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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是61-70 订阅

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Low level feature selection for a content based digital mammography image retrieval system

Low level feature selection for a content based digital mamm...

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2009 IEEE 17th Signal processing and Communications Applications conference, SIU 2009

作者： Öztürk, Özlem Bulu, Hakan Alpkoçak, Adil Güzelis, Cüneyt Bilgisayar Mühendisligi Bölümü Turkey Elektrik ye Elektronik Mühendisligi Bölümü Dokuz Eylül Üniversitesi Turkey

ISBN: (纸本)9781424444366

Content Based image Retrieval (CBIR) systems enables to retrieve images from large image archieves based on its contents as well as external attributes associated to each image. this study aims at extracting low level attributes to be used in a CBIR model that enables the utilization of low level image based attributes together with high level concepts. the contribution of this study is to develop an infrastructure for the selection of best low level attribute set to be used in the CBIR method by considering model performance. Within the scope of this study: segmentation of mammogram images, development of a mammogram database, low level attribute extraction from the segmented images and breast type estimation by means of machine learning algorithms are realized. ©2009 IEEE.

关键词： Learning algorithms

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

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17th International conference on Interfaces and Human Computer Interaction 2023, IHCI 2023, the 17th International conference on Computer Graphics, Visualization, Computer Vision and image processing 2023, CGVCVIP 2023 and 16th International conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. this is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). the second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. these databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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Investigating image processing algorithms for Navigating Cultural Heritage Spaces Using Mobile Devices 17th

Investigating Image Processing Algorithms for Navigating Cul...

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17th International conference on Asia-Pacific Digital Libraries (ICADL)

作者： Olojede, Ayodeji Suleman, Hussein Univ Cape Town Dept Comp Sci ZA-7700 Cape Town South Africa

ISBN: (纸本)9783319279749;9783319279732

the use of mobile devices is increasing in the cultural heritage and museum context. the most common approach is to provide a customized mobile device to the museum visitor to navigate museum spaces. In this paper, a mobile cultural heritage guide is presented, which enables image based navigation of rock art sites using computer vision and image processing algorithms for rock art image feature detection and extraction. Traditionally such systems have used algorithms such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented Fast and Rotational Brief (ORB). the three algorithms have been integrated in a prototype and their performance has been evaluated. It was observed that digital recognition of rock art images is possible under certain image preprocessing conditions. Also, the evaluation result shows that, generally, SIFT has good accuracy and, when used in conjunction with K Nearest Neighbor, has acceptable matching speed.

关键词： Cultural heritage Rock art images Content based image retrieval system

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Real-time trajectory similarity processing using longest common subsequence 21

Real-time trajectory similarity processing using longest com...

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21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Ding, Jiafeng Fang, Junhua Zhang, Zonglei Zhao, Pengpeng Xu, Jiajie Zhao, Lei School of Computer Science Technology Soochow University Suzhou China

ISBN: (纸本)9781728120584

Driven by the fast development of mobile internet and mobile devices, huge volumes of trajectory data describing the spatial-temporal information of moving objects are currently generated. Trajectory similarity processing is playing a critical role to explore valuable information, such as the patterns of human activities and behaviors. However, most of the existing works optimize this operation in an off-line manner, which is unable to provide timely feedback for time-sensitive applications. In this paper, we present a new real-time processing framework for trajectory similarity measurement, with practical algorithms to reduce the computing resource cost and guarantee the correctness of the result. Our proposal includes a cost-effective framework to ensure the correctness of trajectory similarity processing, a stream partition algorithm to maximize the processing throughput, a lightweight Top-k computing mode to cut off unnecessary network cost. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the huge advantage of our approaches over state-of-the-art solutions in the literature. © 2019 IEEE.

关键词： Cost effectiveness

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Wireless Capsule Endoscopic image Enhancement Method Based on Histogram Correction and Unsharp Masking in Wavelet Domain 17

Wireless Capsule Endoscopic Image Enhancement Method Based o...

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17th IEEE International New Circuits and systems conference (NEWCAS)

作者： Long, Mingzhu Xie, Xiang Li, Guolin Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728110318

Due to the limited illumination and complex environment in the gastrointestinal (GI) tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. In order to solve this problem, a method based on histogram correction and unsharp masking in the wavelet domain is proposed in this paper, to improve the contrast and reveal details of endoscopic images. the HSI color space is adopted for color naturalness, and a logarithm based histogram correction method (LHCM) is proposed to improve the contrast and avoid aritifacts. Details are further enhanced by unsharp masking. Experimental results show that performance of the proposed method is better compared with the state-of-art methods. It can be applied to general images and have good effects as well.

关键词： wireless capsule endoscopy image enhancement logarithm transformation histogram equalization unsharp masking

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Energy performance profiling of a GPU-based CPM implementation 17

Energy performance profiling of a GPU-based CPM implementati...

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17th International conference on Signal-image Technology and Internet-Based systems, SITIS 2023

作者： Luca, Pasquale De Galletti, Ardelio Marcellino, Livia Parthenope University of Naples Science and Techonologies Department Naples Italy

ISBN: (纸本)9798350370911

In recent years, there has been a growing interest in the development of in vitro models to predict cellular behavior within living organisms. Mathematical models, based on differential equations and associated numerical algorithms, have been employed for this purpose. In this study, we present initial forays into the design of parallel strategies aimed at accelerating an algorithm for behavior prediction, specifically based on the Cellular Potts Model. To do this, we engage the computational power of Graphic processing Units within the CUDA environment to optimize critical low-level *** work intends to provide a comprehensive analysis of the energy performance of the proposed implementation. Tests and experiments affirm significant performance gains in terms of both processing time and substantial energy savings. © 2023 IEEE.

关键词： Graphics processing unit

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Automatic Brightness Adjustment System by Fuzzy Inference System for Object Recognition 17

Automatic Brightness Adjustment System by Fuzzy Inference Sy...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Kim, Eun Kyeong Lee, Hansoo Kim, Sungshin Cho, Hyunhak Pusan Natl Univ Dept Elect & Comp Engn Busan South Korea Pusan Natl Univ Dept Interdisciplinary Cooperat Course Robot Busan South Korea

ISBN: (纸本)9781509049172

A camera has been widely used in practical fields with a diversity of purposes recently. there is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L*a*b* color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L*a*b* color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Factorization with uncertainty and missing data: exploiting temporal coherence

Factorization with uncertainty and missing data: exploiting ...

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17th Annual conference on Neural Information processing systems (NIPS)

作者： Gruber, A Weiss, Y Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel

ISBN: (纸本)0262201526

the problem of "Structure From Motion" is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of the points. Under simplified camera models, the problem reduces to factorizing a measurement matrix into the product of two low rank matrices. Each element of the measurement matrix contains the position of a point in a particular image. When all elements are observed, the problem can be solved trivially using SVD, but in any realistic situation many elements of the matrix are missing and the ones that are observed have a, different directional uncertainty. Under these conditions, most existing factorization algorithms fail while human perception is relatively unchanged. In this paper we use the well known EM algorithm for factor analysis to perform factorization. this allows us to easily handle missing data and measurement uncertainty and more importantly allows us to place a prior on the temporal trajectory of the latent variables (the camera position). We show that incorporating this prior gives a significant improvement in performance in challenging image sequences.

关键词： image enhancement

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the vehicle classification based on neural networks in intelligent transport systems 17

The vehicle classification based on neural networks in intel...

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17th IEEE International conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Frniak, M. Kamencay, P. Markovic, M. Dubovan, J. Dado, M. University of Zilina Department of Multimedia and Information-communication Technologies Zilina Slovakia

ISBN: (纸本)9781728149677

this article is aimed to new methods of measurement, transfer and processing of data from complex system of FibreBragg Grating (FBG) sensors. State of the art technologies and algorithms for vehicle recognition and categorisation in university campus are used. Content of the Article is divided into two parts. the first part is devoted to fibre optic FBG sensors and their basic properties. the second part ofarticle is dedicated to visual data processing by neural networks for visual vehicle categorisation as visual confirmation of data from the fibre Bragg grating sensor arrays. Experimental results show that using AlexNet neural network can be achieved an accuracy for categorization of vehicles of 87.1%. Research is done under the grant project dealing with coexistence of photonic sensor systems and photonic networks within the Internet of things. © 2019 IEEE.

关键词： Vehicles

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***: A Zero-Energy Neural Network Accelerator for Embedded Sensing and Inference systems 19

***: A Zero-Energy Neural Network Accelerator for Embedded S...

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17th ACM conference on Embedded Networked Sensor systems (SenSys)

作者： Lee, Seulki Nirjon, Shahriar Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9781450369503

We introduce *** a co-processor architecture consisting of a main microcontroller (MCU) that executes scaled-down versions of a deep neural network(1) (DNN) inference task, and an accelerator microcontroller that is powered by harvested energy and follows the intermittent computing paradigm [76]. the goal of the accelerator is to enhance the inference performance of the DNN that is running on the main microcontroller. *** opportunistically accelerates the run-time performance of a DNN via one of its four acceleration modes: extended inference, expedited inference, ensemble inference, and latent training. To enable these modes, we propose two sets of algorithms: (1) energy and intermittence-aware DNN inference and training algorithms, and (2) a fast and high-precision adaptive fixed-point arithmetic that beats existing floating-point and fixed-point arithmetic in terms of speed and precision, respectively, and achieves the best of both. To evaluate ***, we implement low-power image and audio recognition applications and demonstrate that their inference speedup increases by 1.6x and 1.7x, respectively, and the inference accuracy increases by 10% and 16%, respectively, when compared to battery-powered single-MCU systems.

关键词： Accelerator Deep neural networks Zero energy Batteryless

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