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检索条件"任意字段=4th International Conference on Biomedical Signal and Image Processing, ICBIP 2019"

共 321 条记录，以下是41-50 订阅

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Attention Computation in the processing of L2 Cognition: Revisiting Learning Models 4

Attention Computation in the Processing of L2 Cognition: Rev...

引用

4th international conference on Communication, image and signal processing (CCISP)

作者： Lan, Yizhou Li, Will Xiangyu Shenzhen Univ Sch Foreign Languages Nanhai Ave Shenzhen 518000 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

Studies show that the way our brain processes incoming speech sounds has a lower-level grounding derived of acoustic similarity. Previous theoretical models of speech sound processing posit that higher-level cognitive process plays little role in perception and in successful and complete processing of speech sounds. the present study investigates if such models may be effectively extended to incorporate influences from higher level cognitive cues, such as voluntary attention, to certain acoustic dimensions of the speech sound stimuli. In this paper, we investigate the relationship in a qualitative way between the efficiency of the language processing and high-level perceptual mechanism through computational simulation of speech perception, and accuracy and reaction-time measurements. the results of experiments lead to an enhancement of existing statistical signal processing and perception models' predictions. Our findings revealed that acoustic similarity in speech sound signals merely does not accurately predict the acquisition outcome, and the enhancement of natural language learning can be achieved by effectively mining out the auxiliary cognitive cues in these signal processing activities.

关键词： signal processing

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Analysis of Convolutional Neural Network for Fundus image Segmentation 4

Analysis of Convolutional Neural Network for Fundus Image Se...

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4th international conference on Communication, image and signal processing (CCISP)

作者： Shirokanev, A. S. Ilyasova, N. Yu Demin, N. S. RAS IPSI RAS Branch FSRC Crystallog & Photon Molodogvardejskaya St 151 Samara 443001 Russia Samara Natl Res Univ Moskovskoe Shosse 34A Samara 443086 Russia

In this paper the study of fundus image segmentation using convolutional neural networks is carried out. A neural network architecture was made to classify four classes of images, which are made up of thick and thin blood vessels, healthy areas, and exudate areas. the CNN architecture was constructed empirically so as the required accuracy of no less than 96 % is ensured. the segmentation error was calculated on the exudates class, which is key for laser coagulation surgery. In the paper we utilized the HSL color model because it renders color characteristics of eye blood vessels and exudates most adequately. We have demonstrated the H channel to be most informative. We have investigated the robustness of technology to various noises. Experimental studies have shown the instability of the convolutional neural network to Gaussian white noise and resistance to impulse noise.

关键词： image segmentation

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Detect Black Box signals with Enhanced Spectrum and Support Vector Classifier 4

Detect Black Box Signals with Enhanced Spectrum and Support ...

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4th international conference on Communication, image and signal processing (CCISP)

作者： Lou, Chenlu Pan, Xiang Zhejiang Univ Coll Informat Sci & Elect Engn Zheda Rd 38 Hangzhou 310000 Peoples R China

When the plane crash into the sea and the search and rescue team search the location black box, we need to detect a specific acoustic signal in the ocean. the underwater acoustic channel has several distinct features: multipath propagation, high attenuation, and sound velocity varying as a function of depth and temperature, which make the detection of underwater signals very difficult. this paper proposes a new method in the marine signal detection and discrimination based on adaptive line enhancer and support vector machine classifier, which can provide a new idea for marine black box search. the adaptive line enhancer of the least mean square algorithm can detect narrowband signals hidden in wideband noise, while the support vector machine classifier maps signals to high-dimensional space and screens out hyperplanes separating different signals from machine data through previous data. Experimental data show that this method can bring a very high recognition rate.

关键词： signal detection

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three-dimensional Reconstruction of Optical Coherence Tomography images of Esophagus 19

Three-dimensional Reconstruction of Optical Coherence Tomogr...

引用

4th international conference on biomedical signal and image processing

作者： Nao, Sihan Zhang, Miao Wang, Lirong Xu, Yongjin Chen, Xiaohe Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Soochow Univ Sch Elect & Informat Engn Suzhou Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Med Elect Dept Suzhou Peoples R China

ISBN: (纸本)9781450372244

the combination of optical coherence tomography (OCT) and endoscope can take images of the body tissues for clinical diagnosis. OCT images are difficult to photograph with regular imaging devices, such as the esophagus and gastrointestinal tract. three-dimensional reconstruction of the two-dimensional sequence images can help the doctor understand the clinical situation of the body tissue, therefore improve the accuracy of diagnosis. In this paper, Ray Casting method is used to reconstruct three-dimensional image of OCT cross-section images of guinea pig esophagus. Preprocessing including image segmentation, coordinate transformation, angle correction is used to achieve a better result in three-dimensional reconstruction. the performance of the algorithm is discussed and can achieve the same effect as what of commercial software.

关键词： Optical coherence tomography (OCT) three-dimensional reconstruction Ray Casting Computer vision Medical image processing

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Assertion of Low Bone Mass in Osteoporotic X-ray images using Deep Learning Technique

Assertion of Low Bone Mass in Osteoporotic X-ray images usin...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Gautam Amiya Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Vishnuvarthanan Govindaraj Muneeswaran Vasudevan Arunprasath thiyagarajan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

Osteoporosis is a disease that causes low bone mass and bone tissue deterioration. the application of medical image segmentation in identifying the anomalies present in the multimodal medical images is utilized in this work to predict and locate the low bone mass section. the X ray dataset is downloaded from the freely available Kaggle repository contains osteoporosis and normal bone images. this paper utilizes Gabor Filter for the extraction of features and the modified U-net algorithm derived from Deep Convolutional Neural Network for segmentation. For improvement over classical U-Net, we had gone for Parametric Relu as an activation function to overcome the disadvantages of Relu while keeping the advantages of Relu intact. the performance metric parameters including Mean Squared Error (MSE), Peak signal to Noise Ratio (PSNR), Tanimoto Co-efficient index (TC), Dice Overlap Index (DOI) and computational time (T) are evaluated andthe outcomes attained are *** fuzzy logic dependent rules could be applied to the final outcome of image processing and deep learning techniques for clinical decision making and handling treatmenttherapy of those suffering from any of these orthopedic ailments in the regular clinical practices. the proposed method performs better on many aspects than the existing deep learning algorithms like Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN).

关键词： Deep learning Osteoporosis image segmentation PSNR Bones Feature extraction Convolutional neural networks

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CNN Assisted Hybrid Algorithm for Medical images Segmentation 20

CNN Assisted Hybrid Algorithm for Medical Images Segmentatio...

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5th international conference on biomedical signal and image processing, icbip 2020

作者： Khvostikov, Alexander Krylov, Andrey S. Mikhailov, Ilya Malkov, Pavel Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow Russia Medical Research and Educational Center Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow Russia

ISBN: (纸本)9781450387767

In this report we focus on a hybrid method based on a convolutional neural network (CNN) for histological image segmentation. We propose a CNN assisted interactive segmentation tool with weakly-supervised learning to accelerate the process of manual image annotation. the core of our annotation approach is a classical KNN classifier that uses parameters predicted by CNN. User annotates an image with scribbles of two types corresponding to glands and non-glands histological structures. Next the model performs label propagation to all unlabeled pixels providing user a fully annotated image build from his scribbled-based input. User can interact with the annotation tool and add new scribbles to correct the result. the algorithm allows to reduce one image annotation time from 150 to 25-30 minutes for PAth-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images which is necessary for the development of diagnostic algorithms. © 2020 ACM.

关键词： image annotation

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Encephalographic signal LabView processing 4th

Encephalographic Signal LabView Processing

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4th international conference on Nanotechnologies and biomedical Engineering (ICNBME)

作者： Rosca, I. C. Druga, C. Serban, I. Necula, R. D. Transilvania Univ Prod Design Mechatron & Environm Dept 1 Univ Brasov Romania Transilvania Univ Dept Med & Surg Specialties Brasov Romania

ISBN: (纸本)9783030318666;9783030318659

the research presented in the present paper concern the treatment of signals obtained from monitoring the encephalic function through electroencephalogram. the signal processing is based on the creation of a routine in named Labview software, which allow analyzing both electrocardiographic and electroencephalographic (EEG) signals. Automatic interpretation of an EEG implies analyzing a large diversity of normal and abnormal wave forms on a great number of channels. EEG monitoring aims the detection of significant changes either for slow and fast waves (spikes). A routine was created in LabVIEW software for processing the EEG signal and to indicate anesthesia degree (cerebral state index). After caption and filtering, the signal is distributed through four channels corresponding the electroencephalographic frequencies. Further, the signal was transformed as to be possible its graphic representation and, the Cerebral state index was estimated by implementing its specific formula and expressed by a numerical value. this application has the advantage to save data in different formats as to be visualized and interpreted by other software giving the graphic representations (Excel, Origin etc.). It also allows to be used for a great number of subjects, depending on PC memory, and it is easy and friendly to use for persons that have no specific IT education. the last but not the least, it is very low cost: LabVIEW license and low cost for acquisition board and little other accessories.

关键词： EEG LabVIEW signal Routine

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Research on Optimization Recognition Method of Digital image Target Point Based on Machine Vision 4

Research on Optimization Recognition Method of Digital Image...

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4th international conference on Digital Medicine and image processing, DMIP 2021

作者： Zhao, Guotao Ding, Jie Hubei Engineering University China College of Technology Hubei Engineering University Xiaogan China

ISBN: (纸本)9781450386487

In order to enhance the auto-focus detection ability of digital images exposed by a single strong light source, an optimized recognition method of digital image target points based on machine vision tracking learning is proposed. Establishing a single strong light source exposure digital image feature point enhancement detection model, carrying out feature matching of the single strong light source exposure digital image under information enhancement technology, establishing a single strong light source exposure digital image three-dimensional reconstruction model, constructing a fuzzy feature detection algorithm of the single strong light source exposure digital image, and carrying out RGB decomposition of the single strong light source exposure digital image through fast low illumination image feature point identification feature matching, the spatial matching function of digital image under fast low illumination image feature point recognition is obtained. Under the machine vision tracking recognition model, fast low illumination image feature point recognition information fusion is carried out. And combined with spatial visual information enhancement method, the matching filter detection of digital image exposed by single strong light source is carried out. through wavelet feature decomposition and enhanced information method, the target point optimization recognition of digital image exposed by single strong light source is carried out, and the signal-to-noise ratio of digital image exposed by single strong light source is improved. the results show that this method can be used to identify the target points of digital images exposed by high single strong light source. © 2021 ACM.

关键词： Light sources

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Facial Spot Contour Extraction based on Color image processing 19

Facial Spot Contour Extraction based on Color Image Processi...

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4th international conference on biomedical signal and image processing

作者： Liu, Xiaojin Sun, Jiuai Wang, Xiong Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China Shanghai Univ Med & Hlth Sci Sch Med Imaging Shanghai Peoples R China Shanghai Sixth Peoples Hosp East Hosp Dermatol Dept Shanghai Peoples R China

ISBN: (纸本)9781450372244

In this paper we discuss the problem of automatic contour extraction of facial spot based on RGB images. Prior similar work has been frequently used for processing those hyperpigmentation skin conditions such as melasma and melanoma, where the separation between pigmented area and normal skin is easy to define. However the melanin under facial spots is normally deposited in a scatter way and distributed superficially, this makes the contrast between the area of spots and that of normal skin become small. As such it is difficult to directly extract the contour of the spots. After analyzing the individual three color channels of facial spot RGB skin image, we found that the blue channel provides the clearest edge of the spots, while the edge presents a certain amount of blur in the red channel. therefore, this study proposed a new image processing strategy for facial spots analysis, i.e. to firstly separate the RGB channels to obtain the blue channel, then, the maximum entropy threshold segmentation and the Snake method are used to extract the contour of color spots. the experiments verified that the separated color channel and Snake-based method can help to reliably extract edge contours and preserve the color information of the spot.

关键词： Snake algorithm Edge detection Contour extraction Color image analysis White balance

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Deep Learning Based Surface EMG Hand Gesture Classification for Low-Cost Myoelectric Prosthetic Hand 9

Deep Learning Based Surface EMG Hand Gesture Classification ...

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Joint 9th international conference on Informatics, Electronics and Vision (ICIEV) / 4th international conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Nahid, Nazmun Rahman, Arafat Ahad, M. A. R. Univ Dhaka Dhaka Bangladesh

ISBN: (纸本)9781728193311

In this paper, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository, and UCI Machine Learning Repository, is shown. Applying transfer learning and CNN-LSTM neural network architectures, we find out a suitable control scheme for a myoelectric prosthetic hand (we mention it as DUFAB Hand). At first, the continuous wavelet transform (CWT) is exploited to create images from the sEMG signal, which serves as a powerful feature for the classification of different hand gestures. then, we transferred the learning of various neural nets of image classification, e.g., AlexNet, and ResNet-18 to the sEMG image classification. Application of these deep neural networks outperformed general machine learning techniques with higher accuracy and performance. For example, the combination of CNN and LSTM has achieved the state of the art accuracies for these data sets, of 99.72% for UCI Machine Learning Repository and 99.83% for Rami Khusaba EMG repository respectively. the main contribution of this paper is, establishing an algorithmic pipeline using continuous wavelet transform (CWT) and CNN-LSTM deep neural networks to achieve high accuracy in two sEMG datasets.

关键词： Deep Learning Transfer Learning sEMG CNN LSTM biomedical signal processing

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