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Temporal Feature Extraction for Improving Myoelectric based recognition of Prosthetic Hand 5

Temporal Feature Extraction for Improving Myoelectric based ...

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5th international conference on Wireless Communications Signal Processing and Networking (IEEE WiSPNET)

作者： Bhagwat, Smita Mukherji, Prachi Vishwakarma Inst Informat Technol Dept Elect & Telecommun Pune Maharashtra India MKSSSs Cummins Coll Engn Dept Elect & Telecommun Pune Maharashtra India

ISBN: (纸本)9781728152844

the physically disabled people with upper limb amputation may get benefited from the improved control of prosthetic hand that combines individual and multiple finger control. sEMG signals recorded from muscles are mainly used to control these prosthetic hands. the data patterns of sEMG generated during muscle contraction while performing a different finger movements are utilized to generate the control commands required by such controllers. In sEMG based PR system, various features are extracted and fed to the classifier. However, the major drawback using existing time domain features is the poor recognition rate. this research aims at improving the classification accuracy of sEMG based multi-fingered prosthetic hand using two novel TD features when combined with the existing feature set. three feature sets are evaluated in terms of classification accuracy. the proposed method is validated on sEMG signal recorded by two electrodes placed on the forearm for operating ten different finger movements. ULDA, the feature projection is employed to reduce the dimensionality of feature vector size. three classifiers (SVM, KNN and LDA) are implemented to evaluate the classification accuracy. An average accuracy of 94% across all eight participants for ten different finger movements using only two channels sEMG signal proving the significance of the proposed scheme.

关键词： sEMG pattern recognition ULDA QDA

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Continuous Motion Numeral recognition Using RNN Architecture in Air-Writing Environment 5th

Continuous Motion Numeral Recognition Using RNN Architecture...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Rahman, Adil Roy, Prasun Pal, Umapada Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783030414030

Air-writing, defined as character tracing in a three dimensional free space through hand gestures, is the way forward for peripheral-independent, virtual interaction with devices. While single unistroke character recognition is fairly simple, continuous writing recognition becomes challenging owing to absence of delimiters between characters. Moreover, stray hand motion while writing adds noise to the input, making accurate recognition difficult. the key to accurate recognition of air-written characters lies in noise elimination and character segmentation from continuous writing. We propose a robust and hardware-independent framework for multi-digit unistroke numeral recognition in air-writing environment. We present a sliding window based method which isolates a small segment of the spatio-temporal input from the air-writing activity for noise removal and digit segmentation. Recurrent Neural Networks (RNN) show great promise in dealing with temporal data and is the basis of our architecture. recognition of digits which have other digits as their sub-shapes is challenging. Capitalizing on how digits are commonly written, we propose a novel priority scheme to determine digit precedence. We only use sequential coordinates as input, which can be obtained from any generic camera, making our system widely accessible. Our experiments were conducted on English numerals using a combination of MNIST and Pendigits datasets along with our own air-written English numerals dataset (ISI-Air Dataset). Additionally, we have created a noise dataset to classify noise. We observe a drop in accuracy with increase in the number of digits written in a single continuous motion because of noise generated between digit transitions. However, under standard conditions, our system produced an accuracy of 98.45% and 82.89% for single and multiple digit English numerals, respectively. © Springer Nature Switzerland AG 2020.

关键词： Recurrent neural networks

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the Analysis on Student' Psychologic Status of Online Learning under Extraction Model from Computer Face Features 5

The Analysis on Student' Psychologic Status of Online Learni...

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2020 5th international conference on Intelligent Computing and Signal Processing, ICSP 2020

作者： Wen, Lan Yang, Shaogang Zeng, Jiao Liang, Xiaowen Xu, Yanping Institute of Psychological and Education South China Business College Guangdong University of Foreign Studies China South China Business College Guangdong University of Foreign Studies China

the research aims to analyze students' psychological mood during online study and enhance the efficiency of learning. In this research, face features are extracted by LBP (local binary pattern) algorithm based on face feature recognition. the facial feature points are tracked by CLM (constrained local model) algorithm, and the face images are normalized by AdaBoost algorithm. In the end, the fatigue level of learners is calculated by P80 method in PERCLOS criterion to verify the accuracy, recognition rate and error rate of the model. the results indicate that through the recognition of the faces in a complex background, the accuracy of the mode is 95%, the recognition rate is 95.53% and the error rate is only 0.53%, the range of aspect ratio in the human eye image is 0.22 ≤ λopen ≤ 0.27, 0.05 ≤ λclose ≤0.1, and learner's level of excitement can be estimated according to the range of f. therefore, this algorithm model can extract learner's facial features very well and show a good result. Detecting the degree of learner's excitement accurately can provide an important fundamental for the realization of online learning emotion detection system which also has guiding significance for the development and popularization of the networked education. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Local binary pattern

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. this framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation 5th

A New Forged Handwriting Detection Method Based on Fourier S...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Kundu, Sayani Shivakumara, Palaiahnakote Grouver, Anaica Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. this paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. the proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. the extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate. © Springer Nature Switzerland AG 2020.

关键词： Spectral density

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VIRTECS: Virtual Screening of therapeutic Classes Using Encodings of Chemical Structures 5

VIRTECS: Virtual Screening of Therapeutic Classes Using Enco...

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5th international conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020

作者： Honnavalli, Dweepa Varma, Kavya Srinivasa, Gowri PES University PES Centre for Pattern Recognition Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781728188188

In recent times, the need for virtual screening of chemical compounds has grown with the advent of computational synthesis and de-novo generation of drugs. the state-of-the-art benchmarks of virtual screening today, incorporate chemical and physiological properties, binding affinities, along with the targets of known chemical compounds. However, benchmarks for the classification of drugs into overarching functional groups based purely on the structure of the compound are yet to be explored. In this paper, we introduce VIRTECS: a tool that leverages the simplified molecular-input line-entry system (SMILES) - a structural representation of a drug - to enable virtual screening of large scale chemical databases, based on the therapeutic classes of drugs. the only input required by the system is the SMILES representation, one that is readily available with most computational generation approaches. the experimental results on multiple datasets demonstrate the potency of structural information in determining the functional groups of chemical compounds. VIRTECS holds enormous potential in yielding insights into various properties of novel molecules when an embedding of the SMILES input is used and paired with an apposite graph algorithm, and tested with known molecules. We present a framework that allows for multiple combinations of the input (SMILES with or without the embedding) and a choice of models and databases that can be tested based on the desired output: insight to the function or potential therapeutic value of a chemical compound. © 2020 IEEE.

关键词： Embeddings

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Comparison of Backbones for Semantic Segmentation Network 5

Comparison of Backbones for Semantic Segmentation Network

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2020 5th international conference on Intelligent Computing and Signal Processing, ICSP 2020

作者： Zhang, Rongyu Du, Lixuan Xiao, Qi Liu, Jiaming International School Beijing University of Post and Telecommunication Beijing100876 China International School of BusinessandFiance Sun Yat-Sen University Guangzhou510275 China Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing100876 China

As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. this paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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A New Distance Measure Between Atanassov’s Intuitionistic Fuzzy Sets Based on Mapping 15th

A New Distance Measure Between Atanassov’s Intuitionistic F...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Su, Fang Li, Jing Xiao, Dongxuan Duan, Chengrui School of Information and Telecommunication Engineering Beijing University of Posts and Telecommunication No. 10 Xitucheng Road Haidian District Beijing100876 China

ISBN: (纸本)9783030324551

A new trapezoidal approximation of Atanassov’s intuitionistic fuzzy sets (AIFSs) and a new distance measure between AIFSs based on the area relations of membership function graphs of their corresponding trapezoidal fuzzy sets are proposed. By the conversion from Atanassov’s intuitionistic fuzzy numbers (AIFNs) to trapezoidal fuzzy numbers, the measurement of distance between AIFSs can be transformed into the calculation of area relations of the corresponding trapezoidal fuzzy sets. then the new distance measurement can integrate continuous information rather than only taking discrete variables into consideration as before. the main innovation of our distance is that it can integrate comprehensive and continuous information in calculation instead of partial discrete variables, which can efficiently avoid information loss. the contrastive tests demonstrate the efficiency and practicality of the approach. © Springer Nature Switzerland AG, 2020.

关键词： pattern recognition

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New Moments Based Fuzzy Similarity Measure for Text Detection in Distorted Social Media Images 5th

New Moments Based Fuzzy Similarity Measure for Text Detectio...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Roy, Soumyadip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783030414030

A trend towards capturing or filming images using cellphone and sharing images on social media is a part and parcel of day to day activities of humans. When an image is forwarded several times in social media it may be distorted a lot due to several different devices. this work deals with text detection from such distorted images. In this work, we consider images pass through three mobile devices on WhatsApp social media, which results in four images (including the original image) Unlike the existing methods that aim at developing new ways, we utilize the results detected by the existing ones to improve performances. the proposed method extracts Hu moments and fuzzy logic from detected texts of images. the similarity between text detection results given by three existing text detection methods is studied for determining the best pair of texts. the same similarity estimation is then used in a novel way to remove extra background or non-texts and restoring missing text information. Experimental results on own dataset and benchmark datasets of natural scene images, namely, MSRA-TD500, ICDAR2017-MLT, Total-Text, CTW1500 dataset and COCO datasets, show that the proposed method outperforms the existing methods. © Springer Nature Switzerland AG 2020.

关键词： Fuzzy logic

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Anomaly detection for images using auto-encoder based sparse representation 17th

Anomaly detection for images using auto-encoder based sparse...

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17th international conference on Image Analysis and recognition, ICIAR 2020

作者： Zhao, Qiang Karray, Fakhri Center for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo Ontario Canada

ISBN: (纸本)9783030505158

Anomaly detection is a pattern recognition task that aims at distinguishing abnormal patterns from normal ones. In this paper, we propose a convolutional auto-encoder based model to detect anomaly images by producing a sparse representation in the latent space. the proposed approach is able to represent the normal images using sparse encoding and the encoding can be well reconstructed by the decoder. However, the learned convolutional filters are not able to represent the abnormal images in a sparse way. therefore, the decoder can not reconstruct the abnormal images with high quality. By assessing the reconstruction performance, we can distinguish the abnormal images from the normal ones. the experimental results show the superiority of our proposed model over other variants of auto-encoder based anomaly detection models in terms of AUC. In addition, the results show that the sparse representation based anomaly detection method could apply to different scenarios. © Springer Nature Switzerland AG 2020.

关键词： Anomaly detection

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