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Improving Handwritten Chinese Text recognition by Confidence Transformation

Improving Handwritten Chinese Text Recognition by Confidence...

引用

international conference on Document Analysis and recognition

作者： Qiu-Feng Wang Fei Yin Cheng-Lin Liu National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences Beijing China

This paper investigates the effects of confidence transformation (CT) of the character classifier outputs in handwritten Chinese text recognition. The classifier outputs are transformed to confidence values in three confidence types, namely, sigmoid, soft max and Dempster-Shafer theory of evidence (D-S evidence). The confidence parameters are optimized by minimizing the cross-entropy (CE) loss function (both binary and multi-class) on a validation dataset, where we add non-character samples to enhance the outlier rejection capability in text recognition. Experimental results on the CASIA-HWDB database show that confidence transformation improves the handwritten text recognition performance significantly and adding non-characters for confidence parameter estimation is beneficial. Among the confidence types, the D-S evidence performs best.

关键词： Text recognition Parameter estimation Character recognition Training Handwriting recognition Lattices

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Exploring Cortical Folding pattern Variability Using Local Image Features 1

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13th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Rajalingham, Rishi Toews, Matthew Collins, D. Louis Arbel, Tal McGill Univ Ctr Intelligent Machines Montreal PQ H3A 2T5 Canada Brigham & Womens Hosp Harvard Med Sch Boston MA 02115 USA McGill Univ Montreal Neurol Inst Montreal PQ H3A 2T5 Canada

ISBN: (数字)9783642184215

ISBN: (纸本)9783642184208

The variability in cortical morphology across subjects makes it difficult to develop a general atlas of cortical sulci. In this paper, we present a data-driven technique for automatically learning cortical folding patterns from MR brain images. A local image feature-based model is learned using machine learning techniques, to describe brain images as a collection of independent, co-occurring, distinct, localized image features which may not be present in all subjects. The choice of feature type (SIFT, KLT, Harris-affine) is explored with regards to identifying cortical folding patterns while also uncovering their group-related variability across subjects. The model is built on lateral volume renderings from the ICBM dataset, and applied to hemisphere classification in order to identify patterns of lateralization based on each feature type.

关键词： Volume rendering

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An extended Evolving Spiking Neural Network model for spatio-temporal pattern classification

An extended Evolving Spiking Neural Network model for spatio...

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international Joint conference on Neural Networks (IJCNN)

作者： Haza Nuzly Abdull Hamed Nikola Kasabov Siti Mariyam Shamsuddin Harya Widiputra Kshitij Dhoble Knowledge Engineering and Discovery Research Institute Auckland University of Technology New Zealand Universiti Teknologi Malaysia Malaysia Soft Computing Research Group University Teknologi Malaysia Malaysia

This paper proposes a new model of an Evolving Spiking Neural Network (ESNN) for spatio-temporal data (STD) classification problems. The proposed ESNN model incorporates an additional layer for capturing both spatial and temporal components of the STD and then transforms them into high dimensional spiking patterns. These patterns are learned and classified in the evolving classification layer of the ESNN. A fast time-to-first-spike learning algorithm is used that enables the new model to be more suitable for learning from the STD streams in an adaptive and incremental manner. The proposed method is evaluated on a benchmark sign language video that is spatio-temporal in nature. The results show that the proposed method is able to capture important spatio-temporal information from the STD stream. This results in significantly higher classification accuracy than the traditional time-delay MLP neural network model. Future directions for the development of ESNN models for STD are discussed.

关键词： Neurons Biological neural networks Encoding Educational institutions Artificial neural networks pattern recognition Knowledge engineering

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Mental Stress Detection Based on soft computing Techniques

Mental Stress Detection Based on Soft Computing Techniques

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： F. Mokhayeri M-R. Akbarzadeh-T Biomedical Engineering Department Mashhad Branch Islamic Azad University Mashhad Iran Electrical and Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran

In this study, a novel approach is proposed for mental stress recognition through automatic analysis of eye video sequences. The proposed system consists of five stages: video capturing, fuzzy image processing, signal processing, feature extraction and, classification. The pupil parameters including Pupil Diameter (PD) and Pupil Dilation Acceleration (PDA) are measured using soft computing techniques wherein the eye region is detected using the genetic algorithm (GA), and a fuzzy filter is designed for noise reduction. Edge detection is performed based on fuzzy reasoning and linking is done using Hough transform. Then, signal processing technique is applied to the pupil parameters to extract their most relevant features. Extracted features are imported into the learning system to classify the affective states between "stress" and "relaxed". The Fuzzy SVM (FSVM) is applied to this classification process. In order to induce the stress in subjects, a Stroop color-word test is designed. Also, the results obtained from the pupil parameters are compared with two other physiological signals including Electrocardiogram (ECG) and Photoplethysmogram (PPG). The experimental results indicate the pupil parameters have great potential for stress recognition compared to the other two physiological signals and, proposed stress recognition system is promising.

关键词： Stress Feature extraction Image edge detection Electrocardiography Support vector machines Physiology

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A Novel Approach for Face recognition and Age estimation using Local Binary pattern, Discriminative approach using Two layered Back Propagation Network

A Novel Approach for Face Recognition and Age estimation usi...

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Trendz in Information Sciences & computing (TISC)

作者： P. Karthigayani S. Sridhar Computer Applications Department Sathyabama University Chennai India Sbyte Technology Bangalore India

The recent technology of image processing forms the basic principles of research entitled “A Novel Approach for Face recognition and Age estimation using Local Binary pattern, Discriminative approach using Two layered Back Propagation Network” has been developed to overcome the inconveniences faced by the organizations in recognizing the exact person. The proposed system sustains a high recognition rate in a wide range of resolution levels and it breaks the other alternative methods. Skin patches are also one of the features of our proposed work. We propose a face detection algorithm for different lighting conditions. Human Skin patches is also one of the parameter in the algorithm. The new methods using Local Binary pattern, Discriminative approach, facial algorithm and two layered back propagation algorithm for identifying the face and as well as age estimation. The Texture features and Global features are extracted from the image in different scales. The Gradient Orientation Pyramid can be formed for calculating the Age Progression and Age Estimation. The proposed method having high calculation speed compared with the existing method using Back propagation network with single layer. The dataset are taken from FG-NET and Morph Dataset. The performance comparison has been done using different datasets.

关键词： Training Skin Image segmentation Face recognition Image recognition Histograms

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PCA-Based Multispectral Band Compression and Multispectral Palmprint recognition

PCA-Based Multispectral Band Compression and Multispectral P...

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international conference on Hand-Based Biometrics (ICHB)

作者： Yong Xu Qi Zhu Bio-Computing Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

In this paper, we propose a PCA-based spectral band compression and multispectral palmprint recognition method. This method first exploits PCA to compress original multispectral bands to a smaller number of 'bands' and then uses the compressed bands to classify palmprint images. The experimental results show that our proposed PCA-based spectral band compression and recognition method can use very low-dimensional data to represent the original multispectral palmprint images and obtain a high classification accuracy.

关键词： Principal component analysis Image coding Accuracy pattern recognition Feature extraction Image recognition Support vector machine classification

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Cognitive weave pattern prioritization: An application-oriented approach

Cognitive weave pattern prioritization: An application-orien...

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IEEE international conference on Cognitive Informatics

作者： Dejun Zheng George Baciu Jinlian Hu Hao Xu Department of Computing & Institute of Textiles and Clothing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Very often, the recognition of a pattern is accompanied by a cognitive process of interpretation and understanding. In the arts and sciences, as well as in our daily lives, we learned patterns from nature and create new patterns for various applications. Weave pattern is one of the most important artificial patterns in our daily lives and there are numerous applications. To manipulate the weave patterns, texton indexing and prioritization are needed to perform, which is associated with a cognitive process of interpretation and understanding of pattern. In this regard, we use an interdisciplinary approach to help selecting weave texture patterns using tailored features and algorithms, taking into account essential features or rules of pattern design. The features and algorithms are designed based on the object-attribute-relation (OAR) model and cognitive informatics model. Three essential features of weave pattern are proposed, i.e. the complexity of patterns in production process, visual structural appearance and cognitive features to track for weave pattern. Our experiments on a wide variety of weave patterns show that the proposed approach is capable of effectively prioritizing weave texture patterns.

关键词： Weaving Degradation Insulation life Visualization

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pattern recognition of Finger Motion's EMG Signal based on Improved BP Neural Networks

Pattern Recognition of Finger Motion's EMG Signal based on I...

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2011 international conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Feng Li Yu Zhang Kening Gao Computing Centre Northeastern University Shenyang China

In the pattern recognition of Finger Motion's EMG signal, the Stability and Efficiency are both the problem. The paper proposes a new method of pattern recognition of EMG signal. The method uses AR model in the modern signal processing theory and numerical variance calculation to compress and make the feature extraction of the EMG. To make the classification of the eigenvalues of the EMG, these eigenvalues have been inputted to the MATLAB to build up a improved multilayer BP neural networks. For the recognition of three different kinds of finger motion's EMG signals, the experiment obtained more higher accuracy. It shows that the method is efficient.

关键词： BP Neural Network AR Model EMG Signal

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Computation pattern Driven Reuse of Manual Optimizations for GPGPUs

Computation Pattern Driven Reuse of Manual Optimizations for...

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IEEE international conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Shixiong Xu Dongni Han Li Chen Key Laboratory of Computer System and Architecture Institute of Computing Technology Graduate University of Chinese Academy of Sciences Beijing China

The wide application of General Purpose Graphic Processing Units (GPGPUs) results in large manual efforts on porting and optimizing algorithms on them. However, most existing automatic ways of generating GPGPU code fail to conduct optimization strategies regarding a specific computation and to reuse constantly evolving manual optimizations. In this paper, we present a computation pattern driven approach for computation-specific GPGPU code generation and optimization, which in turn reuses manual optimizations to a certain extent. We suggest language extensions to OpenMP, high-level data structure attributes, in order to assist the process of computation pattern matching and to help give users intuitive performance tuning parameters in the view of data structure attributes. We illustrate the feasibility of this approach through three important computation dwarfs, which are dense matrix, sparse matrix, and structured mesh computation in scientific computing. We also build a prototype OpenMP-to-CUDA translator that consists of computation pattern recognition and code template instantiation. The experimental results demonstrate the performance benefits of computation pattern driven method. To our best knowledge, it is the first work on reusing manual optimizations for GPGPUs with computation pattern driven approach.

关键词： Data structures Optimization pattern matching Sparse matrices Manuals Libraries Tuning

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Learning"Verb-object" Concepts for semantic image annotation 11

Learning"Verb-object" Concepts for semantic image annotation

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19th ACM international conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhang, Xinming Zha, Zheng-Jun Xu, Chang Sheng National Laboratory of Pattern Recognition CAS Institute of Automation Beijing 100190 China China-Singapore Institute of Digital Media 119613 Singapore School of Computing National University of Singapore Singapore 117417 Singapore

ISBN: (纸本)9781450306164

In real-world image understanding and retrieval applications, there exists a large number of images containing"verb-object" semantic. The most existing image annotation approaches which mainly focus on annotating images with "object" concepts may not well describe the image semantics. In this paper, we propose a novel image annotation approach by learning "verb-object" concepts. The "verb-object" concept learning method is developed based on the assumption that the classifiers of the "verb-object" concepts which contain the same object usually share a common structure. We formulate each "verb-object" concept classifier as a combination of a private part and a common part shared by all the "verb-object" concepts containing the same object. These classifiers are learned simultaneously through a joint optimization process. Experiments on a Web image data set containing 22,812 images with 28 concepts demonstrate that the proposed approach can achieve promising performance compared to the baseline method. Copyright 2011 ACM.

关键词： Image annotation

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