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international conference on Information Systems and Computer Networks (ISCON)

作者： Shradha Bhatia Tushar Chauhan Sumita Gupta Sapna Gambhir Jitesh H. Panchal Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida Department of Computer Engineering J. C Bose University of Engineering & Technology Faridabad Department of Mechanical Engineering Purdue University

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches’ heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.

关键词： Deep learning Computational modeling Wearable computers Time series analysis Feature extraction pattern recognition Convolutional neural networks

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A new visualization method for virtual colonoscopy 4th

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4th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001

作者： Vos, F.M. Serlie, I. Van Gelder, R.E. Post, F.H. Truyen, R. Gerritsen, F.A. Stoker, J. Vossepoel, A.M. Pattern Recognition Group Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Department of Radiology University Hospital Rotterdam Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands Computer Graphics and CAD/CAM Group Delft University of Technology Zuidplantsoen 4 Delft2628 BJ Netherlands Department of Radiology Academic Medical Centre Amsterdam P.O. Box 22700 Amsterdam1100 DE Netherlands EasyVision Advanced Development Group Philips Medical Systems Nederland B.V. P.O. Box 10000 Best5680 DA Netherlands

ISBN: (纸本)3540426973

Virtual colonoscopy or ‘colonography’ is a patient-friendly, modern screening technique for polyps. Automatic detection of polyps can serve to assist the radiologist. this paper presents a method based on clustering the principal curvatures. Via automatic polyp detection 5/6 polyps (>5 mm) were detected at the expense of 9 false positive findings per case. For visualization, the bowel surface is presented to the physician in a ‘panoramic’ way as a sequence of unfolded cubes. Conventionally, only 93% of the colon surface is available for examination. In our approach the area in view is increased to 99.8%. the unfolded cube visualization is another step to optimize polyp detection by visual examination. Experiments show a sensitivity of 10/10 (on a per patient basis) for any polyp. the specificity was 7/10. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Visualization

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Inference in rule-based systems by interpolation and extrapolation revisited 28th

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28th international conference on Current Trends in theory and Practice of Informatics, SOFSEM 2001

作者： Jenei, Sándor Institute of Mathematics and Informatics University of Pécs Ifjúság u. 6 PécsH–7624 Hungary

ISBN: (纸本)9783540429128

We deal with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. the methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. For this purpose the well-known fuzzy inference mechanisms have to be extended or replaced by more general ones. the methods proposed so far in the literature for rule interpolation are mainly conceived for the application to fuzzy control and miss certain logical characteristics of an inference. this serves as a motivation for looking for a more flexible method that is superior to the proposed ones with respect to its general applicability to fuzzy as well as to possibilistic systems. First, a set of axioms is proposed. With this, a definition is given for the notion of interpolation, extrapolation, linear interpolation and linear extrapolation of fuzzy rules. the axioms include all the conditions that have been of interest in the previous attempts and others which either have logical characteristics or try to capture the linearity of the interpolation. A new method for linear interpolation and extrapolation of compact fuzzy quantities of the real line is suggested and analyzed in the spirit of the given definition. the method is extended to non-linear interpolation and extrapolation. Finally, the method is extended to the general case, where the input space is n-dimensional, by using the concept of aggregation operators. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Fuzzy control

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Real-Time thermal Face Identification System for Low Memory Vision Applications Using CNN 25th

Real-Time Thermal Face Identification System for Low Memory ...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Devaram, Rami Reddy Ortis, Alessandro Battiato, Sebastiano Bruna, Arcangelo R. Tomaselli, Valeria Department of Mathematics and Computer Science University of Catania Viale A. Doria 6 Catania95125 Italy STMicroelectronics Catania95121 Italy

ISBN: (纸本)9783030688202

Image based face identification systems have attained optimal performance. However, the design of such systems often involves some issues related to extreme light conditions and privacy protection, among others. Since several years, Face Identification (FI) based on thermal images using deep neural networks (DNN) has received significant attention. Yet, the majority of the FI systems developed through DNN’s need huge computational power;those systems are not suitable for the devices with memory limitations. In this paper, we proposed a new CNN framework based on depthwise separable convolutions for real-time face identification for low memory vision applications. the lack of publicly available thermal datasets makes very hard the research and developing of new techniques. In this work, we further present a new large-scale thermal face database called "ST_UNICT_thermal_Face". As per our analysis, the evaluation of the learnt model using the data obtained in the single-day (without temporal variations), it might not stable over time. One of the main reasons behind the development of this database for the real-time evaluation of the proposed model depends on the fact that most thermal face identification systems are not stable over time and climate due to insufficient time data. the evaluation results exhibit that the proposed framework is suitable for the devices having limited memory and which is stable over time and different indoor environmental conditions. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Quantification of Epileptic Electrical Sustained Activity during Sleep in BECT Patients

Quantification of Epileptic Electrical Sustained Activity du...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Guohan Mei Zhezhao Zhang Dinghan Hu Danping Wang Tiejia Jiang Tao Jiang Junfeng Zhang Jiuwen Cao Machine Learning and I-Health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang China Plateforme d’Etude de la Sensorimotricité (PES) BioMedTech Facilities Université Paris Cité Paris France Department of Neurology Children’s Hospital Zhejiang University School of Medicine China Department of Neurosurgery The First Affiliated Hospital of Anhui Medical University China Department of Neurosurgery Anhui Public Health Clinical Center China School of Information and Communication Engineer Hainan University China

Electrical status epilepticus during sleep (ESES) is a specific EEG phenomenon induced by sleep with near-constant spike and slow wave emission. In the clinical diagnosis of its associated syndromes, the quantification of abnormal electroencephalography (EEG) during sleep, i.e. spike-wave index (SWI), is often used as an important reference standard. It is based on the characteristics of spikes and slow-spikes in the central temporal region. EEG signals can identify the presence of seizures for patients and provide information about the severity and extent of abnormal EEG activity during sleep, which helps doctors to better understand the patient’s physiological function and to develop individualised treatment and prognosis plans. In this paper, we proposed a novel method that combines deep learning and morphological operations to identify and quantify epileptic electrical sustained activity during sleep. the proposed method provides the mean SWI error of 6.04%, the Recall of 87.37% and the Precision of 56.11%. Besides, 15.2%, 64.6% and 85.3% could be achieved for PCT (1%), PCT (5%) and PCT (10%), respectively. the experimental results show that the proposed method has great potential for the clinical diagnosis and prognosis of children with epilepsy. It will help to provide long-range EEG detection for patients with ESES syndrome, thus offering the possibility of early treatment for patients.

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HAWAII international conference ON SYSTEM SCIENCES, 6th, PROCEEDINGS, 1973.

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1973年

作者： Fine, Terrence L. Ruspini, Enrique H. Butler, G.A. Ritea, H.B. Michalopoulos, Demetrios A. Lopez-Toledo, Alejandro A. Jain, Anil K. Chandrasekaran, B. Gose, Earl E. Wu, Francis B.H.

Following is a continuation of the list of titles and authors: Approach to pattern Classification When Little Is Known. By Terrence L. Fine. Fast method for probabilistic and Fuzzy Cluster Analysis Using Association Measures. By Enrique H. Ruspini. Estimation of the Information in a Two Class Sample. By G. A. Butler and H. B. Ritea. Non-Parametric Method with Applications to pattern recognition and Mode Estimation. By Demetrios A. Michalopoulos. On the Estimation of the Inverse of a Covariance Matrix. By Alejandro A. Lopez-Toledo. Conditions for Perfect Discrimination for Independent Measurements. By Anil K. Jain and B. Chandrasekaran. Some Second-Order Techniques for Selecting Subsets of pattern recognition Properties. By Earl E. Gose and Francis B. H. Wu.

关键词： Systems science and cybernetics

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Real time eye tracking using initial centroid and gradient analysis technique

Real time eye tracking using initial centroid and gradient a...

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international conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Sunu Wibirama Supan Tungjitkusolmun Chuchart Pintavirooj Kazuhiko Hamamoto Department of Electronics Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Information Media Technology School of Information and Telecommunication Engineering Tokai University Tokyo Japan

Robust and accurate algorithm in real time eye tracking system has been a fundamental and challenging problem for computer vision and biomedical engineering area. this paper proposes a new method to estimate eye-motion position and direction based on initial centroid and gradient analysis technique. the proposed method was validated by tracking eye position within high and low occlusion conditions. Experimental results show that the proposed method improves the accuracy of tracking pupil position compared to conventional window-matching method. the proposed method yields 76.07% and 87,89% accuracies while the conventional window-matching performs 44.56% and 67.89% accuracies within high and low occlusion conditions, respectively.

关键词： Tracking Real time systems Biomedical engineering Computer vision Motion detection Motion estimation Cameras Algorithm design and analysis pattern recognition Head

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A framework for nuclear image enhancement based on the Anscomb transform and the bayesian thresholding

A framework for nuclear image enhancement based on the Ansco...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Imen Mbazaa Najla Gribaa Nawres Khlifa Kamel Hamrouni Research Unit of Signal Processing Image Processing and Pattern Recognition National Engineering School of Sfax (ENIS) Tunisia

Scintigraphic imagery is an important data source since it provides morphological and functional clinical informations. However, scintigraphic images present very bad quality because of several degradation factors. In fact, the recorded data are embedded in noise modelled as the realisation of Poisson process. the aim of this paper is to provide a framework able to enhance nuclear images quality by Poisson intensity estimation. this framework consists, in a first step, of performing variance-stabilizing step for the Poisson process thanks to the Anscombe transformation. So the obtained data can be considered as contaminated by a white Gaussian noise. In a second step, it uses a Bayesian technique inspired of Pizurica approach, known in the literature for exhibiting good results as for white Gaussian noise. In fact, the complex wavelet packets were exploited regarding to Pizurica algorithm.

关键词： Image enhancement Bayesian methods Gaussian noise Noise reduction Wavelet packets Wavelet transforms Image processing Image quality Pixel Cameras

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A Cognitive Data Visualization Method Based on Hyper Surface

A Cognitive Data Visualization Method Based on Hyper Surface

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IEEE international conference on Cognitive Informatics

作者： Qing He Xiurong Zhao Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing China

the understanding of data is highly relevant to how one senses and perceives them. the existing approaches for classification have been developed mainly based on exploring the intrinsic structure of dataset itself less or no emphasis paid on simulating human visual cognition. A new hyper surface classification method (HSC) has been studied since 2002. HSC is a universal classification method, in which a model of hyper surface is obtained by adoptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan curve theorem in topology. In this paper we point out that HSC is a cognitive data visualization method. Simulation results show the effectiveness of the proposed method on large test data with complex distribution and high density. In particular, we show that HSC can very often bring a significant reduction of computation effort without loss of prediction capability.

关键词： Data visualization Humans Cognition Business process re-engineering Helium Topology Classification algorithms Partitioning algorithms pattern recognition Laboratories

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A modular CMOS analog fuzzy controller

A modular CMOS analog fuzzy controller

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： F. Vidal-Verdu R. Navas A. Rodriguez-Vazquez Dto. de Electrónica Universidad de Málaga Malaga Spain Department of Analog and Mixed-Signal Circuit Design Centro Nacional de Microelectróica Universidad de Sevilla Seville Spain

the low/medium precision required for many fuzzy applications makes analog circuits natural candidates to design fuzzy chips with optimum speed/power figures. this paper presents a sixteen rules-two inputs analog fuzzy controller in a CMOS 1 /spl mu/m single-poly technology based on building blocks implementations previously proposed by the authors (1995). However, such building blocks are rearranged here to get a highly modular architecture organized from two high level blocks: the label block and the rule block. In addition, sharing of membership function circuits allows a compact design with low area and power consumption and its highly modular architecture will permit to increase the number of inputs and rules in future chips with hardly design effort. the paper includes measurements from a silicon prototype of the controller.

关键词： Fuzzy control Fuzzy logic CMOS technology Prototypes Fuzzy systems Analog circuits Energy consumption Semiconductor device measurement Associative memory pattern recognition

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