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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16444 条记录，以下是4711-4720 订阅

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On The Efficiency of Heterogeneous System Architecture for image processing

On The Efficiency of Heterogeneous System Architecture for I...

引用

conference on Information Communication Technology and Society (ICTAS)

作者： Shaylin Chetty Simon Winberg Department of Electrical Engineering University of Cape Town Cape Town South Africa

ISBN: (数字)9781728137704

ISBN: (纸本)9781728137711

Graphics processing Unit (GPU) based image processing algorithms have been previously developed to take advantage of the highly parallel nature of GPUs. However, these algorithms still exhibit problems of high programming complexity, relatively low device utilisation and difficulty when integrating into larger systems. In this paper, a set of image processing modules have been developed to take advantage of the computational characteristics of a System on a Chip (SoC) that contains both a GPU and a Central processing Unit with fine grained shared virtual memory capabilities. The usage of shared memory simplifies design and removes the latency and bandwidth constraints associated with discrete GPUs on the Peripheral Component Interconnect Express (PCIe) bus. These modules feature a simple, composite design that improves upon previously developed algorithms by running discrete stages of the algorithms on the portions of the SoC that are best suited for them. This allows greater efficiency and lower code complexity than more expensive discrete-GPU-based alternatives.

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Multi-Task Active Learning for Simultaneous Emotion Classification and Regression

Multi-Task Active Learning for Simultaneous Emotion Classifi...

引用

IEEE International conference on systems, Man and Cybernetics

作者： Xue Jiang Lubin Meng Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442084

Emotion recognition, which aims to identify an individual’s emotional state from the acquired physiological or body signals, is very important in affective computing. Emotions have two common representations: categorical, e.g., happy, sad, etc., and dimensional (continuous), e.g., valence, arousal and dominance. Training a good emotion classification or regression model usually requires a large number of labeled data. However, the labeling process is very difficult. As emotions are subtle and uncertain, it usually requires multiple assessors to label each emotional instance to obtain the groundtruth categorical label or dimensional values. In this paper, we propose a multi-task active learning (MTAL) framework to query the most useful samples for labeling, which enables the efficient training of an emotion classification model and multiple emotion regression models simultaneously. This is novel and challenging, as all previous research considered only emotion classification or regression alone, but not simultaneously. Experimental results on the IEMOCAP dataset demonstrated that MTAL outperformed random selection and several state-of-the-art single task active learning approaches, i.e., with the same number of labeled samples, MTAL can obtain better emotion classification and regression models simultaneously.

关键词： Training Emotion recognition conferences Multitasking Physiology Data models Classification algorithms

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Automatically Designing CNN Architectures for Acoustic Scene Classification

Automatically Designing CNN Architectures for Acoustic Scene...

引用

International conference on Computer Engineering and systems

作者： Noha W. Hasan Ali S. Saudi Mahmoud I. Khalil Hazem M. Abbas Ain Shams University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Qalubiya Egypt

ISBN: (纸本)9781665408684

Deep convolutional neural networks (CNNs) have proven their effectiveness in the acoustic scene classification (ASC) task, becoming the state-of-the-art solution to build pow-erful ASC models. Usually, the architectures of these networks and their hyper-parameters have been manually designed by expert researchers in both the investigated problem and CNN architecture design, which can be a challenging task. In this work, we propose a genetic algorithm (GA) that benefits from the characteristics of state-of-the-art CNN architectures in the field of ASC to specifically optimize CNN architectures for the ASC task, within an acceptable search cost compared to other GAs. After searching for 14 GPU days using a single NVIDIA Tesla K80 GPU on the development dataset of DCASE2020 Task lA, the proposed GA has optimized a CNN architecture that achieved a test accuracy of 68.5 % on the same dataset. The same network has been evaluated on the development dataset of DCASE2018 Task 1A and achieved a classification accuracy of 73.7% without repeating the search process, which demonstrates the generalization capability of the generated CNNs.

关键词： image analysis Neural networks Graphics processing units Computer architecture Acoustics Classification algorithms Convolutional neural networks

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Parallel Multithreaded Medical images Filtering

Parallel Multithreaded Medical Images Filtering

引用

International conference on Computational Science and Computational Intelligence (CSCI)

作者： Veska Gancheva Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781665458429

The quality of medical images is paramount. Being of high grade, it guarantees the quality of medical diagnosis, treatment and quality of patient’s life through the means of health care or using automate intelligent systems for medical diagnosing, treatment and monitoring. The paper presents the computational challenges in medical images processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for medical images filtering. Parallel computational model based on two-dimensional filters is designed. The proposed parallel model is verified by multithreaded parallel program implementation. An investigation of the efficiency of medical images filters based on parallel multithreaded program implementation, applying two-dimensional filters on a given list of compressed jpeg medical images and generating output jpeg images for each type of applied filter. The applied filters are Brightness Control, horizontal and vertical filter of Sobel, Laplace and Blur. A number of experiments have been carried out for the case of dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x and various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability.

关键词： Scientific computing Computational modeling Scalability Instruction sets Filtering algorithms image filtering Medical diagnosis

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Application of Blind Signal processing in image Watermarking systems

Application of Blind Signal Processing in Image Watermarking...

引用

International SoC Design conference, ISOCC

作者： Chuen-Yau Chen Cheng-Yuan Lin Yi-Ze Zou Hung-Ming Hsiao Yen-Ting Chen National University of Kaohsiung Kaohsiung Taiwan National Taiwan University of Science & Technology Taipei Taiwan

ISBN: (数字)9781728183312

ISBN: (纸本)9781728183329

In this paper, an efficient blind signal processing algorithm called relative-gradient bound component analysis algorithm that combines the concepts of the relative-gradient approach and the bound-component analysis is applied to the image watermarking systems. Unlike to the other blind-signal-processing algorithms, neither the inverse matrix nor the covariance matrix is necessary to be computed in this one. It can successfully extract the embedded watermark without the whittling process. The time complexity and the space complexity of this algorithm are both lower than those of the original bound component analysis algorithm.

关键词： Signal processing algorithms Watermarking Signal processing Complexity theory Covariance matrices Time complexity

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Traditional Bulgarian Costume Classification in a Tourist Guide application

Traditional Bulgarian Costume Classification in a Tourist Gu...

引用

2020 International conference Automatics and Informatics, ICAI 2020

作者： Staynov, Stefan Milev, Kristian Kostadinov, Georgi Stoyanova-Doycheva, Asya Faculty of Mathematics and Informatics Plovdiv University Paisii Hilendarski Department of Computer Systems Plovdiv Bulgaria

ISBN: (纸本)9781728193083

Bulgaria is a culturally rich country which has been kept unexplored for years. Modern information and communication technologies can shrink the gap between tourists and its rich historical heritage. A digital tourist guide has been in development that offers its users a tailored experience and guides them through their journey in Bulgaria. Users are able to take a picture of a traditional Bulgarian costume and using various machine learning algorithms the tourist guide is able to recognize it and present interesting information about the photographed object. The following article gives an overview of the tourist guide architecture and image classification process. The authors used a convolutional neural network to classify the uploaded images of traditional Bulgarian costumes. The lack of dataset for training the network is solved by collecting the user-uploaded images, classifying them and using them for improving the classification algorithm automatically. Through this process a lifelong learning has been achieved. © 2020 IEEE.

关键词： image classification

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Real-time image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Real-time Image Enhancement for Vision-based Autonomous Unde...

引用

International conference on Underwater Networks and systems (WUWNET)

作者： Chen, Wenjie Rahmati, Mehdi Sadhu, Vidyasagar Pompili, Dario Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ USA

ISBN: (纸本)9781450377409

Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.

关键词： Underwater image processing image dehazing image enhancement Generative Adversarial Networks (GANs)

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BUILDING AN ACTIVE PALMPRINT RECOGNITION SYSTEM 26

BUILDING AN ACTIVE PALMPRINT RECOGNITION SYSTEM

引用

26th IEEE International conference on image processing (ICIP)

作者： Du, Xuefeng Zhong, Dexing Shao, Huikai Xi An Jiao Tong Univ Sch Elect & Informat Engn 28 West Xianning Rd Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538662496

Palmprint recognition allows accurate identity verification to build a security system. Recently, researchers introduce deep learning to this area that largely improves the recognition accuracy. However, as a supervised approach, its performance relies on availability of data and labels for every registered identity. For large-scale security systems, after image acquisition, we need to check the whole dataset and manually assign labels through comparison, which is a time-consuming task. Besides, labelling some redundant training samples contributes little to the recognition result. In this paper, we introduce an active learning framework to select the best sample set for label assignment. We regard the active learning as a binary classification task and attempt to make the labeled and unlabeled set indistinguishable. Experiments on different datasets demonstrate our model can reduce the annotation cost while achieving comparable recognition performance.

关键词： Biometrics Palmprint Recognition Active Learning Information Security

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Canonical images 30

Canonical Images

引用

30th conference on Infrared Imaging systems - Design, Analysis, Modeling, and Testing

作者： Jacobs, Eddie L. Bahhur, Bassam Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9781510626683

Most forms of optical image formation involve the use of an optical system to form a real image on an array of sensing elements. The output from the sensing elements is a sampled image. Mathematically, this process is described by convolution of the point spread function of the system (including the sensing elements) and the projection of objects in the image plane. In general, this process cannot be done mathematically in closed form for arbitrary images. Sampling and image processing algorithms are often assessed with respect to their performance on sampled images that are accepted standards. By applying known degradations such as noise and blur to a standard image, operating on the degraded image with a prospective algorithm, and comparing the result with the original uncorrupted image, an image processing algorithm or sampling scheme can be assessed. A weakness of this approach is the fact that the accepted standard is just that: an accepted standard. The image contains within itself uncertainties associated with the original image acquisition process. These uncertainties place bounds on the utility of the image. In this research we introduce the concept of canonical images. Canonical images are closed form, mathematically computable images that retain the essentials of the linear shift invariant image formation process. We derive one form of a canonical image, show its properties, and show how complex images can be generated using superposition. We also demonstrate how arbitrary images can be decomposed into canonical images that approximate them. We discuss applications for canonical images that include modeling and simulation, sensor testing, perception testing, and algorithm development.

关键词： image processing Point spread functions image resolution Distortion image acquisition Convolution Detection and tracking algorithms Imaging systems Sensing systems Optical components

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Test Input Partitioning for Automated Testing of Satellite On-board image processing algorithms 14

Test Input Partitioning for Automated Testing of Satellite O...

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14th International conference on Software Technologies (ICSOFT)

作者： Witteck, Ulrike Griessbach, Denis Herber, Paula German Aerosp Ctr DLR Inst Opt Sensor Syst Berlin Germany Univ Munster Embedded Syst Grp Munster Germany

ISBN: (纸本)9789897583797

On-board image processing technologies in the satellite domain are subject to extremely strict requirements with respect to reliability and accuracy in hard real-time. Due to their large input domain, it is infeasible to execute all possible test cases. To overcome this problem, we define a novel test approach that efficiently and systematically captures the input domain of satellite on-board image processing applications. To achieve this, we first present a dedicated partitioning into equivalence classes for each input parameter. Then, we define multidimensional coverage criteria to assess a given test suite for its coverage on the complete input domain. Finally, we present a test generation algorithm that automatically inserts missing test cases into a given test suite based on our multidimensional coverage criteria. This results in a reasonably small test suite that covers the whole input domain of satellite on-board image processing applications. We demonstrate the effectiveness of our approach with experimental results from the ESA medium-class mission PLATO.

关键词： image processing Software Testing Input Partitioning Satellite systems

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