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A new post-processing method to detect brain tumor using rough-fuzzy clustering 1

6th international conference on pattern recognition and Machine Intelligence, PReMI 2015

作者： Roy, Shaswati Maji, Pradipta Biomedical Imaging and Bioinformatics Lab Machine Intelligence Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9783319199412

ISBN: (纸本)9783319199405

Automatic and accurate brain tumor segmentation from MR images is one of the important problems in cancer research. However, the lack of shape prior and weak contrast at boundary make unsupervised brain tumor segmentation more challenging. In this background, a new brain tumor segmentation method is being developed, integrating judiciously the merits of multiresolution image analysis technique and rough-fuzzy clustering. One of the major issues of the clustering based segmentation method is how to extract brain tumor accurately, since tumors may not have clearly defined intensity or textural boundaries. In this regard, this paper presents a new post-processing method for clustering based brain tumor detection. It combines the merits of mathematical morphology and the concept of rough set based region growing approach to refine the result obtained after clustering, thereby ensuring the accurateness of brain tumor segmentation application. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images. © Springer international Publishing Switzerland 2015.

关键词： Mathematical morphology

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3rd international conference on Statistical Language and Speech Processing, SLSP 2015

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3rd international conference on Statistical Language and Speech Processing, SLSP 2015

ISBN: (纸本)9783319257884

The proceedings contain 27 papers. The special focus in this conference is on Statistical Language and Speech Processing. The topics include: Towards two-way interaction with reading machines;the prediction of fatigue using speech as a biosignal;supertagging for a statistical HPSG parser for spanish;residual-based excitation with continuous F0 modeling in HMM-based speech synthesis;discourse particles in french;effects of evolutionary linguistics in text classification;evaluation of the impact of corpus phonetic alignment on the HMM-based speech synthesis quality;decoding distributed tree structures;combining continuous word representation and prosodic features for ASR error prediction;semi-extractive multi-document summarization via submodular functions;a comparison of human and machine estimation of speaker age;acoustical frame rate and pronunciation variant statistics;the influence of boundary depth on phrase-final lengthening in russian;automatic detection of voice disorders;semantic features for dialogue act recognition;conversational telephone speech recognition for lithuanian;long-term statistical feature extraction from speech signal and its application in emotion recognition;rhythm-based syllabic stress learning without labelled data;unsupervised and user feedback based lexicon adaptation for foreign names and acronyms;combining lexical and prosodic features for automatic detection of sentence modality in french;corpus based methods for learning models of metaphor in modern greek;probabilistic speaker pronunciation adaptation for spontaneous speech synthesis using linguistic features;weakly supervised discriminative training of linear models for natural language processing and merging of native and non-native speech for low-resource accented ASR.

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3rd international conference on Mining Intelligence and Knowledge Exploration, MIKE 2015

3rd International Conference on Mining Intelligence and Know...

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3rd international conference on Mining Intelligence and Knowledge Exploration, MIKE 2015

ISBN: (纸本)9783319268316

The proceedings contain 67 papers. The special focus in this conference is on Mining Intelligence and Knowledge Exploration. The topics include: Spreading activation way of knowledge integration;class specific feature selection using simulated annealing;a redundancy study for feature selection in biological data;new feature detection mechanism for extended kalman filter based monocular SLAM with 1-point RANSAC;sequential instance based feature subset selection for high dimensional data;facial expression recognition using entire gabor filter matching score level fusion approach based on subspace methods;cluster dependent classifiers for online signature verification;classification using rough random forest;extending and tuning heuristics for a partial order causal link planner;symbolic representation of text documents using multiple kernel FCM;GIST descriptors for sign language recognition;a graph processing based approach for automatic detection of semantic inconsistency between BPMN process model and SBVR rules;an improved intrusion detection system based on a two stage alarm correlation to identify outliers and false alerts;a geometric viewpoint of the selection of the regularization parameter in some support vector machines;discovering communities in heterogeneous social networks based on non-negative tensor factorization and cluster ensemble approach;on the impact of post-clustering phase in multi-way spectral partitioning;bees swarm optimization for clustering large applications and an energy-efficient cluster-head selection algorithm in wireless sensor networks.

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3rd international conference on Distributed, Ambient, and Pervasive Interactions, DAPI 2015 Held as Part of 17th international conference on Human-Computer Interaction, HCI international 2015

3rd International Conference on Distributed, Ambient, and Pe...

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3rd international conference on Distributed, Ambient and Pervasive Interactions, DAPI 2015 Held as Part of 17th international conference on Human Computer Interaction, HCI international 2015

ISBN: (纸本)9783319208039

The proceedings contain 70 papers. The special focus in this conference is on Developing Intelligent Environments, Natural Interaction, Development of Distributed, Ambient and Pervasive Interactions. The topics include: Visualizing human-environment interactions;integrating concepts and techniques from HCI, human factors and media psychology;using the GQM method to evaluate calmness in ubiquitous applications;distributable interface design for web applications;enabling programmability of smart learning environments by teachers;unlocking hidden talents through sharing and making;a framework for navigating human behavior through gameful digital rhetoric;evaluating ubiquitous computing environments using 3d simulation;the transformative potential of making in teacher education;developing and evaluating two gestural-based virtual environment navigation methods for large displays;immersing users in landscapes using large scale displays in public spaces;a gesture recognition method for proximity-sensing surfaces in smart environments;developing intuitive gestures for spatial interaction with large public displays;AR coloring jigsaw puzzles with texture extraction and auto-UV mapping algorithm;smart kiosk with gait-based continuous authentication;gesture-based configuration of location information in smart environments with visual feedback;auditory browsing interface of ambient and parallel sound expression for supporting one-to-many communication;immersiveness of ubiquitous computing environments prototypes;employing virtual humans for interaction, assistance and information provision in ambient intelligence environments and a spatial interaction design in a sensible space for connecting family.

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OMEG: Oulu multi-pose eye gaze dataset 19th

OMEG: Oulu multi-pose eye gaze dataset

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19th Scandinavian conference on Image Analysis, SCIA 2015

作者： He, Qiuhai Hong, Xiaopeng Chai, Xiujuan Holappa, Jukka Zhao, Guoying Chen, Xilin Pietikäinen, Matti Department of Computer Science and Engineering University of Oulu Oulu Finland Key Lab of Intelligent Information Processing of the Chinese Academy of Sciences The Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319196640

Data is in a very important position for pattern recognition tasks including eye gaze estimation. In the literature, most researchers used normal face datasets, which are not specifically designed for eye gaze estimation. As a result, it is difficult to obtain fine labeled eye gaze direction. Therefore large datasets with well-defined gaze directions are desired. To facilitate related researches, we collect and establish the Oulu Multi-pose Eye Gaze Dataset. Inspired by the psychological observation that gaze direction is intrinsically linked with the head orientation, we are devoted to a new data set of eye gaze images captured under multiple head poses. It finally results in a dataset containing over 40K images from 50 subjects, who were asked to fixate on 10 special points on screen under different poses respectively. We investigate a new eye gaze estimation approach by using the IGO based description, and compare it with other popular eye gaze estimation approaches to provide the baseline results on our dataset. © Springer international Publishing Switzerland 2015.

关键词： pattern recognition

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Fixed point learning based 3D conversion of 2D videos 1

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6th international conference on pattern recognition and Machine Intelligence, PReMI 2015

作者： Chahal, Nidhi Chaudhury, Santanu Indian Institute of Technology Delhi India

ISBN: (数字)9783319199412

ISBN: (纸本)9783319199405

The depth cues which are also called monocular cues from single still image are more versatile while depth cues of multiple images gives more accurate depth extraction. Machine learning is a promising and new research direction for this type of conversion in today scenario. In our paper, a fast automatic 2D to 3D conversion technique is proposed which utilizes a fixed point learning framework for the accurate estimation of depth maps of query images using model trained from a training database of 2D color and depth images. The depth maps obtained from monocular and motion depth cues of input images/video and ground truth depths are used in training database for the fixed point iteration. The results produces with fixed point model are more accurate and reliable than MRF fusion of both types of depth cues. The stereo pairs are generated then using input video frames and their corresponding depth maps obtained from fixed point learning framework. These stereo pairs are put together to get the final 3D video which can be displayed on any 3DTV and seen using 3D glasses. © Springer international Publishing Switzerland 2015.

关键词： Learning systems

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A ToF-Aided Approach to 3D Mesh-Based Reconstruction of Isometric Surfaces 3rd

A ToF-Aided Approach to 3D Mesh-Based Reconstruction of Isom...

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3rd international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Hosseini, S. Jafar Araujo, Helder Univ Coimbra Dept Elect & Comp Engn Inst Syst & Robot Coimbra Portugal

ISBN: (纸本)9783319255309;9783319255293

In this paper, we investigate structure-from-motion (SfM) for surfaces that deform isometrically. Our SfM framework is intended for the estimation of both the 3D surface and the camera motion at one time through a template-based approach founded on the combination of a ToF sensor and a conventional RGB camera. The objective is to take advantage of depth maps acquired by the ToF sensor so that a considerable enhancement can be achieved in the reconstruction of the non-rigid structure using the high-resolution images captured by means of the RGB camera. A triangular mesh is adopted to represent isometric surfaces. The depth of a sparse set of 3D feature points spread all over the surface will be obtained with the help of the ToF camera, thereby enabling the recovery of the depth of the mesh vertices using a multivariate linear system. Subsequently, a non-linear constraint is formed based on the projected length of each edge of the mesh. A second non-linear constraint is then used for minimizing re-projection errors. These constraints are finally incorporated into an optimization scheme to solve for structure and motion. Experimental results show that the proposed approach has good performance even if only a low-resolution depth image is used.

关键词： Structure from motion Isometric surface ToF camera 3D reconstruction

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The Application of Dynamic Self-organised Multilayer network Inspired by the Immune Algorithm for Weather Signals Forecast 3

The Application of Dynamic Self-organised Multilayer network...

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3rd international conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

作者： Hussain, Abir Jaafar Al-Jumeily, Dhiya Al-Askar, Haya Liverpool John Moores Univ Appl Comp Res Grp Byrom St Liverpool L3 3AF Merseyside England Salman Bin Abdulaziz Univ Dept Comp Sci Al Kharj 11942 Saudi Arabia

ISBN: (纸本)9781479956807

Neural network architecture called Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm is proposed for the prediction of weather signals. Two sets of experiments have been implemented. The simulation results showed slight improvement achieved by the proposed network when using the average results of 30 simulations. For the second set of experiments, the simulation results indicated that there is no significant improvement over the first set of experiments. Since clustering methods have been widely used in different applications of data mining, the adaption of unsupervised learning in the proposed network might serve these different applications, for example, medical diagnostics and pattern recognition for big data. The structure of the proposed network can be modified for clustering tasks by changing the back-propagation algorithm in the output layer. This can extend the application of the proposed network to scientifically analyse different types of big data.

关键词： Artificial Immune Systems Time Series Data weather data

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The relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

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BMC bioinformatics 2015年第4期16卷 1-14页

作者： Ibrahim, Zina M. Ngom, Alioune Kings Coll London Inst Psychiat London SE5 8AF England NIHR Biomed Res Ctr Mental Hlth South London England Biomed Res Unit Dementia South London England Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Maudsley NHS Fdn Trust London SE5 8AZ England

Background: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results: We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions: Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

关键词： Protein Complexes Weighted Networks Functional Modules Network Clustering Criterion Protein Interaction Networks

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Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture recognition Case Study

Window-Based Feature Extraction Framework for Multi-Sensor D...

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3rd international conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated conference on Computer Science and Information Systems (FedCSIS)

作者： Grzegorowski, Marek Stawicki, Sebastian Univ Warsaw Fac Math Informat & Mech Banacha 2 PL-02097 Warsaw Poland

ISBN: (纸本)9788360810668

The article introduces a novel mechanism for automatic extraction of features from streams of numerical data. It was originally designed for the purpose of processing multiple streams of readings generated by sensors in coal mines. The original research was conducted on methane concentration analysis in the DISESOR project. The article demonstrates an application of the elaborated mechanism for the case of tagging short series of readings from sensors that monitor activities and movements of firefighters during the action with labels corresponding to firefighter activities. The purpose of the experiment was to assess how the automatic feature extraction and construction of classifiers ( without parameters tuning and without the use of classifier ensembles) can cope with the competition's task in comparison to other participants.

关键词： coal data mining feature extraction pattern classification DISESOR project coal mine methane concentration analysis multisensor data posture recognition window-based feature extraction framework Measurement units Feature extraction data mining Measurement units Coal mines pattern recognition Coal reading Fire Fighters Classifier Ensembles Sensors automatic extracting

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