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6th international conference on Image processing theory, Tools and Applications (IPTA)

作者： Li, Lei Feng, Xiaoyi Boulkenafet, Zinelabidine Xia, Zhaoqiang Li, Mingming Hadid, Abdenour Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China Univ Oulu Ctr Machine Vis Res CMV SF-90100 Oulu Finland

ISBN: (纸本)9781467389105

Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. the experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods.

关键词： face anti-spoofing deep part features convolutional neural network block PCA

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Resource-Efficient Latent Fingerprint Age Estimation for Adhoc Crime Scene Forensics: Quality Assessment of Flat Bed Scans and Statistical Features 6

Resource-Efficient Latent Fingerprint Age Estimation for Adh...

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6th international conference on Image processing theory, Tools and Applications (IPTA)

作者： Merkel, Ronny Dittmann, Jana Vielhauer, Claus OvG Univ Magdeburg Dept Comp Sci Res Grp Multimedia & Secur D-39106 Magdeburg Germany

ISBN: (纸本)9781467389105

In the field of biometrics and forensics, age estimation is an important topic, for example when determining the age of human subjects or when assessing the age of a particular taken biometric sample or captured forensic trace from crime scenes. the latter case is investigated in this paper, with the focus on latent fingerprints. Here, the trace age represents the time between deposition and capture of a print, which might assist an investigation in several respects, such as forgery detection (trace freshness inconsistencies) or strengthening a prints evidentiary value in court (linking a suspect to the time of a crime). Noninvasive, high-resolution and very cost-intensive capturing devices have recently been proposed to address this 80 year old challenge of latent print age estimation by studying print time series. this approach is transferred here to a (cheap) off-the-shelf flat bed scanner, enabling a broad variety of researchers to study print aging in the future. Based on 700 time series (including 10% empty substrate series for comparison), a separation performance of 89.2% (eccrine) and 98.7% (sebaceous) was achieved for separating fresh prints and those aged for at least half a day. Results include a comparison to prior used high-resolution Chromatic White Light (CWL) sensors and Confocal Laser Scanning Microscopes (CLSM), also showing the possibility of significant preprocessing simplifications due to decreased capturing distortions as well as the need for further (quantitative) studies.

关键词： Latent Fingerprints Crime Scene Forensics Age Estimation Flat Bed Scanner machine learning

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Detecting Arabic Spammers and Content Polluters on Twitter 6

Detecting Arabic Spammers and Content Polluters on Twitter

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6th international conference on Digital Information processing and Communications (ICDIPC)

作者： El-Mawass, Nour Alaboodi, Saad King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781467375047

Spam is thriving on Arabic Twitter. With a large online population, a mounting political unrest, and an undersized and unspecialized response effort, the current state of Arabic online social networks (OSNs) offers a perfect target for the spam industry, bringing both abuse and manipulation to the scene. the result is a ubiquitous spam presence that redefines the signal to noise ratio, and makes spam a de facto component of the online social platforms. English spam on online social networks has been heavily studied in the literature. To date however, social spam in other languages has been largely ignored. Our own analysis of spam content on Arabic trending hash tags in Saudi Arabia results in an estimate of about three quarters of the total generated content. this alarming rate, backed by independent concurrent estimates, makes the development of adaptive spam detection techniques a very real and pressing need. In this study, we present a first attempt at detecting accounts that promote spam and content pollution on Arabic Twitter. Using a large crawled dataset of more than 23 million Arabic tweets, and a manually labeled sample of more than 5000 tweets, we analyze the spam content on Saudi Twitter, and assess the performance of previous spam detection features on our recently gathered dataset. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new highly accurate data-driven detection system.

关键词： Online Social Networks Social Spam Detection machine learning Supervised Classification Twitter Arabic Spam

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Anomaly Detection in Radiation signals Using Kernel machine Intelligence 6

Anomaly Detection in Radiation Signals Using Kernel Machine ...

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6th international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Alamaniotis, Miltiadis Choi, Chan K. Tsoukalas, Lefteri H. Purdue Univ Sch Nucl Engn Appl Intelligent Syst Lab W Lafayette IN 47907 USA

ISBN: (纸本)9781467393119

Acquisition of consecutive ultra-short interval measurements with a radiation detector serves in detecting anomalies in the ambient environment, potentially caused by hidden radioactive material. An anomaly is defined as the contribution of a man-made radiation source to the measured signal. In this paper a new methodology is presented and tested on detecting anomalies in various radiation signals. the methodology employs machine intelligence tools to perform automated detection of malignant source contribution. In particular, a Gaussian process (GP) modeled as a function of a learning kernel is used for post-processing of measured signals. For detection purposes, a window that slides over the measured signal is divided into three parts, where the two parts are used for training the GP that is subsequently used for predicting the values of the third part. Next, a comparison between predicted and measured values in the third part is conducted by computing the mean average percentage error (MAPE). If MAPE is relatively high, then the likelihood of an anomaly in the part under examination is respectively high. Adoption of a sliding window ensures that no part of signal will remain unchecked. Testing results obtained on a set of gamma-ray signals exhibit the potency of the presented methodology in effectively detecting anomalies.

关键词： Gaussian processes kernel machines anomaly detection radiation signals

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Augmenting MRI-transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

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international JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2015年第6期10卷 727-735页

作者： Imani, Farhad Zhuang, Bo Tahmasebi, Amir Kwak, Jin Tae Xu, Sheng Agarwal, Harsh Bharat, Shyam Uniyal, Nishant Turkbey, Ismail Baris Choyke, Peter Pinto, Peter Wood, Bradford Moradi, Mehdi Mousavi, Parvin Abolmaesumi, Purang Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Philips Res North Amer Briarcliff Manor NY USA NIH Bethesda MD 20892 USA Queens Univ Sch Comp Kingston ON Canada

In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS)-guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has shown to be highly sensitive to detect higher-grade prostate cancer, but suffers from a high rate of false positives for lower-grade cancer, leading to unnecessary biopsies. this paper utilizes a machine-learning framework to further improve the sensitivity of targeted biopsy through analyzing temporal ultrasound data backscattered from the prostate tissue. Temporal ultrasound data were acquired during targeted fusion prostate biopsy from suspicious cancer foci identified in mp-MRI. Several spectral features, representing the signature of backscattered signal from the tissue, were extracted from the temporal ultrasound data. A supervised support vector machine classification model was trained to relate the features to the result of histopathology analysis of biopsy cores obtained from cancer foci. the model was used to predict the label of biopsy cores for mp-MRI-identified targets in an independent group of subjects. Training of the classier was performed on data obtained from 35 biopsy cores. A fivefold cross-validation strategy was utilized to examine the consistency of the selected features from temporal ultrasound data, where we achieved the classification accuracy and area under receiver operating characteristic curve of 94 % and 0.98, respectively. Subsequently, an independent group of 25 biopsy cores was used for validation of the model, in which mp-MRI had identified suspicious cancer foci. Using the trained model, we predicted the tissue pathology using temporal ultrasound data: 16 out of 17 benign cores, as well as all three higher-grade cancer cores, were correctly identified. the results show that temporal anal

关键词： Temporal ultrasound data Cancer diagnosis Prostate cancer

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Anomaly detection in radiation signals using kernel machine intelligence

Anomaly detection in radiation signals using kernel machine ...

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international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Miltiadis Alamaniotis Chan K. Choi Lefteri H. Tsoukalas Applied Intelligent Systems Laboratory Purdue University West Lafayette IN USA

关键词： Kernel Gaussian processes Predictive models machine intelligence Testing Time measurement Nuclear measurements

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Cardiac Arrhythmia Classification Using Hierarchical Classification Model

Cardiac Arrhythmia Classification Using Hierarchical Classif...

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6th international conference on Computer Science and Information Technology (CSIT)

作者： Ahmed, Rashad Arafat, Samer KFUPM Ctr Commun & Informat Technol Res Dhahran 31261 Saudi Arabia KFUPM Res Inst Dhahran 31261 Saudi Arabia KFUPM Coll Comp Sci & Engn Informat Dhahran 31261 Saudi Arabia KFUPM Dept Comp Sci Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781479939992

the application of machine learning techniques in medicine and biomedicine has shown a rising trend and corresponding promising results. Several machine learning techniques, such as artificial neural networks, reclial basis function networks and support vector machines, are successfully applied to the classification of different types of heart beat arrhythmia. this paper explores the use of a hierarchical model for the classification of cardiac arrhythmia. Furthermore, it investigates the performance of four machine learning techniques for heart beat arrhythmia classification. the benchmark MIT ECG arrhythmia database is used to evaluate the different models. the results indicate that a Tree Boost based supervised model generally achieves the best performance result. A decision tree forest has comparable results to that of Tree Boost and has slightly higher performance, compared to SVM. However, MLP has the lowest performance result. the results also show that the hierarchical model slightly outperforms the conventional one-stage model in terms of accuracy, sensitivity, and specificity.

关键词： decision trees electrocardiography learning (artificial intelligence) medical signal processing multilayer perceptrons signal classification support vector machines MLP SVM TreeBoost based supervised model artificial neural network benchmark MIT ECG arrhythmia database biomedicine cardiac arrhythmia classification decision tree forest heart beat arrhythmia classification hierarchical classification model machine learning radial basis function network support vector machine Accuracy Computational modeling Databases Electrocardiography Feature extraction Support vector machines Vegetation

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Towards Glaucoma Detection Using Intraocular Pressure Monitoring 6

Towards Glaucoma Detection Using Intraocular Pressure Monito...

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6th international conference on Soft Computing and Pattern Recognition (SoCPaR)

作者： Gisler, Christophe Ridi, Antonio Fauquex, Milene Genoud, Dominique Hennebert, Jean Univ Fribourg Dept Informat Blvd Perolles 90 CH-1700 Fribourg Switzerland Univ Appl Sci Western Switzerland Inst Complex Syst CH-1705 Fribourg Switzerland Univ Appl Sci Western Switzerland Inst Informat Gest CH-3960 Sierre Switzerland

ISBN: (纸本)9781479959341

Diagnosing the glaucoma is a very difficult task for healthcare professionals. High intraocular pressure (IOP) remains the main treatable symptom of this degenerative disease which leads to blindness. Nowadays, new types of wearable sensors, such as the contact lens sensor Triggerfish (R), provide an automated recording of 24-hour profile of ocular dimensional changes related to IOP. through several clinical studies, more and more IOP-related profiles have been recorded by those sensors and made available for elaborating data-driven experiments. the objective of such experiments is to analyse and detect IOP pattern differences between ill and healthy subjects. the potential is to provide medical doctors with analysis and detection tools allowing them to better diagnose and treat glaucoma. In this paper we present the methodologies, signal processing and machine learning algorithms elaborated in the task of automated detection of glaucomatous IOP-related profiles within a set of 100 24-hour recordings. As first convincing results, we obtained a classification ROC AUC of 81.5%.

关键词： Glaucoma diagnosis biomedical signal processing machine learning

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An Optimal-Pruned Extreme learning machine based modelling of surface roughness 6

An Optimal-Pruned Extreme Learning Machine based modelling o...

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6th international conference on Information Technology and Multimedia, ICIMU 2014

作者： Janahiraman, Tiagrajah V. Ahmad, Nooraziah Centre for Signal Processing and Control Systems Dept of Electronics and Communication Engineering College of Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia Department of Creative Technology Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Bachok Kelantan Malaysia

ISBN: (纸本)9781479954230

A computer based modelling and prediction method is vital in the field of Computer Numerical Control based cutting operation. the final quality of finished surface is mainly influenced by the interaction between the work piece, cutting tool and machining system. therefore, many researchers attempted to develop an efficient prediction systems for surface roughness before machining. In this paper, Optimal Pruned Extreme learning machine (OPELM) is proposed for modelling and predicting surface roughness with respect to its cutting parameters in turning based machining process. the surface roughness models obtained from other methods such as Response Surface Method, Neural Network and Extreme learning machine were compared with the experimental results. Our experimental study consist of 15 workpieces that were used for cutting using turning operation. the correlation between the input parameters such as feed rate, cutting speed and depth of cut with surface roughness was modelled using OPELM. Based on our study, OPELM performed the best in modelling and predicting based on unknown set of input. © 2014 IEEE.

关键词： Surface roughness

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Gait Recognition Based on Modified Phase Only Correlation

Gait Recognition Based on Modified Phase Only Correlation

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6th international conference on Image and signal processing (ICISP)

作者： Rida, Imad Bouridane, Ahmed Al Kork, Samer Bremond, Francois INSA Rouen LITIS EA 4108 St Etienne France Northumbria Univ Dept Comp Sci & Digital Technol Newcastle England ESPCI Paris Tech Signal Proc & Machine Learning Lab Paris France INRIA Sophia Antipolis STARS team Sophia Antipolis Sophia Antipolis France

ISBN: (纸本)9783319079981;9783319079974

Clothing, carrying conditions, and other intra-class variations, also referred as "covariates", affect the performance of gait recognition systems. this paper proposes a supervised feature extraction method which is able to select relevant features for human recognition to mitigates the impact of covariates and hence improve the recognition performance. the proposed method is evaluated using CASIA Gait Database (Dataset B) and the experimental results suggest that our method yields attractive results.

关键词： Gait phase only correlation feature selection

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