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共 1495 条记录，以下是421-430 订阅

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Lip-reading via a DNN-HMM Hybrid System Using Combination of The image-based and Model-based Features 3

Lip-reading via a DNN-HMM Hybrid System Using Combination of...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Rahmani, Mohammad Hasan Almasganj, Farshad Amirkabir Univ Technol Tehran Polytech Biomed Engn Dept Tehran Iran

ISBN: (纸本)9781509064540

Introducing features that better represent the visual information of speakers during the speech production is still an open issue that highly affects the quality of the lip-reading and Audio Visual Speech recognition (AVSR) tasks. In this paper, three different types of visual features from both the image-based and model-based ones are investigated inside a professional lip reading task. The simple raw gray level information of the lips Region of Interest (ROI), the geometric representation of lips shape and the Deep Bottle-neck Features (DBNFs) extracted from a 6-layer Deep Auto-encoder Neural Network (DANN) are three valuable feature sets compared while employed for the lip reading purpose. Two different recognition systems, including the conventional GMM-HMM and the state-of-the-art DNN-HMM hybrid, are utilized to perform an isolated and connected digit recognition task. The results indicate that the high level information extracted from deep layers of the lips ROI can represent the visual modality with advantage of "high amount of information in a low dimension feature vector". Moreover, the DBNFs showed a relative improvement with an average of 15.4% in comparison to the shape features and the shape features showed a relative improvement with an average of 20.4% in comparison to the ROI features over the test data.

关键词： lip-reading feature extraction deep auto-encoder DBNF

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A Feature-based Approach for Monocular Camera Tracking in Unknown Environments 3

A Feature-based Approach for Monocular Camera Tracking in Un...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Hoseini, Seyyed Ali Kabiri, Peyman Iran Univ Sci & Technol Sch Comp Engn Tehran Iran

ISBN: (纸本)9781509064540

Camera tracking is an important issue in many computer vision and robotics applications, such as, augmented reality and Simultaneous Localization And Mapping (SLAM). In this paper, a feature-based technique for monocular camera tracking is proposed. The proposed approach is based on tracking a set of sparse features, which are successively tracked in a stream of video frames. In the developed system, camera initially views a chessboard with known cell size for few frames to be enabled to construct initial map of the environment. Thereafter, Camera pose estimation for each new incoming frame is carried out in a framework that is merely working with a set of visible natural landmarks. Estimation of 6-DOF camera pose parameters is performed using a particle filter. Moreover, recovering depth of newly detected landmarks, a linear triangulation method is used. The proposed method is applied on real world videos and positioning error of the camera pose is less than 3 cm in average that indicates effectiveness and accuracy of the proposed method.

关键词： Camera Tracking Particle Filter 3D reconstruction visual SLAM

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Introducing a New Method of image Reconstruction against Crop Attack Using Sudoku Watermarking Algorithm 3

Introducing a New Method of Image Reconstruction against Cro...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Behravan, Bahareh Naghsh, Alireza Islamic Azad Univ Najafabad Branch Fac Comp Engn Comp Architecture Najafabad Iran Islamic Azad Univ Najafabad Branch Digital Proc & Machine Vis Res Ctr Najafabad Iran Islamic Azad Univ Najafabad Branch Dept Elect Engn Najafabad Iran

ISBN: (纸本)9781509064540

The rapid development of digital technology makes the faster and easier transmission of electronic data with very low cost. Watermarking is one of the methods proposed for data protection in the way that information is embedded in the image without reducing image quality, but watermark may appear against different attacks. There are different ways for dealing with attacks. This article is aimed to make the watermarking image robust against the crop attack in the spatial domain by Least Significant Bit (LSB) of the image. First, in this algorithm, the host image and the algorithm of creating and solving Sudoku have been called and stored in an image form. Then, applying the XOR function on the created Sudoku and on the bits of the host image produced a robust watermark image. If the watermarked image is exposed to crop attack and partially destroyed, through recalling an algorithm to solve Sudoku, it will be recovered, and finally, the cropped parts of the image will be recovered by recovered Sudoku and the XOR function.

关键词： watermarking of image Sudoku code spatial domain crop attack XOR

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Sentence-Level Sentiment analysis in Persian 3

Sentence-Level Sentiment Analysis in Persian

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Basiri, Mohammad Ehsan Kabiri, Arman Shahrekord Univ Dept Comp Engn Shahrekord Iran

ISBN: (纸本)9781509064540

Sentiment analysis (SA) is a subfield of natural language processing and data mining which concerns the problem of extracting useful information from users' comments on the Web. Although researchers have been studying different problems in SA for more than one decade, most studies concentrate on English and languages like Persian have not received the attention they deserved. Resource scarcity for assessing sentiment analysis studies is the main limiting factor in Persian. This paper addresses the problem of resource scarcity by introducing two new resources;a sentence-level dataset for sentiment analysis in Persian, SPerSent and a new Persian lexicon, CNRC. SPerSent contains 150000 sentences, each associated with two labels;a binary label indicating the polarity of the sentence, and a five-star rating. These labels are obtained automatically using a lexicon-based method. Specifically, three lexicons are used independently to label each sentence. Then, the majority voting and average methods are used to aggregate the results for polarity and five-star labels, respectively. Finally, a well-known machine learning method, Naive Bayes, is used to evaluate the SPerSent.

关键词： Sentiment analysis Persian text mining opinion mining Persian dataset sentence-level dataset

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CLASSIFICATION OF FNIRS BASED BRAIN HEMODYNAMIC RESPONSE TO MENTAL ARITHMETIC TASKS 3

CLASSIFICATION OF FNIRS BASED BRAIN HEMODYNAMIC RESPONSE TO ...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Rahimpour, Ali Dadashi, Amin Soltanian-Zadeh, Hamid Setarehdan, Seyed Kamaledin Univ Tehran Coll Engn Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Med Image Anal Lab Detroit MI USA

ISBN: (纸本)9781509064540

Specific characteristics of the functional near infrared spectroscopy (fNIRS) of the hemodynamic response may represent the brain cortical activity levels during mental arithmetic tasks. In this paper, we use hemodynamic response signals of the prefrontal cortex, acquired by a 4-channel fNIRS system to identify the difficulty level of an arithmetic task. To this end, twelve temporal features and several classification methods are used. In addition, most discriminating features are identified by principle component analysis (PCA) method. Experimental results show that the highest accuracy rate of 92.2% is achieved by a linear Support Vector Machine (SVM) classifier. They also show that skewness and total area of the signal from the 3 cm channel on the left prefrontal lobe are the most discriminating features.

关键词： near-infrared spectroscopy mental arithmetic mathematical task cortical activation correct classification rate (CCR)

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Robust and sparse kernel PCA and its outlier map 18

Robust and sparse kernel PCA and its outlier map

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3rd International conference on Biomedical Signal and image Processing, ICBIP 2018

作者： Wang, Kunzhe Xiao, Huaitie Fan, Hongqi Zhu, Yongfeng Liu, Rang College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9781450364362

Kernel principal component analysis (PCA) generalizes linear PCA to high-dimensional feature spaces, related to input space by some nonlinear map. One can efficiently compute principal components via an eigen-decomposition of the kernel matrix. Nevertheless, classical kernel PCA has two deficiencies: the lack of robustness and sparseness. It can be affected by outliers so strongly that the resulting eigenvectors will be tilted toward them. Moreover, the technique is not sparse, since each principal component in the Hilbert space is expressed in terms of kernels associated with every training pattern. To overcome these issues, we proposed a two-stage algorithm: a robust distance was computed to identify the uncontaminated data set, followed by estimating the best-fit ellipsoid to these data for an informative and concise representation. Finally, a kernel PCA outlier map was proposed to display and classify the outliers. Simulations with synthetic data show the effectiveness of our algorithm and its corresponding outlier map. © 2018 Association for Computing Machinery.

关键词： Principal component analysis

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Shape analysis of hippocampus in temporal lobe epilepsy using Signed Poisson Mapping 3

Shape analysis of hippocampus in temporal lobe epilepsy usin...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Farazi, Mohammad Soltanian-Zadeh, Hamid Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Med Image Anal Lab Detroit MI USA

ISBN: (纸本)9781509064540

Complementary role of computer assisted models using machine learning methods in medical imaging has been a center of attention in recent years. Shape analysis of the brain structures can be used to evaluate their abnormalities and deformations, specifically in patients suffering from neurological diseases like epilepsy, Alzheimer, and Parkinson. We propose an automatic diagnosis and lateralization algorithm using Signed Poisson Mapping (SPoM), which has been recently proposed as a new framework for shape analysis of three-dimensional (3D) structures. In contrast to previous studies, we use a three-class classification to show the robustness of our algorithm in differentiating between normal, left temporal lobe epilepsy (LTLE), and right temporal lobe epilepsy (RTLE) subjects. We also use a support vector machine (SVM) classifier with a radial basic function (RBF) kernel for lateralization, i.e., differentiating between RTLE and LTLE patients. The classification accuracy for the three-class classifier is 94% and for the lateralization task is 95% which is superior to those reported in the related literature.

关键词： 3D shape analysis magnetic resonance imaging (MRI) SPoM lateralization temporal lobe epilepsy

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Dorsal Hand Vein recognition Based on Riesz Wavelet Transform and Local Line Binary pattern 3

Dorsal Hand Vein Recognition Based on Riesz Wavelet Transfor...

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3rd International conference on Frontiers of Signal Processing (ICFSP)

作者： Malutan, Raul Emerich, Simina Crisan, Septimiu Pop, Olimpiu Lefkovits, Laszlo Tech Univ Cluj Napoca Cluj Napoca Romania Sapientia Univ Dept Elect Engn Targu Mures Romania

ISBN: (纸本)9781538610381

Biometrics has been widely used in the last decades for security purposes and for increasing the confidence of people in the new informational systems. The present paper presents a new analysis and encoding method of dorsal hand vein patterns, for biometric recognition. Two multiresolution approaches, Discrete Wavelet Transform and Riesz Wavelet Transform, are firstly applied to extract directional image features. The resulted coefficients are encoded based on an ordinal procedure, namely Local Line Binary pattern.

关键词： biometrics dorsal hand vein wavelet analysis local line binary pattern

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Classification of human sperm heads using elliptic features and LDA 3

Classification of human sperm heads using elliptic features ...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Shaker, Fariba Monadjemi, S. Amirhassan Alirezaie, Javad Univ Isfahan Dept Comp Engn Esfahan 81746 Iran Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781509064540

For diagnosis of infertility in men semen analysis is conducted in which sperm morphology i.e. the size and shape of the sperm, is one of the factors that are evaluated. Since manual assessment of sperm morphology is time consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the "within class" differences and "between class" similarities. To automatically classify the sperms, appropriate features should be extracted from their microscopic images. In this research, a set of previously proposed features is extracted and examined in an automatic framework in order to evaluate their discriminating capacity in classifying sperms into four classes of shapes (Normal, Tapered, Pyriform and Amorphous). Also, a new set of features called elliptic features is proposed and added to the original features to improve the classification results. Both sets of features are used with Linear Discriminant analysis (LDA) classifier. It is shown that adding these new features, significantly improves the discrimination between those classes of sperm shapes.

关键词： sperm head classificatin sperm abnormality sperm morphology infertility eliptic features

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MULTI-MODAL DATA FUSION USING GROUP-STRUCTURED SPARSE CANONICAL CORRELATION analysis: A SIMULATION STUDY 3

MULTI-MODAL DATA FUSION USING GROUP-STRUCTURED SPARSE CANONI...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Mohammadi-Nejad, Ali-Reza Hossein-Zadeh, Gholam-Ali Soltanian-Zadeh, Hamid Univ Tehran Sch Elect & Comp Engn CIPCE Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran Henry Ford Hlth Syst Dept Radiol Med Image Anal Lab Detroit MI 48202 USA

ISBN: (纸本)9781509064540

Recent studies in the field of multi-modal data fusion demonstrate that considering the prior spatial structure and groupness information available from many years of intensive neuroimaging research can increase interpretability and detection accuracy of the hidden phenomena within multi-modal datasets. Although recent functional neuroimaging data analyses indicate that certain brain regions may participate in multiple groups (networks), the extent of group overlap is never considered in data fusion frameworks. To address this issue, here we propose a group-structured sparse canonical correlation analysis (gssCCA) method by employing groupness and sparsity constraints in a unified fusion framework. To investigate the performance of the proposed algorithm, we compare the effect of overlapping and disjoint gssCCA in a simulation study, to evaluate their ability in detecting multi-modal data associations. The results demonstrate that considering an overlapping group-structure constraint can increase the sensitivity and specificity of the true associations between multi-modal datasets.

关键词： Overlapping and Disjoint Groups Multi-Modal Data Fusion Canonical Correlation analysis (CCA) Multivariate analysis

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