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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

共 650 条记录，以下是111-120 订阅

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TOPOLOGY-PRESERVING SHAPE-BASED REGRESSION OF RETINAL LAYERS IN OCT image DATA USING CONVOLUTIONAL NEURAL NETWORKS 16

TOPOLOGY-PRESERVING SHAPE-BASED REGRESSION OF RETINAL LAYERS...

引用

16th IEEE International symposium on Biomedical Imaging (ISBI)

作者： Kepp, Timo Ehrhardt, Jan Heinrich, Mattias P. Huettmann, Gereon Handels, Heinz Univ Lubeck Inst Med Informat Lubeck Germany Univ Lubeck Grad Sch Comp Med & Life Sci Lubeck Germany Univ Lubeck Inst Biomed Opt Lubeck Germany

ISBN: (纸本)9781538636411

Optical coherence tomography (OCT) is a non-invasive imaging modality that provides cross-sectional 3D images of biological tissue. Especially in ophthalmology OCT is used for the diagnosis of various eye diseases. Automatic retinal layer segmentation algorithms, which are increasingly based on deep learning techniques, can support diagnostics. However, topology properties, such as the order of retinal layers, are often not considered. In our work, we present an automatic segmentation approach based on shape regression using convolutional neural networks (CNNs). Here, shapes are represented by signed distance maps (SDMs) that assign the distance to the next object contour to each pixel. thus, spatial regularization is introduced and plausible segmentations can be produced. Our method is evaluated on a public OCT dataset and is compared with two classification-based approaches. the results show that our method has fewer out-liers with comparable segmentation performance. In addition, it has an improved topology preservation, which saves further post-processing.

关键词： image segmentation regression topology OCT retina

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SPECTRAL CT RECONSTRUCTION VIA SELF-SIMILARITY IN image-SPECTRAL TENSORS 16

SPECTRAL CT RECONSTRUCTION VIA SELF-SIMILARITY IN IMAGE-SPEC...

引用

16th IEEE International symposium on Biomedical Imaging (ISBI)

作者： Xia, Wenjun Wu, Weiwen Liu, Fenglin Yu, Hengyong Zhou, Jiliu Wang, Ge Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China Univ Massachusetts Dept Elect & Comp Engn Lowell MA 01854 USA Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA

ISBN: (纸本)9781538636411

Spectral computed tomography (CT) reconstructs multi energy images from data in different energy bins. these reconstructed images can be contaminated by noise due to the limited numbers of photons in the corresponding energy bins. In this paper, we propose a spectral CT reconstruction method aided by self-similarity in image-spectral tensors (ASSIST), which utilizes the self-similarity of patches in both spatial and spectral domains Patches with similar structures identified by a joint spatial and spectral searching strategy form a basic tensor unit, and can be utilized to improve image quality. Specifically, each tensor is decomposed into a low-rank component and a sparse component, which respectively represent the stable structures and feature differences across different energy bins. the experimental results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms.

关键词： Spectral CT low-rank decomposition sparse representation tensor

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Semantic segmentation of hands in multimodal images: A region new-based CNN approach 16

Semantic segmentation of hands in multimodal images: A regio...

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16th IEEE International symposium on Biomedical Imaging, ISBI 2019

作者： Pemasiri, Akila Ahmedt-Aristizabal, David Nguyen, Kien Sridharan, Sridha Dionisio, Sasha Fookes, Clinton Image and Video Research Laboratory Queensland University of Technology Australia Department of Mater Advanced Epilepsy Unit Mater Centre for Neurosciences Australia

ISBN: (纸本)9781538636411

Segmentation of body parts is a critical but challenging stage in the medical image processing pipeline due to the anatomical complexity. Recent advances in deep learning have successfully dealt with this complexity in visible images. However, the efficacy of these segmentation techniques for other modalities of interests such as X-ray images is not known. We propose a unified semantic segmentation approach of body parts for both X-ray and visible images which can be concurrently applied to both modalities. Unifying the two modalities in a single model not only reduces the number of parameters, but also improves the accuracy by enabling end-to-end training and inference. Quantitative results are validated on two clinical applications: (1) a static analysis of hand segmentation in visible and X-ray images;and (2) a dynamic analysis which quantifies and classifies epileptic seizures from clinical manifestations of visible hand and finger motions. the proposed model is a potential stepping stone towards developing more robust automated systems that support the assessment of medical conditions based on multimodal information. © 2019 IEEE.

关键词： Motion analysis

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Blockchain-based access control schemes for medical image analysis system 10th

Blockchain-based access control schemes for medical image an...

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10th International symposium on Parallel Architectures, algorithms and Programming, PAAP 2019

作者： Liu, Tonglai Wu, Jigang Zhang, Xinpeng Peng, Zhihao Li, Jingyi School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

Medical image analysis systems with machine learning have played an important role in the computer-aided diagnosis and treatment for diseases. However, individual privacy of user data is vulnerable since the training data is exposed to unauthorized user. therefore, this paper designs an access control scheme to prevent illegal users from accessing medical data while achieving high accuracy of lesion classification. Specifically, in the novel lightweight consortium blockchain-based access control scheme, a chosen consortium node is utilized as key generation center instead of a trusted third party in conventional schemes. Two public retinal datasets are utilized for the classification of diabetic retinopathy (DR). Security analysis shows that the proposed scheme can prevent the user data from leakage and malicious tampering. Experimental results demonstrate that the processing of data cleaning is efficient to increase the accuracy of the classification for early lesions of DR by removing low quality images, and the accuracy is up to 90.2%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Blockchain

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Can Exposure, Noise and Compression affect image Recognition? An Assessment of the Impacts on State-of-the-art ConvNets 16

Can Exposure, Noise and Compression affect Image Recognition...

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16th Latin American Robotics symposium (LARS) / 7th Brazilian symposium on Robotics (SBR) / 10th Workshop on Robotics in Education (WRE)

作者： Steffens, C. R. Messias, L. R., V Drews-Jr, P. L. J. Botelho, S. S. d C. Univ Fed Rio Grande FURG Ctr Comp Sci Av Italia Km8 Rio Grande Brazil

ISBN: (纸本)9781728142685

Convolutional Neural Networks stand the current state-of-the-art in image recognition, as well as many computer vision tasks. Nevertheless, these architectures have been shown to be vulnerable to image manipulations, which may undermine the reliability and safety of CNN-based models in autonomous and robotic applications. We present a rigorous evaluation of the robustness of several high-level image recognition models and investigate their performance under distinct image distortions. We propose a testing framework which emulates ill exposure conditions, low-range image sensors, lossy compression, as well as commonly observed noise types. One one side results measured in terms of accuracy, precision, and F1-Score, indicate that most CNN models are marginally affected by mild miss-exposure, heavy compression, and Poisson noise. Severe miss-exposure, impulse noise, or signal-dependent noise, on the other side, show a substantial drop in accuracy and precision. A careful evaluation of some typical image distortions, commonly observed in computer vision and machine vision pipelines, provides insights and directions for further developments in the field.

关键词： Autonomous systems Deep learning image processing image recognition image restoration Noise removal Robotics

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EXPLOITING VISUAL AND REPORT-BASED INFORMATION FOR CHEST X-RAY ANALYSIS BY JOINTLY LEARNING VISUAL CLASSIFIERS AND TOPIC MODELS 16

EXPLOITING VISUAL AND REPORT-BASED INFORMATION FOR CHEST X-R...

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16th IEEE International symposium on Biomedical Imaging (ISBI)

作者： Daniels, Zachary A. Metaxas, Dimitris N. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9781538636411

Manual examination of chest x-rays is a time consuming process that involves significant effort by expert radiologists. Recent work attempts to alleviate tins problem by developing learning-based automated chest x-ray analysis systems that map images to multi-label diagnoses using deep neural networks. these methods are often treated as black boxes, or they output attention maps but don't explain why the attended areas are important. Given data consisting of a frontal-view x-ray, a set of natural language findings, and one or more diagnostic impressions, we propose a deep neural network model that during training simultaneously 1) constructs a topic model which clusters key terms from the findings into meaningful groups, 2) predicts the presence of each topic for a given input image based on learned visual features, and 3) uses an image's predicted topic encoding as features to predict one or more diagnoses. Since the net learns the topic model jointly with the classifier, it gives us a powerful tool for understanding which semantic concepts the net might be exploiting when making diagnoses, and since we constrain the net to predict topics based on expert-annotated reports, the net automatically encodes some higher-level expert knowledge about how to make diagnoses.

关键词： Chest X-Ray Analysis Multimedia Analysis Natural Language processing Deep Learning

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Tiresias: A GPU Cluster Manager for Distributed Deep Learning 16

Tiresias: A GPU Cluster Manager for Distributed Deep Learnin...

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16th USENIX symposium on Networked systems Design and Implementation

作者： Gu, Juncheng Chowdhury, Mosharaf Shin, Kang G. Zhu, Yibo Jeon, Myeongjae Qian, Junjie Liu, Hongqiang Guo, Chuanxiong Univ Michigan Ann Arbor MI 48109 USA Microsoft Redmond WA USA Bytedance Beijing Peoples R China UNIST Ulsan South Korea Alibaba Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781931971492

Deep learning (DL) training jobs bring some unique challenges to existing cluster managers, such as unpredictable training times, an all-or-nothing execution model, and inflexibility in GPU sharing. Our analysis of a large GPU cluster in production shows that existing big data schedulers cause long queueing delays and low overall performance. We present Tiresias, a GPU cluster manager tailored for distributed DL training jobs, which efficiently schedules and places DL jobs to reduce their job completion times (JCTs). Given that a DL job's execution time is often unpredictable, we propose two scheduling algorithms-Discretized Two-Dimensional Gittins index relies on partial information and Discretized Two-Dimensional LAS is information-agnostic that aim to minimize the average JCT. Additionally, we describe when the consolidated placement constraint can be relaxed, and present a placement algorithm to leverage these observations without any user input. Experiments on the Michigan ConFlux cluster with 60 P100 GPUs and large-scale trace-driven simulations show that Tiresias improves the average JCT by up to 5.5x over an Apache YARN-based resource manager used in production. More importantly, Tiresias's performance is comparable to that of solutions assuming perfect knowledge.

关键词： Graphics processing unit

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ASIC Implementation of High-Speed Adaptive Recursive Karatsuba Multiplier with Square-Root-Carry-Select-Adder

ASIC Implementation of High-Speed Adaptive Recursive Karatsu...

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IEEE Latin American symposium on Circuits and systems (LASCAS)

作者： Akhilesh Naik Debarshi Deka Dipankar Pal Department of Electrical and Electronics Engineering BITS Pilani

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

Computation intensive applications such as DSP, image processing, floating point processors and communication technologies today require efficient binary multiplication which usually is the most power and time consuming block. this paper proposes an efficient design for unsigned binary multiplication to reduce delay. A 16×16-bit multiplier has been designed which is based on Vedic Karatsuba algorithm. It is optimized using adaptive and recursive approach combined with square-root-carry-select-adder. the designs have been coded in Verilog, synthesized in Cadence Genus and physically verified with Cadence Innovus in GDSII under ASIC platform.

关键词： Adders Delays Signal processing algorithms Standards Hardware design languages Complexity theory

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Separability Method for Homogeneous Leaves Using Spectroscopic imagery and Machine Learning algorithms 16th

Separability Method for Homogeneous Leaves Using Spectroscop...

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16th International symposium on Neural Networks (ISNN)

作者： Abe, Bolanle Tolulope Jordaan, Jaco Tshwane Univ Technol Dept Elect Engn eMalahleni Campus Emalahleni South Africa

ISBN: (纸本)9783030228088;9783030228071

Agathosma Buchu plants are a holistic healing system used as alternative medicine in dealing with numerous diseases and also for cooking oil production. there are two types namely, Betulina and Crenulata. the plants are difficult to separate if mixed up after harvest. Furthermore, the high rate of the plants' cultivation poses challenges in separating them for specific functions. Hence, other identification methods are crucial. this paper presents an implementation of machine learning algorithms based on spectroscopic imagery properties for automatic recognition of the plants' species. image Local Polynomial Approximation method is used for the image processing to reduce classification error and dimensionality of classification challenges. To demonstrate the efficacies of the processed dataset, K-Nearest Neighbour, Naive Bayes, Decision Tree, and Neural Network classifiers were used for the classification procedures in different data mining tools. the classifiers' performances are valuable for decision-makers to consider tradeoffs in method accuracy versus method complexity.

关键词： image Agathosma Separability Classification

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Hand-held 3D Dense Map Construction System Based on Improved ICP Algorithm

Hand-held 3D Dense Map Construction System Based on Improved...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Ziqi Cao Fan Lu Lei Yu Soochow University Jiangsu CHN

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In view of the problems of poor image quality and inaccurate positioning due to interference in interactive control systems, this paper proposes a non-local mean algorithm (NLM) based on adaptive median algorithm (ALM) based on the strategy of salt and pepper noise processing), and applied to non-contact interactive systems based on laser sensors. the main innovations of this paper: 1) In the image preprocessing algorithm, a noise reduction method that is effective for Gaussian noise, salt and pepper noise, and mixed noise of both of them is proposed; 2) the improved denoising algorithm is applied to lidar-based sensor's non-contact interactive system, which significantly improves the interactive performance. Experimental results show that the proposed algorithm has obvious denoising effect, accurate image positioning and good target tracking effect.

关键词： Filtering algorithms Noise reduction Filtering image denoising Gaussian noise Noise measurement Laser noise

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