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检索条件"任意字段=3rd IAPR International Conference on Pattern Recognition in Bioinformatics"

共 2194 条记录，以下是521-530 订阅

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Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation

Classification of Indonesian Batik Using Deep Learning Techn...

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international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： I Made Artha Agastya Arief Setyanto Informatics Universitas AMIKOM Yogyakarta Sleman Indonesia

ISBN: (纸本)9781538670835;9781538670828

Although batik is one of the most prestige heritage in Indonesia, many Indonesian people cannot recognize the pattern name of batik that they wear or see. Moreover, the batik varieties increase each year so batik pattern becomes harder to be identified. Based on that fact, automatic batik classification become of more importance to assist people in recognizing the batik pattern. In addition, Batik patterns are critical to being understood because there is the history behind the pattern. In order to recognize the batik pattern automatically, we implement batik classification methods using Convolutional Neural Network (CNN) that is called VGG-16 and VGG-19 and they are able to predict almost 90% correctly in classifying batik patterns. But the variation of batik images such as rotated and scaled images make the classifier cannot effectively detect the type of batik pattern. To illustrate, the batik classification accuracy becomes less than 56% when it has classified a batik pattern that is scaled 2.0. Then we train the CNN with augmented data to improve the accuracy. After all, the augmented data technique can improve the accuracy as well as 10% for rotated images or scaled images.

关键词： Feature extraction Testing Training Support vector machines Training data pattern recognition Information technology

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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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Accurate Fall Detection Using 3-Axis Accelerometer Sensor And MLF Algorithm 3

Accurate Fall Detection Using 3-Axis Accelerometer Sensor An...

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3rd international conference on pattern Analysis and Image Analysis (IPRIA)

作者： Jahanjoo, Anice Tahan, Marjan Naderan Rashti, Mohammad Javad Shahid Chamran Univ Ahvaz Fac Engn Comp Engn Dept Ahvaz Iran

ISBN: (纸本)9781509064540

Nowadays, with the growing population of elderly people, the number of elderly without caregivers at home has also increased. It is clear that an elderly living alone at home is at higher risk of severe damage, due to potential delays in notifying caregivers and providing care at healthcare facilities. This especially becomes critical in case of high-risk incidents such as stroke or heart attack. To address this issue, an increasing number of methods have been proposed that employ various fall detection algorithms for elderly people. In this paper, we propose a new algorithm to detect falls, using a multi-level fuzzy min-max neural network. The proposed algorithm is compared with three other machine-learning algorithms (MLP, KNN, SVM). The main focus of this paper is on the effect of dimensionality reduction with using the Principal Component Analysis (PCA) method inside the proposed algorithm. The evaluations show that the multi-level fuzzy min-max neural network provides a high level of accuracy with a small number of dimensions. This is in contrast to the other algorithms, where accuracy is further lowered after applying dimensionality reduction. The performance evaluation of this algorithm on a public dataset obtained using accelerometer sensor data with using three dimensions indicates an accuracy of 97.29% for the sensitivity metric and 98.70% for the specifity metric.

关键词： fall detection triaxial accelerometer fuzzy minmax neural networks

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An efficient post-processing approach for dimensionality reduction methods for face recognition 3

An efficient post-processing approach for dimensionality red...

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3rd international conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Abbad, Abdelghafour Abbad, Khalid Tairi, Hamid Fac Sci Dhar EL Mahraz Lab LIIAN Fes Morocco Fac Sci & Technol Lab ISA Fes Morocco

ISBN: (纸本)9781538605516

In this paper we propose a new post-processing approach for dimensionality reduction methods based on multidimensional ensemble empirical mode decomposition (MEEMD). In the proposed method, the features are decomposed into different components and then we maximize the dependency and the dispersion between classes thanks to Gaussian filter and Butterworth filter. The performance of the proposed algorithm is demonstrated in experiments by several dimensionality reduction techniques on two public databases.

关键词： Computer vision dimensionality reduction techniques face recognition MEEMD pattern recognition post-processing

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Cyclic Cumulant Based On Communication Signal Multilayer Neural Network Modulation pattern recognition

Cyclic Cumulant Based On Communication Signal Multilayer Neu...

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international conference on Cloud Computing and Internet of Things (CCIOT)

作者： Guan Qingyang Zhang Yue College of Electronic and Information Engineering Shenyang Aerospace University Shenyang

ISBN: (数字)9781538671412

ISBN: (纸本)9781538671429

After deducing and simulating the cyclic spectrum theory of MPSK and MQAM modulated signals, the cyclic spectrum of the modulated signal is preprocessed and the original feature set is formed as the input data of the CNN. With feature extraction of cyclic spectrum from convolution and subsampling layers of CNN, the modulation signal can be effectively identified within and between classes. The experimental simulation results show that the classification and recognition algorithm of communication signal modulation mode proposed in this paper has better modulation recognition accuracy than other traditional modulation recognition algorithms in the case of low SNR. And when the signal to noise ratio is lower than 5db and higher than -5db, the modulation pattern recognition accuracy can be reached up to 92%.

关键词： Mathematical model Feature extraction Classification algorithms Binary phase shift keying Signal to noise ratio Correlation

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Generalizing Integer Projected Graph Matching Algorithm for Outlier Problem

Generalizing Integer Projected Graph Matching Algorithm for ...

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international conference on Image, Vision and Computing (ICIVC)

作者： Lei He Xu Yang Zhi-Yong Liu Field Engineering College Army Engineering University of PLA Nanjing China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538649923

Graph matching plays an important role in computer vision and pattern recognition. Recent graph matching algorithms usually formulate graph matching by a discrete optimization problem, and have designed various types of optimization techniques to find a local optimum in reasonable time. Among them some algorithms utilizing the graduated projection to the discrete domain exhibit superior performance, but these algorithms are limited to specific applications. From the outlier perspective, they are applicable to subgraph matching in which outliers exist in at most one graph. However, in real tasks there are usually outliers in both graphs. Previously we have proposed a method directly targeting at finding the most similar subgraphs in two weighted graphs. In this paper we show that the idea can be generalized to other algorithms, and the IPFP is chosen as a representative algorithm for generalization. Experiments witness the effectiveness of the generalization.

关键词： Optimization Task analysis Electronic mail Computer vision Kernel Linear programming pattern matching

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4th iapr TC 9 Workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

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4th iapr TC 9 Workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

ISBN: (纸本)9783319592589

The proceedings contain 13 papers. The special focus in this conference is on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction. The topics include: Bimodal recognition of Cognitive Load Based on Speech and Physiological Changes;Human Mobility-pattern Discovery and Next-Place Prediction from GPS Data;Fusion Architectures for Multimodal Cognitive Load recognition;Performance Analysis of Gesture recognition Classifiers for Building a Human Robot Interface;On Automatic Question Answering Using Efficient Primal-Dual Models;Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion for Action recognition;Audio Visual Speech recognition Using Deep Recurrent Neural Networks;Audio-Visual recognition of Pain Intensity;The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Painand Emotion-recognition System;Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models and Recursively Measured Action Units.

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Analyses of the Technological Accumulation over the 2nd and the 3rd AI Boom and the Issues Related to AI Adoption by Firms

Analyses of the Technological Accumulation over the 2nd and ...

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Portland international conference on Management of Engineering and Technology (PICMET)

作者： Kumiko Miyazaki Ryusuke Sato Dept. of Technology and Innovation Management Tokyo Institute of Technology Tokyo Japan Global Business Services Watson Delivery No.2 IBM Tokyo Japan

Although AI was proposed by J. McCarthy back in 1956, 60 years later, we are beginning to witness a surge of interest in the practical applications of AI in many sectors. However, the actual adoption rate of AI in businesses has been quite low. The current 3rd AI boom follows the 2nd AI boom in the 1980s which focused mainly on expert systems. In this paper, an attempt is made to analyze the technological accumulation process from the 2 nd to the 3rd AI boom and to identify forthcoming key application areas. The methodology is based on a 3 step approach. In the first step, a bibliometric analyses of Artificial Intelligence was carried out to analyze the technological accumulation during the 2nd and the 3rd AI boom. Having done the desk work and interviewed several experts in AI, we decided that 2013 was the year which marked the boundary between the two AI booms. A bibliometric analyses based on countries and institutions over the 2 periods was followed by co-occurrence analyses of the author keywords in the 2 periods, before and after 2013. In the third stage, interviews were carried out with some corporate members to do a qualitative analysis on the possible application areas of AI and issues to be solved for adopting AI were identified. The results showed that in the 3rd AI boom, machine learning, deep learning, genetic algorithm have been identified as the key technologies. Furthermore, prediction, forecasting, data mining, fault diagnosis, pattern recognition were identified as important areas in the 3rd AI boom. It was also revealed that firms' R&D has been changing to focus more on AI applications.

关键词： Machine learning Bibliometrics Companies Neural networks Market research Expert systems

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Survey on Fault Diagnosis of Diesel Engine Based on Vibration Signal 3

Survey on Fault Diagnosis of Diesel Engine Based on Vibratio...

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3rd international conference on Information Management (ICIM)

作者： Wu, Husheng Yan, Qian Ling, Xiaodong Armed Police Force Engn Univ Mat Engn Coll Xian Shaanxi Peoples R China Satellite Maritime Tracking & Control Ctr Joint Lab Marine Measurement & Control Jiangying Peoples R China

ISBN: (纸本)9781509063062

The diesel engine, an efficient power machinery, is widely applied in many fields, such as vehicle, watercraft and petroleum machinery. The significance and difficulties of fault diagnosis on diesel engine is stated. Some domestic and foreign conventional methods of diesel state monitoring and fault diagnosis and their principles is discussed. Then, the applications of vibration signal time frequency analysis method and pattern recognition method in fault diagnosis for diesel engine are analyzed. What's more, the established fault diagnosis model which combined signal time frequency analysis method with pattern recognition method is discussed in detail. Finally, the development trend of marine diesel fault diagnosis technology is forecasted.

关键词： vibration signal processing diesel engine fault diagnosis pattern recognition

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Human Action recognition Using Autoencoder 3

Human Action Recognition Using Autoencoder

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3rd IEEE international conference on Computer and Communications (ICCC)

作者： Xiao, Qinkun Si, Yang Xian Technol Univ Sch Elect Informat Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781509063529

In this research, we developed a new deep neural network model to identify human action that was composed of an autoencoder and a pattern recognition neural network (PRNN). Our approach was divided into two parts: a system learning stage and an action recognition stage. In the system learning stage, first we secured human body outlines for each image frame, and combined the outlines to build an overlay of binary images to use as training data. Based on deep neural network learning, an autoencoder was trained to extract action features. Next, we used supervised learning to train a PRNN on the obtained features. Last, we combined the autoencoder with the PRNN to build a new deep neural network called the APRNN. Using fine tuning, the APRNN achieved optimal performance. In the action recognition stage of our approach, human action sequences were translated into binary overlay images, and the ARPNN was used to identify the actions. Test results showed our method had better performance than existing approaches.

关键词： action recognition autoencoder binary overlay image deep neural network neural network pattern recognition

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