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

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Update on k-truss Decomposition on GPU

Update on k-truss Decomposition on GPU

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Mohammad Almasri Omer Anjum Carl Pearson Zaid Qureshi Vikram S. Mailthody Rakesh Nagi Jinjun Xiong Wen-mei Hwu ECE University of Illinois at Urbana-Champaign Urbana IL CS University of Illinois at Urbana-Champaign Urbana IL ISE University of Illinois at Urbana-Champaign Urbana IL Cognitive Computing Systems Research IBM Thomas J. Watson Research Center Yorktown Heights NY

In this paper, we present an update to our previous submission on k-truss decomposition from Graph Challenge 2018. For single k k-truss implementation, we propose multiple algorithmic optimizations that significantly improve performance by up to 35.2x (6.9x on average) compared to our previous GPU implementation. In addition, we present a scalable multi-GPU implementation in which each GPU handles a different `k' value. Compared to our prior multi-GPU implementation, the proposed approach is faster by up to 151.3x (78.8x on average). In case when the edges with only maximal k-truss are sought, incrementing the `k' value in each iteration is inefficient particularly for graphs with large maximum k-truss. Thus, we propose binary search for the `k' value to find the maximal k-truss. The binary search approach on a single GPU is up to 101.5 (24.3x on average) faster than our 2018 k-truss submission. Lastly, we show that the proposed binary search finds the maximum k-truss for “Twitter“ graph dataset having 2.8 billion bidirectional edges in just 16 minutes on a single V100 GPU.

关键词： Graphics processing units Compaction Optimization image edge detection Approximation algorithms Upper bound Hardware

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Ship detection in SAR image using YOLOv2 37

Ship detection in SAR image using YOLOv2

引用

37th Chinese Control conference, CCC 2018

作者： Khan, Hamza Mehdi Yunze, Cai Department of Automation Ministry of Education of China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Jiaotong University 800 Dongchuan RD Shanghai200240 China

ISBN: (纸本)9789881563941

Ship detection in SAR images is a challenge and has traditionally been carried out using pixel based algorithms such as CFAR, in this paper we use a deep learning based algorithm called YOLOv2 for the aforementioned task and test its performance on three datasets, at different resolution and quality, with two datasets called DS1 and DS2 consisting of over 400 high resolution SAR images with a high ship to image ratio, in which DS1 consists of raw, unfiltered images and the third dataset called DS3 which consists of full scale 5500 single ship images of lower resolution. High detection results are observed which further improve once we train the network by applying transfer learning using weights from DS3 by truncating the last layer of the network and continue training on the combined first two datasets. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Synthetic aperture radar

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Optimising Quality-of-Control for Data-Intensive Multiprocessor image-Based Control systems Considering Workload Variations

Optimising Quality-of-Control for Data-Intensive Multiproces...

引用

Euromicro Symposium on Digital System Design

作者： Sajid Mohamed Diqing Zhu Dip Goswami Twan Basten Electronic systems Group Eindhoven University of Technology The Netherlands

ISBN: (纸本)9781538673782

image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of compute-intensive image processing algorithms whose response times are dependent on image workload. IBC systems are typically designed for the worst-case workload that results in a long sample period and hence suboptimal quality-of-control (QoC). This worst-case based design is further considered for mapping of controller tasks and allocating platform resources, resulting in significant resource over-provisioning. Our design philosophy is to sample as fast as possible to optimise QoC for a given platform allocation, and for this, we present a structured design flow. Workload variations determine how fast we can sample and we model this dynamic behaviour using the concept of workload scenarios. Our choice of scenario-aware dataflow as the formal model for our application enables us to: i) model dynamic behaviour, analyse timing, and optimally map application tasks to the platform for maximising the effective utilisation of allocated resources, ii) relate throughput of the dataflow graph to the sample period, and thus combine dataflow analysis and mapping with control design parameters and QoC to identify system scenarios, and iii) to efficiently implement a run-time mechanism that manages necessary dynamic reconfiguration between system scenarios. Our results show that our design approach outperforms the worst-case based design with respect to optimising QoC and maximising effective resource utilisation.

关键词： Task analysis Analytical models Resource management Computational modeling Cameras image processing

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Annotation tool designed for hazardous user behavior in guided mountain transport

Annotation tool designed for hazardous user behavior in guid...

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International image processing, Applications and systems conference (IPAS)

作者： Rémi Dufour Cyril Meurie Amaury Flancquart Univ Lille Nord de France Villeneuve d’Ascq

ISBN: (纸本)9781728102481;9781728102474

This paper proposes a semi-automatic ground truth annotation software designed for the specific needs of the EVEREST project. The purpose of this project is to build an annotated and anonymized video database, and use it to evaluate algorithms in the task of detecting hazardous behavior in guided mountain transport. To do so, a ground truth annotation tool that disposes designed specifically for the EVEREST project was needed. Ski lifts safety based on intelligent video systems is a niche domain which has not yet been explored in depth, which means no annotation tool suited for this task was available. That is why, we decided to develop a user-friendly and flexible tool to allows the semi-automatic annotation of events and faces (for privacy purposes). We looked at existing tracking algorithms, chose an implementation of TLD, and designed a new tracking algorithm that could be used when TLD isn't effective. This led to a simple, lightweight tracking algorithm that is more practical to use than the original CAMshift algorithm, and a user-friendly and flexible annotation tool that is well adapted to the specific task of annotating hazardous behavior in guided mountain transport.

关键词： Tools Optical imaging Tracking Databases Task analysis image processing Software

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Improving Feature-based Visual SLAM by Semantics

Improving Feature-based Visual SLAM by Semantics

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International image processing, Applications and systems conference (IPAS)

作者： Ya Wang Andreas Zell Cognitive Systems Univeristy of Tuebingen Tuebingen Germany

ISBN: (纸本)9781728102481;9781728102474

Feature-based simultaneous localization and mapping (SLAM) algorithms with additional semantics can have better feature matching and tracking accuracies than the original SLAM algorithms. Therefore, this paper shows how to improve feature-based SLAM by only matching features from objects of the same semantic class. The basic idea is to use a deep neural network, YOLO (you only look once [1]), to classify objects and to associate features with the objects in whose bounding box they appear, thus giving features the semantic label of these objects. During feature matching of the SLAM algorithms, only features with the same semantic label are matched (e.g. books with books, bottles with bottles etc.), eliminating matches of similar features on different classes of objects. Experiments of classical ORB-SLAM2 with YOLO have been performed on an embedded PC. Additionally, ORB-SLAM2 with different versions of YOLO has also been tested on a powerful desktop GPU as well as on an Nvidia Jetson TX2 board. The experimental results show that using the semantic information given by object recognition methods reduces wrong feature matches in tracking and decreases the tracking lost cases.

关键词： Simultaneous localization and mapping Semantics Visualization Feature extraction Object recognition image color analysis Three-dimensional displays

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A probabilistic U-net for segmentation of ambiguous images 32

A probabilistic U-net for segmentation of ambiguous images

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32nd conference on Neural Information processing systems, NeurIPS 2018

作者： Kohl, Simon A.A. Romera-Paredes, Bernardino Meyer, Clemens De Fauw, Jeffrey Ledsam, Joseph R. Maier-Hein, Klaus H. Ali Eslami, S.M. Rezende, Danilo Jimenez Ronneberger, Olaf DeepMind London United Kingdom Division of Medical Image Computing German Cancer Research Center Heidelberg Germany

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities. © 2018 Curran Associates *** rights reserved.

关键词： Computerized tomography

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13th International conference on Informatics in Control, Automation and Robotics, ICINCO 2016

13th International Conference on Informatics in Control, Aut...

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13th International conference on Informatics in Control, Automation and Robotics, ICINCO 2016

ISBN: (纸本)9783319550107

The proceedings contain 21 papers. The special focus in this conference is on Informatics in Control, Automation and Robotics. The topics include: Parameter identification and model-based control of redundantly actuated, non-holonomic, omnidirectional vehicles;passivity-based control design and experiments for a rolling-balancing system;time-optimal paths for a robotic batting task;an adaptive terminal sliding mode guidance law for head pursuit interception with impact angle considered;kinematic and dynamic approaches in gait optimization for humanoid robot locomotion;identification and control of the waelz process using infrared image processing;modeling and calibrating triangulation lidars for indoor applications;a comparison of discretization methods for parameter estimation of nonlinear mechanical systems using extended kalman filter: Symplectic versus classical approaches;dynamics calibration and real-time state estimation of a redundant flexible joint robot based on encoders and gyroscopes;visual servoing path-planning with elliptical projections;Mathematical model for the output signal’s energy of an ideal DAC in the presence of clock jitter;stochastic integration filter with improved state estimate mean-square error computation;fractional models of lithium-ion batteries with application to state of charge and ageing estimation;co-operation of biology related algorithms for solving opinion mining problems by using different term weighting schemes;bifurcation analysis and active control of surge and rotating stall in axial flow compressors via passivity;task controller for performing remote centre of motion;toward an automatic fongbe speech recognition system: Hierarchical mixtures of algorithms for phoneme recognition;spatial fusion of different imaging technologies using a virtual multimodal camera.

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Modeling and Performance Evaluation of Generative adversarial network for image denoising

Modeling and Performance Evaluation of Generative adversaria...

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International conference on Computational Techniques, Electronics and Mechanical systems (CTEMS)

作者： V NUTHNA Kavita Chachadi Leah S Joshi Department of Electrical and Electronics KLE Technological University Hubballi Karnataka

ISBN: (纸本)9781538677100

During transmission and reception the images are degraded by noise. Also images captured using different low quality devices adversely affect the visual quality of images. The presence of the noise results in loss of visibility, gives a mottled, grainy, textured, or snowy appearance. Thus making the image visually unpleasing which drastically affects the human vision. In addition during image processing, presence of noise makes it hard for further processing (impulse, Gaussian white, salt and pepper, adversarial etc.,). Existing methods use conventional filters and Neural network models for image denoising where they compromise with the visibility of image after rigorous iterations of denoising algorithms. In this paper we implement CGANs for image denoising and evaluate the performance of CGAN with different Neural network models viz., CNN ,GAN for single or multiple image denoising problem. The qualitative performance of de-noised image/images is measured using PSNR and confusion matrix.

关键词： Gallium nitride Generators Generative adversarial networks Data models Neural networks Filtering algorithms Noise reduction

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Motion Detection and Extraction of Human Body Using Hybrid Technique

Motion Detection and Extraction of Human Body Using Hybrid T...

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International conference on Smart systems and Inventive Technology (ICSSIT)

作者： Riya Sharma Deepak Bhatia Department of Electronics Engineering University College of Engineering RTU Kota Rajasthan India

ISBN: (纸本)9781538658741

In recent time digital image processing and its importance have increased significantly, subject to the development of automation systems. Digital image processing and related research areas have been paved for the invention of high end applications in pharmaceutical, robotics, satellite image processing, genetics etc. Many applications have been found in daily applications around human beings in image processing. Therefore, in real time images and video sequences are essential for locating human bodies and have been entered. In this paper, a bottom-up methodology is proposed for automatic detection and extraction of human bodies from single images. Work of this paper is Design and development of a hybrid algorithm for detection and extraction of human bodies from different images and currencies. Use of Artificial Intelligence and Automatic Learning with the combination of image processing techniques to detect and split different human currencies and movements on the basis of structure. The use of vision processing has also been proposed to allow the application of the system in real-time video received from various sources, such as CCTV, Motion Capture Camera etc. The purpose of the proposed system is to be much faster than the existing algorithm, and therefore the task of reducing the complexity of the location and time of the existing algorithms.

关键词： Streaming media Real-time systems Motion detection Standards conferences image processing Cameras

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Automatic Detection of Thistle-Weeds in Cereal Crops from Aerial RGB images 17th

Automatic Detection of Thistle-Weeds in Cereal Crops from Ae...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based systems (IPMU)

作者： Franco, Camilo Guada, Carely Tinguaro Rodriguez, J. Nielsen, Jon Rasmussen, Jesper Gomez, Daniel Montero, Javier Andes Univ Dept Ind Engn Bogota Colombia Univ Complutense Dept Stat & OR Madrid Spain Univ Copenhagen Dept Plant & Environm Sci Copenhagen Denmark Univ Complutense Madrid Dept Stat & OR 3 Madrid Spain Univ Complutense Madrid Geosci Inst CSIC UCM Madrid Spain

ISBN: (纸本)9783319914794;9783319914787

Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50m above the ground, with the purpose of applying herbicide for sitespecific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.

关键词： image analysis k-Nearest Neighbours Neural networks Soft measures Weed detection Precision agriculture

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