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Occupancy driven building performance assessment

Journal of Innovation in Digital Ecosystems 2016年第2期3卷 57-69页

作者： Dimosthenis Ioannidis Pantelis Tropios Stelios Krinidis George Stavropoulos Dimitrios Tzovaras Spiridon Likothanasis Information Technologies Institute Centre for Research and Technology Hellas 6th Km Charilaou-Thermi Thermi-Thessaloniki 57001 Greece Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Rio Patras Greece

In this paper, we focus on the building performance assessment using big data and visual analytics techniques driven by building occupancy. Building occupancy is a paramount factor in building performance, specifically lighting, plug loads and HVAC equipment utilization. Extrapolation of patterns from big data sets, which consist of building information, energy consumption, environmental measurements and namely occupancy information, is a powerful analysis technique to extract useful semantic information about building performance. To this end, visual analytics techniques are exploited to visualize them in a compact and comprehensive way taking into account properties of human cognition, perception and sense making. Visual Analytics facilitates the detailed spatiotemporal analysis building performance in terms of occupancy comfort, building performance and energy consumption and exploits innovative data mining techniques and mechanisms to allow analysts to detect patterns and crucial point that are difficult to be detected otherwise, thus assisting them to further optimize the building’s operation. The presented tool has been tested on real data information acquired from a building located at southern Europe demonstrating its effectiveness and its usability for building managers.

关键词： Building performance assessment Big data analytics Visual analytics Human presence Occupancy extraction Building occupancy visualization

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2016年第3期1卷 230-244页

作者： Calhoun, Vince D. Sui, Jing Mind Research Network Lovelace Biomedical and Environmental Research Institute Albuquerque NM United States Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM United States Brainnetome Center National Laboratory of Pattern Recognition Beijing China Chinese Academy of Sciences Center for Excellence in Brain Science Institute of Automation Beijing China

It is becoming increasingly clear that combining multimodal brain imaging data provides more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e., capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multimodal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this article, we start by introducing the basic reasons why multimodal data fusion is important and what it can do and, importantly, how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multimodal fusion including deep learning and multimodal classification that show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential to mitigate misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness. © 2016 Society of Biological Psychiatry.

关键词： Brain function Connectivity Data fusion Independent component analysis Psychosis Schizophrenia

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Face Alignment Across Large Poses: A 3D Solution

Face Alignment Across Large Poses: A 3D Solution

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IEEE Conference on computer Vision and pattern recognition

作者： Xiangyu Zhu Zhen Lei Xiaoming Liu Hailin Shi Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Science and Engineering Michigan State University

ISBN: (纸本)9781467388528

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： face modeling Face Posing Face landmarks Imagery (Psychotherapy) images Alignment

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Analog Non-Linear Transformation-Based Tone Mapping for Image Enhancement in C-arm CT

Analog Non-Linear Transformation-Based Tone Mapping for Imag...

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IEEE Nuclear Science Symposium

作者： Lan Shi Martin Berger Bastian Bier Christopher Soell Juergen Roeber Rebecca Fahrig Bjoern Eskofier Andreas Maier Jennifer Maier Department of Electrical Engineering Friedrich-Alexander-University Erlangen Nuremberg Germany Department of Computer Science Pattern Recognition Lab Siemens Healthcare GmbH Radiological Sciences Laboratory Stanford University CA

Flat-Panel C-arm Computed Tomography (CT) suffers from pixel saturation due to the detector's limited dynamic range. We describe a novel approach of analog, non-linear tone mapping (TM) for preventing detector saturation. An analog TM operator (TMO) applies a non-linear transformation in a CMOS (complementary metal-oxide semiconductor) sensor and its inverse TMO based on 14-bit digital raw data. This is done in order to prevent overexposure and to enhance image quality to 32 bits. The method was applied to the cases of low-contrast head imaging and to that of imaging both knees. Cone-beam projection data with and without overexposure was simulated for a 200 degree short-scan of the knees and a 360 degree full-scan of a Forbild head phantom. The results show an increased correlation coefficient with respect to ground truth of 0.99 for TMO compared to 0.96 for overexposed knee data and a higher low-contrast visibility (CC=0.99) compared to linear quantization (CC=0.97).

关键词： Computed tomography Detectors Knee Image reconstruction Head Phantoms

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Image-based characterization of the pulp flows

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pattern recognition and Image Analysis 2016年第3期26卷 630-637页

作者： Sorokin, M. Strokina, N. Eerola, T. Lensu, L. Karttunen, K. Kalviainen, H. Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Computer Vision Group Department of Signal Processing Tampere University of Technology PO Box 527 TampereFI-33101 Finland Cemis-Oulu Unit of Measurement Technology Kajaani University Consortium University of Oulu PO Box 127 KajaaniFI-87400 Finland School of Information Technology Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor Darul Ehsan46150 Malaysia

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions. © 2016, Pleiades Publishing, Ltd.

关键词： pattern matching

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Occupant-aware indoor monitoring for enhanced building analysis

Occupant-aware indoor monitoring for enhanced building analy...

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2015 Symposium on Simulation for Architecture and Urban Design, SimAUD 2015, Part of the 2015 Spring Simulation Multi-Conference, SpringSim 2015

作者： Ioannidis, Dimosthenis Krinidis, Stelios Drosou, Anastasius Tzovaras, Dimitrios Likothanassis, Spiridon Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Rio Patras Greece Information Technologies Institute Centre for Research and Technology Hellas Thermi-Thessaloniki Greece

ISBN: (纸本)9781510810600

In this paper a novel, cost-effective and robust occupancy monitoring system is presented, which is based on a fuzzy confidence voting algorithm utilizing spatial height histograms. Spatial height histograms are invariant to rotations and translations, providing this way a desirable feature to occupancy measurement systems, and when combined with distance coefficients can fix an occupancy feature vector, which is the main source for the fuzzy confidence voting occupant tracking algorithm. The proposed occupancy extraction system can be efficiently applied to multi-space environments using a privacy preserving multi-camera cloud. Statistics per building, space and occupant can be finally extracted by the system. The experimental results will illustrate its robustness, accuracy and efficiency on occupancy extraction. © 2015 Society for Modeling & Simulation International (SCS).

关键词： Extraction

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Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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LSSLP – Local structure sensitive label propagation

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Information Sciences 2016年 332卷 19-32页

作者： Zhenfeng Zhu Jian Cheng Yao Zhao Jieping Ye Institute of Information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) 100190 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2218 USA

Label propagation is an approach to iteratively spread the prior state of label confidence associated with each of samples to its neighbors until achieving a global convergence state. Such process has been shown to hold close connection with a general graph-based regularization framework. Within this framework, a closed- form linear system can be built to carry out label propagation. In this paper, to address several issues inherent with previous graph-based label propagation framework, we propose a reformulated one, i.e., local structure sensitive label propagation ( LSSLP ). By associating each graph vertex with a local structure sensitive tuning factor, the empirical loss error on each vertex can be controlled preferably to keep consistent with the commonly preconditioned ‘ cluster assumption ’ of data structure. Out of consideration for information conservation, we relax the state conservation constraint of label confidence from labeled samples proposed by Belkin et al. (2004) to a more general form. Meanwhile, an inverse-warping procedure is incorporated into the proposed local structure sensitive label propagation framework to maintain large and stable enough classification margin. Based on the felicitous inversion technique for blocked matrix, we extend LSSLP to its incremental and inductive versions and also present computationally efficient implementation of it. Experimental results demonstrate the performance of the reformulated regularization framework for label propagation is much competitive.

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Online visual tracking using temporally coherent part cluster 15

Online visual tracking using temporally coherent part cluste...

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2015 15th IEEE Winter Conference on Applications of computer Vision, WACV 2015

作者： Li, Wenbo Wen, Longyin Chuah, Mooi Choo Zhang, Yi Lei, Zhen Li, Stan Z. Department of Computer Science and Engineering Lehigh University United States National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China School of Computer Software Tianjin University China

ISBN: (纸本)9781479966820

Recent advances in visual tracking have focused on handling deformations and occlusions using the part-based appearance model. However, it remains a challenge to come up with a reliable target representation using local parts, and hence existing trackers continue to face drifting problems. To deal with this challenge, we propose a robust online model, formulating the tracking task as a problem of identifying Temporally Coherent Part (TCP) clusters. Specifically, we pose the TCP clusters identification task as a dense neighborhoods searching problem using a relational hyper graph in which the relationship among multiple temporal local parts is encoded as the affinity value of a hyper edge connecting them. Such high-order relations ships among multiple local parts across the temporal domain make our tracker more robust towards deformations and occlusions. Extensive experiments on various challenging video sequences demonstrate that our TCP-based method performs better than the state-of-the-art methods. © 2015 IEEE.

关键词： Deformation

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