timely state-of-the-art perspective of the areas that could be of high interest to researchers, practitioners and students. The response to the call for papers was very keen; we have received very high quality submissions (around 20) even if the topic is challenging. The guest editors have finally accepted 9 papers based on a thorough and a comprehensive review process. Because change detection is our inherently omnipresent in our daily life, the spectrum covered in this special issue conveys both theoretical and practical issues. It covers newborn cardiology, ambient-assisted living, wrinkles detection using image morphology and geometric constraints, local image descriptors, polarimetric SAR data change detection in remote sensing, detection of dynamic changes on cyclic time series, video surveillance, video projection and 3D reconstruction. With a bunch of submissions, many established scientists in this field have been involved in the review process mission. We were fortunate to have them aboard and constitute a task force of experts. They have provided detailed, thoughtful, and timely reviews that leaped the quality of accepted papers. Each manuscript has been assigned at least two reviewers. We begin this special issue with the paper titled “Principles of Time-Frequency Feature Extraction for Change Detection: Application to Newborn EEG Abnormality Detection” by Boashash et al. It highlights the importance of detecting changes in newborn EEG signals that can be life-threatening or, enduring life with a major disability. It presents a methodology for identifying such changes by taking into account the non-stationary characteristic of such signals. The approach to change detection involves the extraction and selection of features observed in the (t; f) domain, in which a method was devised based on a new family of (t; f) features. The paper “A Context Aware Approach for Long-Term Behavioral Change Detection and Abnormality Prediction in Ambient Assisted Living” by Forkan et al. introduces a context-aware change detection model using statistical models and machine learning for early symptom detection of chronic illness. The system being developed is able to recognize anomalies in present and future behaviors using pattern recognition formalisms (HMMs) and advanced statistical learning models based on long-term context histories. Finally a fuzzy rule-based model which combines
anomalies of all domains is devised to make decision if true anomaly and to whom context-aware actions to be sent. The paper “Fast Detection of Facial Wrinkles based on Gabor Features using Image Morphology and Geometric Constraints” by Batool et al. proposes a computationally efficient method based on Gabor filters and image morphology to improve wrinkle localization, which are relevant indicators during the human skin aging process. In fact, facial wrinkle are 3D features of skin and appear as subtle discontinuities or cracks in surrounding skin texture. The core concept of their analysis is to detect curvilinear discontinuity/crack features in skin texture caused by wrinkles using Gabor filters banks, and then image morphology to incorporate geometric constraints to locate curvilinear wrinkles‟ shapes at image sites with large Gabor filter response. The paper “EWMA Model-based Shift Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments” by Raza et al. presents novel methods for covariate shiftdetection tests based on a two-stage structure for both univariate and multivariate time-series. The first step utilizes an exponentially weighted moving average (EWMA) model based control chart to detect the covariate shift-point in non-stationary time-series. The second step evaluates validates the shift-detected by first stage through Kolmogorov-Smirnov statistical hypothesis test (K-S test) for univariate time-series and Hotelling's T-Squared multivariate statistical hypothesis test for multivariate time-series. The performance analysis of these methods is conducted through experiments using several synthetic and real-world datasets. Results indicate that all the covariate shifts are detected with a decrease of the false-alarms rate compared to other existing methods.
The paper “MDGHM-SURF: A Robust Local Image Descriptor based on Modified Discrete Gaussian-Hermite Moment” by Kang et al. introduces a novel family of local feature descriptors, a variant of the speed up robust features (SURF) descriptor with a higher performance. The family of descriptors, called MDGHM-SURF, is grounded on the modified discrete Gaussian-Hermite moment (MDGHM), which builds a movable mask to encode the local feature information of non-square images. MDGHM-SURF utilizes MDGHM, which conveys more feature information than first-order derivative-based local descriptors such as SURF and SIFT. MDGHM-SURF is therefore capable of extracting more distinctive features than conventional SURF. The evaluation of the experimental results conducted on six types of deformations demonstrates the outperformance of the proposed methodology when compared to other variants of SURF algorithms. The paper “A New Patch-based Change Detector for Polarimetric SAR Data” by Liu et al. proposes a patch-based change detection method for polarimetric synthetic aperture radar (PolSAR) data. It is well established that the existing PolSAR change detection methods have difficulty handling strong speckle interference cases due to the absence of despeckling functions. In order to address this problem, an improved change detection framework for PolSAR data is developed. This approach is threefold: 1) A despeckling procedure is introduced to increase robustness to noise; 2) A new equivalent number of looks (ENL) estimator is developed for the PolSAR/SAR data filtered by non-local means filter; 3) The proposed method which detects changes based on filtered data can be adjusted with different CFARs. The experiments on both
synthetic and real PolSAR datasets have demonstrated the effectiveness of the proposed patch based PolSAR change detector (PPCD). The paper “A Pattern Recognition Framework for Detecting Dynamic Changes on Cyclic Time Series” by Gharehbaghi et al. addresses what could be seen as a direct application of change detection, i.e. given a 1D periodic signal, detect any event whose behavior deviates from the one of a normal signal. The authors show that given a cardiac signal, one can detect changes with spectral features and machine learning. In their case, they use SVM to distinguish between normal and abnormal signals. The paper “Combining Where and What in Change Detection for Unsupervised Foreground Learning in Surveillance” by Huerta et al. puts forward a machine learning method for solving the problem of moving objects detection in a surveillance video. With the proliferation of surveillance cameras in public areas as well as the ever-growing processing power and storage capacities of servers, detecting real moving objects while being robust to background motion and obstructing objects , camera jitter (high frequency change) and illumination changes is becoming a compelling issue. In this contribution, the authors train several foreground models and show how they can be adapted to fit different scenarios. As for Drouin et al.‟s contribution titled “High Speed Transition for Video-Projection, 3D Reconstruction and Copyright Protection”, a new application to video change detection is proposed. Instead of trying to detect moving objects as is traditionally conducted, they use change detection in the context of a camera-projector system. The mission of the proposed system is threefold: It consists of: i) projecting a series of images, like any other projector (typically videos, PowerPoint presentations, family pictures, etc.) while at the same time, ii) reconstructing the scene in 3D, and iii) enforcing copyright protection. This action is performed by incorporating high frequency binary patterns in the projected images. While these patterns cannot be seen by a human eye, they interfere with the 30Hz acquisition rate of most handheld cameras and thus producing disturbing visual artifacts called "void pantographs". Meanwhile, synchronized high-resolution cameras film the projected images and recover the binary patterns with a change detection method. This allows for pixel-to-pixel camera-projector matching rebuilding the scene in 3D. On the whole, the studies compiled in this special issue offer a sound coverage of this widespread topic whose ultimate mission consists of exploiting machine learning and pattern recognition algorithms for change detection. We, guest editors sincerely hope that this special issue will raise readers „awareness of the importance of change detection in our everyday life. We would like to thank all those who contributed to make this special issue possible, especially the authors. We are always very grateful to those who participated to the review process. Our thanks go also to Mrs. Jacqueline Zhu, Mr. Oliver Pyne, Miss Sophie Herbert for their support in managing the issue. Finally, we extend a heartfelt gratitude to Prof. Ching Y. Suen, Editor-In-Chief of the Pattern Recognition Journal for giving us the opportunity to edit this special issue.
About the Author
Dr. DJAMEL BOUCHAFFRA received the Ph.D. degree in Computer Science from Grenoble University, France. He currently holds the title of Director of Research. In 2012, he joined the Center for Development of Advanced Technology, and in January 2013 he was appointed Head of the Division "Design and Implementation of Intelligent Machines" (former “Systems Architecture and Multimedia”). Prior to this appointment, Dr. Bouchaffra was a Professor of Computer Science at the Department of Mathematics and Computer Science, Grambling State University, LA. He was a Senior Lead Researcher at the Center of Excellence for Document Analysis and Recognition (CEDAR) at the University of New York, Buffalo. Prior to this appointment, Dr. Bouchaffra was an Assistant Professor at Oakland University, Michigan. He is currently working on mathematical models that embed discrete structures into a Euclidean space or a Riemannian manifold and merge topology with statistics for a classification or a regression task. He introduced the structural and the topological hidden Markov models. He has written many papers in peer-reviewed conference proceedings and premier journals, such as the IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transaction on Neural Networks and Learning Systems and Pattern Recognition. His current research interests include pattern recognition, machine learning, computer vision, and artificial intelligence. Dr. Bouchaffra was the lead Guest Editor of a special issue in the journal of Pattern Recognition titled “Feature Extraction and Machine Learning for Robust Multimodal Biometrics", published by Elsevier. He is an Editorial Board Member of several journals, such as Pattern Recognition (Elsevier), and Advances in Artificial Intelligence (Hindawi). He chaired several sessions in conferences. He is on the review panels of governmental funding agencies, such as NASA (Galaxy Classification) and EPSRC, U.K. Dr. Bouchaffra is an IEEE senior Member and a member of the IEEE Computer Society.
About the Author Dr. MOHAMED CHERIET received his B.Eng. from USTHB University (Algiers) in 1984 and his M.Sc. and Ph.D. degrees in Computer Science from the University of Pierre et Marie Curie (Paris VI) in 1985 and 1988 respectively. Since 1992, he has been a professor in the Automation Engineering department at the University of Quebec‟s École de Technologie Supérieure (ÉTS), Montreal, and was appointed full professor there in 1998. He co-founded the Laboratory for Imagery, Vision and Artificial Intelligence at ÉTS, and was its director from 2000 to 2006. He also founded the SYNCHROMEDIA Consortium (Multimedia Communication in Telepresence) there, and has been its director since 1998. His interests include image processing and analysis, OCR, mathematical models for image processing, pattern classification models and learning algorithms, as well as perception in computer vision. Dr. Cheriet has published more than 300 technical papers in the field, and has served as chair or co-chair of the following international conferences: VI'1998, VI'2000, IWFHR'2002, ICFHR'2008, and ISSPA‟2012. He currently serves on the editorial board and is associate editor of several international journals: IJPRAI, IJDAR, and Pattern Recognition. He co-authored a book entitled, "Character Recognition Systems: A guide for Students and Practitioners," John Wiley and Sons, Spring 2007. Dr. Cheriet was awarded the Queen Elizabeth II Diamond Jubilee Medal in light of his significant contributions to knowledge improvement in computational intelligence and mathematical modeling for image processing, created by MITCAS to mark the 60 thanniversary of Her Majesty‟s accession to the throne. He holds NSERC Canada Research Chair Tier 1 in Sustainable Smart echo-Cloud. Dr. Cheriet is a senior member of the IEEE and the chapter founder and former chair of IEEE Montreal Computational Intelligent Systems (CIS).
About the Author Dr. PIERRE-MARC JODOIN is a Canadian computer engineer and associate professor at the computer science department of the University of Sherbrooke, Canada. He got his Ph.D with honour in computer vision and video analytics from the Université de Montréal in 2007. His research interests include video analytics and surveillance, image processing, medical imaging, and 3D reconstruction. He currently serves as an associate editor of the IEEE transactions on image processing journal and as an invited editor of the Pattern Recognition-Elsevier and Signal Processing-Elsevier journals. He is also director of the Sherbrooke Research center on smart environments which he co-founded in 2012. He also co-founded in 2010 the Sherbrooke medical image processing service, co-founded in 2011 Imeka.ca, a company specialized in medical imaging, and started the "changedetection.net" initiative in 2011, one of the significant benchmarking efforts in the field of video analytics.
About the author Dr. DIANE BECK received her Ph.D. in Psychology from the University of California, Berkeley. She was a postdoctoral fellow in Cognitive Neuroscience at University College London and then Princeton University. In 2005, she joined the Psychology Department at the University of Illinois, Urbana-Champaign. She is currently an Associate Professor of Psychology, a member of the Neuroscience Program, and the Group Leader of the Cognitive Neuroscience Group at the Beckman Institute for Advanced Science and Technology, at the University of Illinois. Her interests are in understanding the brain processes underlying visual perception and attention, including those involved in human change detection. She has used behavioural methods, functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to study change detection and other visual processes in the human brain, as well as electroencephalography (EEG) and optical imaging to understand what neural processes give rise to visual awareness more generally. In addition she has applied pattern recognition techniques to fMRI data to investigate the visual cortex and its connectivity with other brain regions. Her publications include papers in premier journals and proceedings such as Nature Neuroscience, Proceedings of the National Academy of Sciences, Cerebral Cortex, Psychological Sciences, and Neural Information Processing Systems (NIPS).
