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Automatic assessment mark entry system using local binary pattern (LBP) and salient structural features

Automatic assessment mark entry system using local binary pa...

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IEEE international conference on Control System, computing and Engineering (ICCSCE)

作者： Lim Lam Ghai Suhaila Badarol Hisham Norashikin Yahya Department of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Tronoh Malaysia

Offline handwritten digit recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be time consuming, not cost effective and prone to human mistakes. Thus, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students' identity numbers and handwritten marks on assessment forms into a spreadsheet. The training data contain three sets of LBP values for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of scaling the digits `0 to 9' from the highest to the lowest similarity score as compared with the sample using chi square distance. The recognition rate can be greatly improved to about 95% by verifying the ranking of chi square distance with the salient structural features of digits.

关键词： Feature extraction Handwriting recognition Histograms Training data conferences Control systems

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Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Moto: Enhancing Embedding with Multiple Joint Factors for Ch...

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international conference on pattern recognition

作者： Xunzhu Tang Rujie Zhu Tiezhu Sun Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Central Florida Orlando FL USA Momenta Suzhou China Institute of Computing Technology Chinese Academy beijing China

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F₁-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.

关键词： Fuses Text categorization Semantics pattern recognition Task analysis

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Optimising pattern recovery in recurrent correlation associative memories

Optimising pattern recovery in recurrent correlation associa...

引用

international conference on pattern recognition

作者： R.C. Wilson E.R. Hancock Department of Computer Science University of York York UK

ISBN: (纸本)0769507506

Addresses the problem of how to identify the optimal excitation function for the recurrent correlation associative memory. We present a model of pattern recovery which allows us to measure probability of bit-error. By minimising this measure we are able to numerically locate the excitation function which results in the minimum error of pattern recall. Additionally, we show that minimising a simpler measure of pattern overlap leads to an analytical expression for the excitation function which is exponential. We compare the performance of the numerical and exponential functions. This reveals that the more easily controlled exponential is only slightly poorer in its performance.

关键词： pattern analysis pattern recognition Associative memory Computer science Performance analysis Noise measurement Distributed computing Computer architecture

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Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer's Disease Using Forestnets 48

Transferring Quantified Emotion Knowledge for the Detection ...

引用

48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pérez-Toro, P.A. Rodríguez-Salas, D. Arias-Vergara, T. Bayerl, S.P. Klumpp, P. Riedhammer, K. Schuster, M. Nöth, E. Maier, A. Orozco-Arroyave, J.R. Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen Germany Universidad de Antioquia UdeA Gita Lab Facultad de Ingeniería Medellín Colombia Ludwig-Maximilians University Department of Otorhinolaryngology Head and Neck Surgery Munich Germany Technische Hochschule Georg Simon Ohm Nürnberg Germany

ISBN: (纸本)9781728163277

Progressive loss of memory is the most known symptom of Alzheimer's Disease (AD);however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model's decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition;then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD. © 2023 IEEE.

关键词： Alzheimer's Disease Depression Neural Networks Speech Processing

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Feature extraction based on fuzzy bidirectional maximum margin criterion for face recognition

Feature extraction based on fuzzy bidirectional maximum marg...

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2010 IEEE international conference on Intelligent computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Haishun Du Xiaobo Guo Jianjun Zhao Fan Zhang Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan China School of mechanical and power engineering Hennan Polytechnic University Jiaozuo Henan China Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan China

This paper proposes a new method for feature extraction and recognition, namely, the fuzzy bidirectional maximum margin criterion (FBMMC) based on the maximum margin criterion and fuzzy set theory. In FBMMC, a membership grade matrix is calculated by the FKNN algorithm, and then the membership grades are incorporated into the formulation of the scatter matrices to formulate the row and column directional fuzzy scatter matrices. Using the fuzzy scatter matrix, FBMMC performs fuzzy image MMC in the row and column directions simultaneously. Experiments on the ORL and Yale face databases show the effectiveness of the proposed FBMMC based feature extraction method.

关键词： Maximum margin criterion (MMC) Fuzzy bidirectional maximum margin criterion (FBMMC) Feature extraction Face recognition

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How human visual systems recognize objects - a novel computational model

How human visual systems recognize objects - a novel computa...

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international conference on pattern recognition

作者： Sungho Kim Gi-Jeong Jang Wang-Heon Lee In So Kweon Department of Electrical Engineering & Computer Science KAIST Daejeon South Korea

ISBN: (纸本)0769521282

This paper presents a novel computational model of 3D object recognition based on human visual system. Conventional schemes have feed forward structure based on the bottom-up process of human vision. However, psychological and physiological evidence suggests that top-down process and feature binding by visual attention are also important. Hence, we propose a method to integrate these facts under statistical framework, Markov chain Monte Carlo. In this scheme, object recognition is regarded as parameter optimization problem. The bottom-up process is used to initialize parameters and top-down process is used to optimize them. On both processes, feature map binding is performed by spatial attention mechanism. Experimental results show that the proposed computational model is feasible for 3D object recognition.

关键词： Humans Computational modeling Object recognition Computer vision Visual system Psychology Military computing Streaming media pattern recognition Computer science

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A Color Edge Detection Algorithm in RGB Color Space

A Color Edge Detection Algorithm in RGB Color Space

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international conference on Advances in Recent Technologies in Communication and computing, ARTCom

作者： Soumya Dutta Bidyut B. Chaudhuri Department of Electronics and Communication Netaji Subhash Engineering College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424451043

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Image edge detection Color Image processing Object recognition Shape measurement pattern recognition Area measurement Computer vision Image segmentation Image coding

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A Verification Method for Viewpoint Invariant Sign Language recognition

A Verification Method for Viewpoint Invariant Sign Language ...

引用

international conference on pattern recognition

作者： Qi Wang Xilin Chen Chunli Wang Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology CAS China

Viewpoint variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major variances between two video sequences of the same sign: performance variance and viewpoint variance. For small performance variance, DTW can help us eliminate it. When there is only viewpoint variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks

关键词： Handicapped aids Stereo vision pattern recognition Video sequences Computer science Deafness Content addressable storage Humans Hidden Markov models Feature extraction

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Personal Authentication Using Human Iris recognition Based on Embedded Zerotree Wavelet Coding

Personal Authentication Using Human Iris Recognition Based o...

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international Multi-conference on computing in the Global Information Technology, ICCGI

作者： Lili Hsieh Wen-Shiung Chen Tzung-Hao Li Information Management Department Hsiuping Institute of Technology Taichung Taiwan Department of Electrical Engineering National Chi Nan University Nantou Taiwan

This paper presents a biometric recognition system based on the iris of a human eye using embedded zerotree wavelet coding. The proposed system consists of three modules: image pre-processing, feature extraction, and recognition modules. Image pre-processing module uses some image processing algorithms to localize and cut the region of iris out of the input image. The feature extraction module adopts a modified embedded zerotree wavelet coding to discriminate texture features. The system encodes the features to generate its iris feature codes. Finally, the recognition module uses these feature codes for iris matching. The experimental results show that the system has an encouraging performance on the UBIRIS iris image database. The recognition rates up to 100.00% can be achieved.

关键词： Iris recognition Feature extraction Image coding Transforms pattern recognition Authentication Humans

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pattern stability on complex-valued associative memory by local iterative learning scheme

Pattern stability on complex-valued associative memory by lo...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Hiroki Yamamoto Teijiro Isokawa Haruhiko Nishimura Naotake Kamiura Nobuyuki Matsui Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Graduate School of Applied Informatics University of Hyogo Kobe Hyogo Japan

Stability of embedded patterns on associative memory is investigated in this paper. The associative memory is composed of complex-valued Hopfield neural network, in which the state of neurons are encoded by the phase values on a unit circle of complex plane. Local iterative learning scheme and Projection rule are used for embedding the patterns onto the network. The retaining performance for embedded patterns are evaluated through storing randomly generated patterns and gray-scaled images with changing the resolution of neuron state.

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