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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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End-to-End Learning of Variational Models and Solvers for the Resolution of Interpolation Problems

End-to-End Learning of Variational Models and Solvers for th...

引用

IEEE international conference on Acoustics, Speech and signal processing

作者： R. Fablet L. Drumetz F. Rousseau UMR CNRS Lab-STICC IMT Atlantique Brest FR UMR INSERM LaTIM IMT Atlantique Brest FR

ISBN: (纸本)9781728176055;9781728176062

Variational models are among the state-of-the-art formulations for the resolution of ill-posed inverse problems. Following recent advances in learning-based variational settings, we investigate the end-to-end learning of variational models, more precisely of the regularization term given some observation model, jointly to the associated solver, so that we can optimize the reconstruction performance. In the proposed end-to-end setting, both the variational cost and the gradient-based solver are stated as neural networks using automatic differentiation for the latter. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.

关键词： Interpolation image resolution Inverse problems conferences Time series analysis signal processing algorithms signal processing

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Muse: Multi-Modal Target Speaker Extraction with Visual Cues

Muse: Multi-Modal Target Speaker Extraction with Visual Cues

引用

IEEE international conference on Acoustics, Speech and signal processing

作者： Zexu Pan Ruijie Tao Chenglin Xu Haizhou Li Institute of Data Science National University of Singapore (NUS) Singapore Graduate School for Integrative Sciences and Engineering NUS Singapore NUS Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781728176055;9781728176062

Speaker extraction algorithm relies on the speech sample from the target speaker as the reference point to focus its attention. Such a reference speech is typically pre-recorded. On the other hand, the temporal synchronization between speech and lip movement also serves as an informative cue. Motivated by this idea, we study a novel technique to use speech-lip visual cues to extract reference target speech directly from mixture speech during inference time, without the need of pre-recorded reference speech. We propose a multi-modal speaker extraction network, named MuSE, that is conditioned only on a lip image sequence. MuSE not only outperforms other competitive baselines in terms of SI-SDR and PESQ, but also shows consistent improvement in cross-dataset evaluations.

关键词： Visualization Lips conferences signal processing algorithms signal processing Acoustics image sequences

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Pivot-V: An Optimized Algorithm for Password Generation in PCFGs Model

Pivot-V: An Optimized Algorithm for Password Generation in P...

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IEEE international conference on signal and image processing (ICSIP)

作者： Shupeng Wang Xujian Liu Ge Wu Liquan Chen Zhongyuan Qin Qunfang Zhang Yipeng Zhang Key Laboratory of Computer Networking Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing China Air-defence Institute Nanjing Campus Nanjing China

ISBN: (纸本)9781665446006

Password authentication is popular in identity authentication. However, it is vulnerable to guessing attack. Recently, attacks based on PCFGs (Probability Context Free Grammars) shows its effectiveness. In PCFGs, when the number of generated passwords is very large (such as more than 10^10), the generation time of passwords is too long. This paper improves the Pivot algorithm of traditional PCFGs model and adopts the pre-terminal structure based on Pivot-V algorithm to optimize the password generation. The experimental results show that the password generation speed of the optimized model is 4.9 times faster than that of the traditional PCFGs model.

关键词： Context Heuristic algorithms Instruction sets image processing conferences Authentication Passwords

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Fast Low-light image Enhancement Algorithm Based on Fusion

Fast Low-light Image Enhancement Algorithm Based on Fusion

引用

IEEE international conference on signal and image processing (ICSIP)

作者： Junjie Guo Zhengping Li Chao Xu Bo Feng School of Electronics and Information Engineering AnHui University AnHui Engineering Laboratory of Agro-Ecological Big Data HeFei China

ISBN: (纸本)9781665446006

Low-light images generally exist in various situations, and the existing low-light image enhancement algorithms are poor in generality and have high computational complexity, and the comfort of the enhanced image is not satisfactory. This paper proposes a fast low-light image enhancement algorithm based on fusion. First, transform the passing space to HSV space to extract the V component. Then, according to the influence of Weber-Fechner's law on human visual senses, and effectively combining the characteristics of low-light images, a global adaptive brightness transformation function is constructed, and this function generates the first enhanced image. Then use CLAHE to increase the local contrast and generate a second enhanced image. Finally, an image fusion strategy is adopted to extract image details to fuse the image effectively. The proposed algorithm is fast and straightforward, can effectively improve the quality of low-light images, and better preserve its naturalness. Experimental results show that the proposed algorithm has achieved good results both subjectively and objectively.

关键词： Deep learning Visualization Histograms Fuses conferences Brightness Transforms

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Research on Endoscopic image Enhancement Algorithm Based on Contrast Fusion

Research on Endoscopic Image Enhancement Algorithm Based on ...

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IEEE international conference on signal and image processing (ICSIP)

作者： Yuzhe Zhu Chao Xu Bo Feng Shanshan Fang school of Electronics and Information Engineering AnHui University AnHui Engineering laboratory of Agro-Ecological Big Data HeFei China

ISBN: (纸本)9781665446006

image enhancement can improve doctors' perception of gastrointestinal tissues and blood vessels. This paper introduces an image enhancement method for medical endoscopy, which includes three stages: (1) improved contrast enhancement algorithm, using multiple iterative sharpening mask to enhance the edge contour of gastrointestinal tissues and blood vessels; (2) improved limiting threshold of limiting contrast adaptive histogram equalization (CLAHE); (3) improved contrast image fusion to preserve the good characteristics of improved contrast enhancement algorithm and improved CLAHE. The proposed method is applied to large data sets of low contrast white light endoscopes. The experimental results show that the endoscopic image enhancement algorithm based on contrast fusion is a feasible method to solve the defects between different image enhancement algorithms, because the image fusion results can effectively reduce the defects of contrast enhancement algorithm and CLAHE compared with the original image, such as detail loss, local contrast loss and pixel value loss.

关键词： Radiation effects Limiting Endoscopes Medical services Blood vessels Gastrointestinal tract Reflection

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On the Use of Spectrogram Inversion for Speech Enhancement

On the Use of Spectrogram Inversion for Speech Enhancement

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IEEE SSD international Multi-conference on Systems, signals and Devices

作者： Raja Abdelmalek Bedoui Zied Mnasri Faouzi Benzarti Signal Image and Technology of Information Laboratory Ecole Nationale d'Ingénieurs de Tunis University Tunis El-Manar Tunisia Ecole Nationale d'Ingénieurs de Tunis University Tunis El-Manar Tunisia

Spectrogram inversion or phase retrieval is an old topic in digital signal processing, that has been revisited since a few years for its proved relevance to many recent applications, such as source separation, speech enhancement and compressive sensing. Spectrogram inversion aims to reconstruct a signal from partial spectral information, such as the magnitude spectrum or the phase spectrum only, which are obtained by the short-time Fourier transform (STFT). Thus, in this work the relevance of signal reconstruction is studied. First, the proposed algorithm, based on the recent theoretic relationships between STFT magnitude and phase is presented. Secondly, the proposed method is tested on clean and simulated-noisy speech. Finally, the relevance of spectrogram inversion as implemented either in our proposal or in state-of-the-art algorithms is evaluated for the particular application on speech enhancement. The results show the advantages and the limits of using spectrogram inversion in such an application.

关键词： Source separation Fourier transforms signal processing algorithms Digital signal processing Speech enhancement signal reconstruction Iterative algorithms

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Interactive gesture feature recognition method in 3D virtual laboratory based on mobile terminal

Interactive gesture feature recognition method in 3D virtual...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Dan Zhao School of Mathematics and Information Science Guangxi College of Education Nanning China

ISBN: (纸本)9781665447300

In order to further improve the accuracy of laboratory interactive gesture recognition. Therefore, this paper proposes an interactive gesture feature recognition method for 3D virtual laboratory under mobile terminal, which extracts gradient direction histogram and local binary pattern features respectively, and carries out feature fusion. The fusion features include not only the gradient direction information of the local region of the image, but also the texture information, which can more comprehensively describe the gesture features. The fusion feature vector is input into SVM classifier to complete gesture recognition. Experiments show that the method of gesture recognition in 3D virtual laboratory under mobile terminal has higher accuracy than traditional methods. In this experiment, a variety of gestures are recognized, and the maximum recognition rate is significantly improved.

关键词： Support vector machines Histograms image recognition Three-dimensional displays signal processing algorithms Gesture recognition signal processing

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Crowdsourced Mobile Service for Preventing Child Trafficking Using image Analytics 4th

Crowdsourced Mobile Service for Preventing Child Trafficking...

引用

4th international conference on advanced Informatics for Computing Research, ICAICR 2020

作者： Dhinakaran, K. Udhayakumar, S. Nedunchelian, R. Varshini, V.J. Swathi, D. Rajalakshmi Institute of Technology Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Karpaga Vinayaga College of Engineering and Technology Chennai India

ISBN: (纸本)9789811636592

Child trafficking are increasing in urban locations and the count is only increasing. Mostly the children are abused and exploited for begging, involuntary domestic servitude, forced child labor, commercial sex, and other illegal activities. In India, trafficking has grown over 14 times over the last decade according to National Crimes Records Bureau (NCRB) and the country has become a transit point for traffickers. The necessity to prevent child trafficking and engaging them in self-development, is the responsibility of every citizen towards nation building. Technological advancements have taken a huge leap in the domain of mobile analytics and social networking and therefore it should be used as a tool to mitigate this burning issue. Empowering every individual to report child trafficking using mobile devices and let the technology do the rest of analytics to identify vulnerable spots, missing child, type of trafficking and reporting to authorities can make a difference. Therefore, our proposed work is to develop an mobile application environment with the general public as the frontline workers, who can capture an image or video of the scene of trafficking along with the location information. The crowd sourced data is pulled from the cloud and segregated based on location. Further the date is pre-processed using image processing algorithms specifically Convolutional Neural Network (CNN) is used. As a case study, the work identifies a missing child, by applying the crowdsourced data to the CNN based training algorithm to match with the available sources. The algorithm also classifies the gender and age using cascade classifier and physical impairments using MAX pooling filter. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine

Prediction of Apple Leaf Diseases Using Multiclass Support V...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Soarov Chakraborty Shourav Paul Md. Rahat-uz-Zaman Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Every year apple yield has been affected by Black rot and Cedar apple rust. It has a significant effect on both the apple industry and the country's economy. Here, we recommend a system to detect diseases from the infected apple leaves by combining machine learning and image processing principles. This approach can classify both infected and non-infected apple leaves efficiently. The identification is started by preprocessing the image using several image processing techniques, including the Otsu thresholding algorithm and histogram equalization. Using the image segmentation region of the infected part separates, and a Multiclass SVM recognizes the disease type from the original leaf image among 500 images with 96% accuracy. It also demonstrates the percentage of the total infected area of that diseased apple leaf image.

关键词： Support vector machines Industries image segmentation image recognition Thresholding (Imaging) signal processing algorithms Diseases

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international conference on Big Data and Cloud Computing, ICBDCC 2021

International Conference on Big Data and Cloud Computing, IC...

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international conference on Big Data and Cloud Computing, ICBDCC 2021

ISBN: (纸本)9789811921766

The proceedings contain 81 papers. The special focus in this conference is on Big Data and Cloud Computing. The topics include: Blockchain for CCTV Surveillance;Intelligent Traffic Management System Using YOLO Machine Learning Model;ioT-Based Security Camera Bot Using Raspberry Pi;performance Analysis of Different Deep Learning Models for Forest Fire Classification;Secure GEDAR Routing Protocol for Underwater Data Collection Using WSN;a Comprehensive Review on Automatic image Captioning Using Deep Learning;A GAN-Based Triplet FaceNet Detection Algorithm Using Deep Face Recognition for Autism Child;Rapid Efficient Loss Less Color image Compression Using RCT Technique and Hierarchical Prediction;anonymized Questionnaire Analysis with Differential Privacy for Large-Scale Crowdsourcing;design and Development of Web-based Photoplethysmogram signal Monitoring and Human Vital Parameters Measurement;smart Fetal Health Monitor;event Location Detection from Online Clustering algorithms Using Geo-Tagged User Data in Social Streams;smart Cyberbullying Detection with Machine Learning;tourist Sentiment Analysis Using Natural Language processing;sentiment Analysis of Twitter Data Using Machine Learning;comparative Study on Recognition of Food Item from images for Analyzing the Nutritional Contents;shot Boundary Detection and Video Captioning Using Neural Networks;Ensuring the Presence of a Person During Virtual Classes Using Histogram of Oriented Gradients (HOG) Algorithm;an Optimized K-means Clustering Approach on Top of MapReduce;identification of Alzheimer’s Disease Using Principal Component Analysis-Based Data Mining Techniques;Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN;feature Dimensionality Reduction Method on Social Network Dataset;Internet of Things (IoT) for Coronavirus (COVID-19) Pandemic: A Survey on Trailblazing Techniques;optical Character Recognition-Based Signboard Detection.

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