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The boundless spaces of expanded multimedia

The boundless spaces of expanded multimedia

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International conference on Systems, Signals and image processing, IWSSIP

作者： Aleksandar Todorović University of the Arts Belgrade Serbia Mediterranean University Podgorica Montenegro

Back in 1970 Gene Youngblood published his book “Expanded Cinema”, undoubtedly one of the major milestones in the world of media theory. In the book he argues that during the late 1960 cinema and the whole complex of audiovisual media, reached a tipping point when the boundaries of the world of moving images broke down and a new visual reality emerged. That was the time when the videosphere appeared and the intermedia where born. Today, forty years later, we see another, somewhat similar event. The intermedia, which in the meantime evolved in multimedia, reached another “Seldon crisis point”, and we can witness how the whole complex of multimedia creation, processing, distribution and consumption is undergoing a radical change. That change shall profoundly modify the world of mass media communications. The boundaries that until now separated different mass media are becoming progressively blurred and in a not so distant future they might completely disappear creating thus just one big media industry. In a surrogate social environment lonely media consumers will exchange interaction for communication and try to escape the harsh reality by immersing themselves in infinite virtual worlds of the boundless expanded multimedia.

关键词： Multimedia communication Advertising image coding

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A supervised learning model for live cell segmentation

A supervised learning model for live cell segmentation

引用

ieee Signal processing and communications Applications (SIU)

作者： Can Fahrettin Koyuncu İrem Durmaz Rengül Çetin-Atalay Çiğdem Gündüz-Demir Bilgisayar Mühendisliği Bölümü Bilkent Üniversitesi Ankara Türkiye Moleküler Biyoloji ve Genetik Bölümü Bilkent Üniversitesi Ankara Türkiye

Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds.

关键词： image segmentation Feature extraction Signal processing conferences Microscopy Algorithm design and analysis Supervised learning

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A Bayesian model for brain tumor classification using clinical-based features

A Bayesian model for brain tumor classification using clinic...

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ieee International conference on image processing

作者： Tomás Martínez-Cortés Miguel Ángel Fernández-Torres Amaya Jiménez-Moreno Iván González-Díaz Fernando Díaz-de-María Juan Adán Guzmán-De-Villoria Pilar Fernández Department of Signal Theory and Communications Universidad Carlos III Leganes Madrid Spain Hospital Gregorio Marañón Madrid Spain

This paper tackles the problem of automatic brain tumor classification from Magnetic Resonance Imaging (MRI) where, traditionally, general-purpose texture and shape features extracted from the Region of Interest (tumor) have become the usual parameterization of the problem. Two main contributions are made in this context. First, a novel set of clinical-based features that intend to model intuitions and expert knowledge of physicians is suggested. Second, a system is proposed that is able to fuse multiple individual scores (based on a particular MRI sequence and a pathological indicator present in that sequence) by using a Bayesian model that produces a global system decision. This approximation provides a quite flexible solution able to handle missing data, which becomes a very likely case in a realistic scenario where the number clinical tests varies from one patient to another. Furthermore, the Bayesian model provides extra information concerning the uncertainty of the final decision. Our experimental results prove that the use of clinical-based feature leads to a significant increment of performance in terms of Area Under the Curve (AUC) when compared to a state-of-the art reference. Furthermore, the proposed Bayesian fusion model clearly outperforms other fusion schemes, especially when few diagnostic tests are available.

关键词： Tumors Bayes methods Uncertainty Brain modeling Magnetic resonance imaging Solid modeling

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Bionic Vision Inspired On-Road Obstacle Detection and Tracking using Radar and visual Information

Bionic Vision Inspired On-Road Obstacle Detection and Tracki...

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17th International ieee conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering conference 2014

作者： Xiao Wang Linhai Xu Hongbin Sun Jingmin Xin Nanning Zheng Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University

Inspired by the cooperation between the cone cell and the rod cell in human vision system,this paper presents a novel method for fusing millimeter wave(MMW) radar and monocular vision sensor information for on-road obstacle *** MMW radar performs similarly as the cone cell to indicate potential target region in the view,while the camera plays the same role as that of the rod cell to obtain precise target contour and to allow tracking over *** real-world data collected from on-road vehicles,experimental evaluations have shown that,our proposed system could detect on-road obstacles with high detection rate and low false-alarm rate,yet the overall image processing time is significantly reduced compared to conventional *** addition,the system has been applied on our unmanned vehicle for the past two years' "Future Challenge" autonomous vehicles competition.

关键词： Bionic Vision Inspired On-Road Obstacle Detection and Tracking using Radar and visual Information visual

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Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

Deep convolutional activation features for large scale Brain...

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ieee International conference on Acoustics, Speech and Signal processing

作者： Y. Xu Z. Jia Y. Ai F. Zhang M. Lai E. I. C. Chang Key Lab. of Biomech. & Mechanobiology of Minist. of Educ. Beihang Univ. Beijing China

ISBN: (纸本)9781467369985

We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat- of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor Digital Pathology Challenge. Common traits of such medical image challenges are characterized by large image dimensions (up to the gigabyte size of an image), a limited amount of training data, and significant clinical feature representations. To tackle these challenges, we transfer the features extracted from CNNs trained with a very large general image database to the medical image challenge. In this paper, we used CNN activations trained by imageNet to extract features (4096 neurons, 13.3% active). In addition, feature selection, feature pooling, and data augmentation are used in our work. Our system obtained 97.5% accuracy on classification and 84% accuracy on segmentation, demonstrating a significant performance gain over other participating teams.

关键词： classification deep convolutional activation features deep learning feature learning segmentation segmentation Segmentation Brain Neoplasms Machine Learning imagery (Psychotherapy) image images visual databases image classification

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Digital assessment of facial acne vulgaris

Digital assessment of facial acne vulgaris

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ieee Instrumentation and Measurement Technology conference

作者： Aamir Saeed Malik Roshaslinie Ramli Ahmad Fadzil M. Hani Yasir Salih Felix Boon-Bin Yap Humaira Nisar Universiti Teknologi PETRONAS Seri Iskandar Perak MY Hospital Kuala Lumpur Kuala Lumpur Wilayah Persekutuan MY Universiti Tunku Abdul Rahman Petaling Jaya Selangor MY

ISBN: (纸本)9781467363846

Acne affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. However, these manual methods are time consuming and may result in intra-observer and inter-observer variations, even by experienced dermatologists. The objective of this research is to develop a computational imaging method for automated acne grading. The first step in the proposed method is pre-processing which involves lighting compensation. The CIE La*b* color space is used to measure any dissimilarity between skin colors. Acne segmentation has been performed using automated modified K-means clustering algorithm and support vector machines (SVM) classifier. Color and diameter are the main features extracted to classify acne blobs into different acne classes; papule, pustule, nodule or cyst. Finally, the severity level is determined such as mild, moderate, severe and very severe.

关键词： Skin Lesions image color analysis Feature extraction image segmentation Lighting Support vector machines

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A candidate solutions generator based on mixed strategy for non-rigid object extraction

A candidate solutions generator based on mixed strategy for ...

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International conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Min Jiang Xiaozhou Zhou Shijie Yao Zhaohui Gan Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan University of Science and Technology Wuhan P. R. China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan P. R. China

Extracting non-rigid object from images can be used in object recognition, medical image analysis, video monitoring, etc. In order to improve the efficiency and accuracy of visual object extraction, we design a candidate shape generator based on a mixture strategy, called mixture generator, it combines the image data driven method with model parameter driven method, and tends to generate valid shape in area which has a high shape prior density value by exploiting the GPDM model, so the efficiency of search is greatly improved. To prove the accuracy of our mixture generator, we have done experiments under the framework of global optimization algorithm (simulated annealing) on the FGNET face database. Experiments show that, compared with traditional ASM algorithm, our method is not only insensitive to initialization conditions, but also can put up with clutters and realize a more robust object extraction.

关键词： Shape Generators Face Active shape model Data models Simulated annealing

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The Nature of communications and Emerging Communities on Twitter Following the 2013 Syria Sarin Gas Attacks

The Nature of Communications and Emerging Communities on Twi...

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ieee Joint Intelligence and Security Informatics conference (JISIC)

作者： Yulia Tyshchuk William A. Wallace Hao Li Heng Ji Sue E. Kase Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Computational Information Sciences Directorate Army Research Laboratory MD USA

Social media has become an important communication tool especially following an extreme event. Research in social psychology has shown that people engage in gathering and "milling" information, and confirmation seeking during the process of forming intent to take action or voice an opinion. Twitter serves as a communications channel where people converge to compile collective intelligence, provide event reporting, and diffuse information. In this paper the investigation of Twitter usage seeks to describe human participation on Twitter following a controversial extreme event -- 2013 Syria sarin gas attack. The methodology employed incorporates Natural Language processing (NLP) and network analysis to trace human response on Twitter to this event. NLP techniques include Named Entity Recognition (NER) used to extract relevant entities (e.g. countries), Event Extraction (EE) to excerpt relevant events (e.g. conflict, movement, life, etc.), and Stanford Parser to detect actionable verbs discussed by Twitter participants. Network analysis constructs a network based on the Twitter users' communications, detects communities, extracts their leaders and identifies their roles based on structural properties of the networks. Specifically, the research looked at the Twitter data for two days August 22-23, 2013 following the event. The research suggests that (1) there were no immediate polarization of opinions following the event, (2) the primary event of Twitter communication was the conflict and information about the victims of the event, (3) Twitter communities were too sparse to produce substantial amount of social pressure to force an opinion/opinion shift, (4) top community leaders were news sources, political activists, and select individuals, (5) 'individual' leaders political agendas were not revealed.

关键词： Communities Twitter Media Organizations Natural language processing Feature extraction image edge detection

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REMOTE SENSING image ENHANCEMENT BASED ON WAVELET ANALYSIS AND HISTOGRAM SPECIFICATION

REMOTE SENSING IMAGE ENHANCEMENT BASED ON WAVELET ANALYSIS A...

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2014 ieee 3rd International conference on Cloud Computing and Intelligence Systems

作者： Liang Xie Guoyin Wang Xuerui Zhang Bin Xiao Botian Zhou Fan Zhang Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

In order to monitor the water quality widely and rapidly in The Three Gorges zone,it’s useful to gain the information in real-time by remote sensing images,but there are some defects in remote sensing images,such as bad visual contrast,low-resolution,and low *** than general optical images,remote sensing images contain a large amount of information;its processing is time *** order to improve the contrast and details of the image and reduce the complication of compute,an image enhancement approach based on wavelet and histogram specification is proposed in this *** the experiment results and comparison with the original image,we can see that the contrast improved by about 200%and information entropy improved by about 107%.Our proposed algorithm not only enhances the details of image greatly,but also preserves the color information of original remote sensing image *** this method do not convert the color image to other color space,the computation complexity is simple,so it is an efficient algorithm for remote sensing image enhancement in real-time.

关键词： image enhancement Wavelet transform（WT） Gaussian function Remote sensing image Histogram specification（HS） Probability density function（PDF）

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Automatic classification of sleep stages with artificial neural networks according to visual scoring rules

Automatic classification of sleep stages with artificial neu...

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ieee Signal processing and communications Applications (SIU)

作者： Osman Aydoğan Ali Öter Mahmut Kemal Kıymık Deniz Tuncel Kahramanmaras Sutcu Imam Universitesi Kahramanmaras TR Elektronik ve otomasyon Bölümü Kahramanmaraş Sütçü İmam Üniversitesi Kahramanmaraş Türkiye Elektrik Elektronik Mühendisliği Bölümü Kahramanmaraş Sütçü İmam Üniversitesi Kahramanmaraş Türkiye Dahili Tip Bilimleri Bölümü Kahramanmaraş Sütçü İmam Üniversitesi Kahramanmaraş Türkiye

In this study, Apnea / hypopnea index of less than 15 Obstructive Sleep Apnea patients of sleep stages were scored automatically. For automatic sleep scoring visual scoring system is used EEG, EOG and EMG signals using feedforward neural networks with automatic scoring has been performed. The period about 8 hours of time which patients spent in bed sleep has been divided into 30 seconds epochs. According to the 2014 produced by the American Academy of Sleep Medicine criteria to scoring, power characteristics of waves has been derived by using 6 EEG signal, taking from central, frontal and occipital region, 2 EOG signal taking from the right and left eyes and 1 EMG signals taking from the chin. Automatic sleep scoring done by using the 9 signals, gives better results than scoring a single channel. It has been thought that this automatic sleep scoring study done using visual scoring rules prevent loss of time and contribution to sleep scores of the physicians.

关键词： Electromyography Electrooculography Electroencephalography Brain modeling Signal processing Biological system modeling

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