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

共 16347 条记录，以下是4731-4740 订阅

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Distance Estimation for Endoscopy Based on Depth Detection in the Small Intestine Using image processing techniques

Distance Estimation for Endoscopy Based on Depth Detection i...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Majid Ramedani Vahid J. Majd Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In recent years, three-dimensional measurement techniques have been widely used in medical sciences, and thus, depth detection in an image plays an important role in computer vision applications. In this paper, we discuss the estimation of the distance between the head of an endoscope and the small intestine septum and its problems. The main objective is to detect the depth of the small intestine to estimate distance. images were collected through video sampling, and then the data are preprocessed. Morphological reconstruction, bounding box, Convex Hull, and Euclidean distance are employed to update the mentioned distance. At the end of this process, the outputs are simulated, and we are given the output distance in centimeters. This method will assist the endoscope to move inside the small intestine without injuries.

关键词： Intestines Endoscopes Estimation Signal processing algorithms Measurement techniques Signal processing Intelligent systems

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SPARCLE: Stream processing Applications over Dispersed Computing Networks

SPARCLE: Stream Processing Applications over Dispersed Compu...

引用

International conference on Distributed Computing systems

作者： Parisa Rahimzadeh Jinsung Lee Youngbin Im Siun-Chuon Mau Eric C. Lee Bradford O. Smith Fatemah Al-Duoli Carlee Joe-Wong Sangtae Ha University of Colorado Boulder UNIST CACI System & Technology Research Carnegie Mellon University

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

In this paper, we propose SPARCLE, a novel scheduling system offering network-aware polynomial-time task assignment and resource allocation algorithms for stream processing applications in dispersed computing networks. In particular, we address two major challenges. The first one concerns the assignment of both computation and transport tasks comprising a stream processing application to computing nodes and communication links of the network, respectively, to maximize the application's processing rate. The second one concerns the resource allocation of multiple stream processing applications to satisfy their requested QoS. Our experimental results on a real image stream processing application and extensive simulations show that SPARCLE can increase the application's processing rate by 9 times and 3 times, compared to the cloud computing case and state-of-the-art algorithms, respectively.

关键词： Processor scheduling Heuristic algorithms Computational modeling Streaming media Dynamic scheduling Resource management Task analysis

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Multi-scale Neural Style Transfer Based on Deep Semantic Matching 4th

Multi-scale Neural Style Transfer Based on Deep Semantic Mat...

引用

4th International conference on Cognitive systems and Information processing (ICCSIP)

作者： Yu, Jiachen Jin, Li Chen, Jiayi Tian, Zhiqiang Lan, Xuguang Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9789811379864;9789811379857

Existing Neural Style Transfer (NST) algorithms do not migrate styles well to a reasonable location where the output image can render the correct spatial structure of the object being painted. We propose a deep semantic matching-based multi-scale (DSM-MS) neural style transfer method, which can achieve the reasonable transfer of styles guided by the prior spatial segmentation and illumination information of input images. First, according to real drawing process, before an artist decides how to paint a stroke, he/she needs to observe and then understand subjects, segmenting space into different regions, objects and structures and analyzing the illumination conditions on each object. To simulate the two visual cognition processes, we define a deep semantic space (DSS) and propose a method for calculating DSSs using manual image segmentation, automatic illumination estimation and convolutional neural network (CNN). Second, we define a loss function, named deep semantic loss, which uses DSS to guide reasonable style transfer. Third, we propose a multi-scale optimization strategy for improving the efficiency of our method. Finally, we achieve an interdisciplinary application of our method for the first time-painterly rendering 3D scenes by neural style transfer. The experimental results show that our method can synthesize images in better original structures, with more reasonable placement of each styles and visual aesthetic feeling.

关键词： Neural style transfer image segmentation Illumination estimation Patch matching

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Research Of Text Data processing algorithms In Social Networks

Research Of Text Data Processing Algorithms In Social Networ...

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Information Technology and Nanotechnology (ITNT), International conference on

作者： Yuriy Kurbatov Igor Rytsarev Alexander Kupriyanov Samara National Research University Samara Russia Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and Photonics" RAS Samara National Research University Samara Russia

ISBN: (数字)9781728170411

ISBN: (纸本)9781728170428

In this paper, we investigate various clustering algorithms for a large amount of text data. An analysis of the existing implementation methods was carried out and the algorithms Word2Vec and GloVe were selected. The initial textual data for testing the algorithms were obtained by collecting records from open VKontakte communities. The results showed that the use of these algorithms allows us to assess the frequency of use and the significance of individual words relative to the context of the studied community. The results of the algorithms applications were compared and the conclusion about their efficiency was made in the work as well.

关键词： Social networking (online) Software packages Web and internet services Software algorithms Clustering algorithms Classification algorithms Testing

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Uncertainty-driven loss for single image super-resolution 21

Uncertainty-driven loss for single image super-resolution

引用

Proceedings of the 35th International conference on Neural Information processing systems

作者： Qian Ning Weisheng Dong xin Li Jinjian Wu Guangming Shi School of Artificial Intelligence Xidian University Xi'an China Lane Dep. of CSEE West Virginia University Morgantown WV

ISBN: (纸本)9781713845393

In low-level vision such as single image super-resolution (SISR), traditional MSE or L1 loss function treats every pixel equally with the assumption that the importance of all pixels is the same. However, it has been long recognized that texture and edge areas carry more important visual information than smooth areas in photographic images. How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty. Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously. Moreover, uncertainty estimation allows us to leverage conventional wisdom such as sparsity prior for regularizing SISR solutions. Ultimately, pixels with large certainty (e.g., texture and edge pixels) will be prioritized for SISR according to their importance to visual quality. For the first time, we demonstrate that such uncertainty-driven loss can achieve better results than MSE or L1 loss for a wide range of network architectures. Experimental results on three popular SISR networks show that our proposed uncertainty-driven loss has achieved better PSNR performance than traditional loss functions without any increased computation during testing.

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Utilizing Deep Learning Technology to Segment Pectoral Muscle in Mediolateral Oblique View Mammograms

Utilizing Deep Learning Technology to Segment Pectoral Muscl...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Wei Liu Chengqian Liu Yiran Wei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women all over the world. Accurate pectoral muscle segmentation in mediolateral oblique(MLO) view mammograms is an important pre-processing step for subsequent analysis in computer aided diagnosis (CAD) systems. In this paper, a deep learning based approach for pectoral muscle segmentation is proposed. The method includes the following steps. First, the artifacts in the mammogram is eliminated by applying connected component labeling. Second, a pre-trained U-Net model is utilized to get the initial pectoral muscle segmentation result. Finally, an improved scanline filling method is employed to solve the under-segmentation problem existing in initial segmentation result. Preliminary investigation on a test dataset demonstrated that the proposed method yielded average values of 93.5 ± 3.0% and 88.0 ± 5.0% for the Dice and Jaccard similarity metrics, respectively.

关键词： Deep learning Measurement image segmentation Solid modeling Muscles Mammography Labeling

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Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm

Simple and Efficient Clustering Approach Based on Cuckoo Sea...

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International conference on Intelligent Computing in Data Sciences (ICDS)

作者： Lahbib KHRISSI Nabil EL AKKAD Hassan SATORI Khalid SATORI LISAC Faculty of Sciences Dhar-Mahraz (FSDM) Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory of Engineering Systems and Applications ENSA of Fez.

ISBN: (数字)9781728180847

ISBN: (纸本)9781728180854

image segmentation is the most important operation in the image processing system because it is located at the articulation between image processing and analysis. The advantage of segmentation is to partition an image into several homogeneous regions, within the meaning of a criterion fixed a priori. A multitude of segmentation methods are proposed in the literature, but there is no universal image segmentation technique to apply to all different types of images and in any given computer context. Because of these constraints, in this paper, we will propose a new image segmentation approach which is based on the hybridization of an unsupervised classification method which is fuzzy C-means (FCM) and a metaheuristic which is called Cuckoo Search Algorithm (CSA). In our proposed approach, the cuckoo search is used to find the optimal partitioning according to an objective function which is based on the indices of validity of the clusters. First, CSA is initialized with random cluster centers. The cluster centers are then updated using the CSA principles aimed at minimizing the objective function proposed. The performance of the proposed approach was measured on several images and compared to other existing FCM techniques such as standard FCM and FCM based on genetic algorithms (FCM-GA). The experimental results show that the proposed approach yields satisfactory results in terms of precision, simplicity and efficiency.

关键词： Clustering algorithms image segmentation Classification algorithms Partitioning algorithms Optimization Birds Linear programming

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Astronomical image Restoration and Point Spread Function Estimation with Deep Neural Networks

Astronomical Image Restoration and Point Spread Function Est...

引用

conference on Advances in Optical Astronomical Instrumentation

作者： Jia, Peng Wu, Xuebo Yang, xiaoshan Huang, Yi Cai, Bojun Cai, Dongmei Taiyuan Univ Technol Coll Phys & Optoelect Taiyuan 030024 Peoples R China Univ Durham Dept Phys South Rd Durham DH1 3LE England Taiyuan Univ Technol Minist Educ & Shanxi Prov Key Lab Adv Transducers & Intelligent Control Sys Taiyuan 030024 Peoples R China

ISBN: (纸本)9781510631472

From ground-based extremely large telescopes to small telescope arrays used for time domain astronomy, point spread function plays an important role both for scientific data post-processing and instrument performance estimation. In this paper, we propose a new method which can restore astronomical images and obtain the point spread function of the whole optical system at the same time. Our method uses simulated high resolution astronomical images and real observed blurred images to train a deep neural network (Cycle-GAN). The Cycle-GAN contains a pair of generative adversarial neural networks and each generative adversarial neural network contains a generator and a discriminator. After training, one generator (PSF-Gen) can learn the point spread function and the other generator (Dec-Gen) can learn the deconvolution kernel. We test our method with real observation data from solar telescope and small aperture telescopes. We find that the Dec-Gen can give promising restoration results for solar images and can reduce the PSF spatial variation for images obtained by smaller telescopes. Besides, we also find that the PSF-Gen can provide a non-parametric PSF model for short exposure images, which would then be used as prior model for PSF reconstruction algorithms in adaptive optics systems.

关键词： Point spread function reconstruction Deep neural network Cyclegan image restoration

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Data Flow Mapping onto DNN Accelerator Considering Hardware Cost

Data Flow Mapping onto DNN Accelerator Considering Hardware ...

引用

Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

作者： Baharealsadat Parchamdar Midia Reshadi Islamic Azad University Tehran Iran

ISBN: (数字)9781728185101

ISBN: (纸本)9781728185118

Deep Neural Networks (DNNs) has created outstanding results in computer vision and image processing recently. Computational algorithms complexity has led to widespread research in this field. Besides, using a DNN accelerator is an effective method in order to hasten the computation in those algorithms. The majority of DNN accelerator use parallelism processing Elements (PEs) in order to lessen hardware costs. Regarding high volume of input data in image processing, lessening computing runtime is considered as an open challenge in DNNs accelerator. All above-mentioned issues have created different data flow mapping method in DNNs accelerator. This paper presents the new data flow mapping on Resnet34, regarding stationaries concepts; moreover, evaluates computing runtime in each step of mapping. We also propose a new data flow mapping method based on weight stationary in Resnet34, which reduces computing runtime, by 50.6%.

关键词： Computer vision Runtime image processing Neural networks Parallel processing Hardware Proposals

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Unsupervised Machine Learning Algorithm for MRI Brain image processing 7th

Unsupervised Machine Learning Algorithm for MRI Brain Image ...

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International conference on Soft Computing for Problem Solving (SocProS)

作者： Rani, S. Saradha Rao, G. Sasibhushana Rao, B. Prabhakara GITAM Univ Dept Elect & Commun Engn Visakhapatnam Andhra Pradesh India AU Coll Engn Visakhapatnam Andhra Pradesh India JNTUK Kakinada 533003 Andhra Pradesh India

ISBN: (纸本)9789811315923;9789811315916

Denoising of an image is the first and primary pre-processing step in image processing. In this paper, an algorithm is implemented using machine learning in conjunction with wavelet-based denoising method. Most learning algorithms use activation function that is continuously differentiable. Since standard threshold functions are weakly differentiable, a new type of thresholding function was proposed. Stein's unbiased risk estimate (SURE)-based updating algorithm is used for estimation. The proposed method is compared with conventional filtering andwavelet-based denoising methods, using performance evaluators like PSNR and MSE. Results indicate there is a significant reduction in MSE and increase in PSNR for the proposed method.

关键词： image denoising SURE Thresholding Unsupervised learning

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