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

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Cone-Beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

Cone-Beam CT image contrast and attenuation-map linearity im...

引用

conference on Medical Imaging - Physics of Medical Imaging

作者： Hashemi, Sayed Masoud Lee, Young Eriksson, Markus Nordstrom, Hakan Mainprize, James Grouza, Vladimir Huynh, Christopher Sahgal, Arjun Song, William Y. Ruschin, Mark Sunnybrook Res Inst Odette Canc Ctr Toronto ON Canada Univ Toronto Dept Radiat Oncol Toronto ON Canada Elekta AB Stockholm Sweden Sunnybrook Res Inst Toronto ON Canada Ryerson Univ Dept Med Phys Toronto ON Canada

ISBN: (数字)9781510607101

ISBN: (纸本)9781510607095;9781510607101

A Contrast and Attenuation-map (CT-number) Linearity Improvement (CALI) framework is proposed for cone-beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is used together with our high spatial resolution iterative reconstruction algorithm and is tailored for the Leksell Gamma Knife ICON (Elekta, Stockholm, Sweden). The incorporated CBCT system in ICON facilitates frameless SRS planning and treatment delivery. The ICON employs a half-cone geometry to accommodate the existing treatment couch. This geometry increases the amount of artifacts and together with other physical imperfections causes image inhomogeneity and contrast reduction. Our proposed framework includes a preprocessing step, involving a shading and beam-hardening artifact correction, and a post-processing step to correct the dome/capping artifact caused by the spatial variations in x-ray energy generated by bowtie-filter. Our shading correction algorithm relies solely on the acquired projection images (i.e. no prior information required) and utilizes filtered-back-projection (FBP) reconstructed images to generate a segmented bone and soft-tissue map. Ideal projections are estimated from the segmented images and a smoothed version of the difference between the ideal and measured projections is used in correction. The proposed beam-hardening and dome artifact corrections are segmentation free. The CALI was tested on CatPhan, as well as patient images acquired on the ICON system. The resulting clinical brain images show substantial improvements in soft contrast visibility, revealing structures such as ventricles and lesions which were otherwise un-detectable in FBP-reconstructed images. The linearity of the reconstructed attenuation-map was also improved, resulting in more accurate CT#.

关键词： Signal attenuation Reconstruction algorithms Brain image processing algorithms and systems Neuroimaging X-ray computed tomography Visibility X-rays Brain mapping Cones image filtering Spatial resolution

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A New Method to Detect Moving Targets

A New Method to Detect Moving Targets

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2018 3rd International conference on Education, Management and systems Engineering(EMSE 2018)

作者： Yang-ming HE Ya-fen WU School of Computer Jiangxi University of Traditional Chinese Medicine Office of Academic Research Jiangxi University of Traditional Chinese Medicine

In the moving target detection, there are a lot of methods. But the effects of those algorithms are limited, especially in complex environment. This paper putts forward a new method to detect moving targets, which uses virtual binocular disparity. It is called virtual because the environment uses one camera but the theory is about two cameras. Virtual binocular disparity algorithm regards moving targets as static targets under two cameras. It uses two neighboring images, the current frame image and the next frame image. The algorithm looks the two images as two static images under two cameras, then uses stereo match theory to calculate the disparity of the neighboring images. Because the background of the two images is the same, the disparity of their background is about zero. And the disparity of moving targets is positive or negative. The sign of disparity means the direction of targets moving. So we can use the sign of disparity to distinguish the targets moving in different directions. The direction detected out will be helpful for subsequent work, for example, targets tracking and counting etc. This algorithm fully uses the continuity property of moving targets, so it can have good effect. In the end of the paper, experiments are used to prove the algorithm.

关键词： Virtual binocular disparity Stereo match Moving targets image processing

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Simple Black-Box Adversarial Attacks on Deep Neural Networks 30

Simple Black-Box Adversarial Attacks on Deep Neural Networks

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Narodytska, Nina Kasiviswanathan, Shiva VMware Res Palo Alto CA 94304 USA Samsung Res Amer Mountain View CA USA

ISBN: (纸本)9781538607336

Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our attacks utilize a novel local-search based technique to construct numerical approximation to the network gradient, which is then carefully used to construct a small set of pixels in an image to perturb. We demonstrate how this underlying idea can be adapted to achieve several strong notions of misclassification. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.

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

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A Multilevel Technique for Automatic Foreground Extraction 4

A Multilevel Technique for Automatic Foreground Extraction

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IEEE International conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Yang, Yi-Min Lee, Ming-Sui Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781509040179

Foreground extraction is an important and challenging problem in many applications of computer vision. Most existing algorithms either require user intervention as hard constraints or demand special inputs for extra information. Thus, an automatic foreground extraction algorithm from a single image is proposed in this paper. A Gaussian image pyramid is constructed and the gradient vector flow (GVF) snake is adopted at the coarsest level to generate a rough contour of the object which is then upsampled and serves as the initial input to GVF snake in the next level. This process repeats until the estimated contour is propagated to the finest level. Based on the result in the finest level, a binary mask can be generated accordingly and becomes the initial constraint in the segmentation. The proposed segmentation step includes two novel schemes, which simulate the Latent Dirichlet Allocation (LDA) generative model and a probabilistic stochastic process called Chinese restaurant process. With these mechanisms, the Gaussian Mixture Models adaptively determine the number of components for foreground and background individually. As a result, the proposed method is expected to not only produce a satisfactory result of foreground extraction automatically with more robustness and adaptation but also serve as a good preprocessing to improve the performance and accuracy for tasks in computer vision.

关键词： image segmentation Sun Videos Shape Eigenvalues and eigenfunctions Covariance matrices Maximum likelihood estimation

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Capacity and Delay Analysis of Arterials with Mixed Autonomous and Human-Driven Vehicles 5

Capacity and Delay Analysis of Arterials with Mixed Autonomo...

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5th IEEE International conference on Models and Technologies for Intelligent Transportation systems (MT-ITS)

作者： Ramezani, Mohsen Machado, Joao Aguiar Skabardonis, Alexander Geroliminis, Nikolas Univ Sydney Sch Civil Engn Sydney NSW Australia Ecole Polytech Fed Lausanne Sch Architecture Civil & Environm Engn Urban Transport Syst Lab Lausanne Switzerland Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA

ISBN: (纸本)9781509064847

This paper investigates the traffic flow characteristics of mixed stream of autonomous and human-driven vehicles. The proposed model aims at understanding the fundamental properties of mixed flow of human-driven (N) and autonomous vehicles (AV) such as headway, capacity, and delay at signalized intersections. This is challenging because of intrinsic differences between longitudinal driving characteristics of these two types of vehicles and the convoluted dynamics of car following situation within various combinations of AV and human-driven vehicles. The expected headway of the mixed flow is determined based on the penetration rate of AV and the headways between two successive AV-AV, AV-N, N-AV, and N-N. Furthermore, the upper and lower bounds of mixed flow headway is presented. The theoretical headway is validated by microsimulation data. The estimated headways are then incorporated to derive the delay of a mixed flow at a signalized 2-lane link. Four combination of (i) mixed lanes, (ii) dedicated lanes for AV and human-driven vehicles, (iii) one mixed lane and one AV dedicated lane, and (iv) one mixed lane and one human-driven vehicle dedicated lane are considered. The results demonstrate the performance of the four lane configurations for various stages of AV deployment penetration rate.

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

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DeepXScope: Segmenting Microscopy images with a Deep Neural Network 30

DeepXScope: Segmenting Microscopy Images with a Deep Neural ...

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Saponaro, Philip Treible, Wayne Kolagunda, Abhishek Chaya, Timothy Caplan, Jeffrey Kambhamettu, Chandra Wisser, Randall Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Delaware Dept Plant & Soil Sci Newark DE 19717 USA Univ Delaware Delaware Biotechnol Inst Newark DE USA

ISBN: (纸本)9781538607336

High-speed confocal microscopy has shown great promise to yield insights into plant-fungal interactions by allowing for large volumes of leaf tissue to be imaged at high magnification. Currently, segmentation is performed either manually, which is infeasible for large amounts of data, or by developing separate algorithms to extract individual features within the image data. In this work, we propose the use of a single deep convolutional neural network architecture dubbed DeepXScope for automatically segmenting hyphal networks of the fungal pathogen and cell boundaries and stomata of the host plant. DeepXScope is trained on manually annotated images created for each of these structures. We describe experiments that show each individual structure can be accurately extracted automatically using DeepXScope. We anticipate that plant scientists will be able to use this network to automatically extract multiple structures of interest, and we plan to release our tool to the community(1).

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

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Novel parallel givens QR decomposition implementation on VLIW architecture with efficient memory access for real time image processing applications 17

Novel parallel givens QR decomposition implementation on VLI...

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2nd International conference on Big Data Cloud and Applications, BDCA 2017

作者： Najoui, Mohamed Hatim, Anas Belkouch, Said LGECOS Lab ENSA-Marrakech University of Cadi Ayyad Marrakech Morocco ENSA-Agadir Ibn Zohr University Agadir Morocco

ISBN: (纸本)9781450348522

Compressed Sensing (CS) methods impact is important in the health care systems. The acquisition and the processing speed of many medical imaging applications is highly improved using this technique. Orthogonal Matching Pursuit (OMP) is one of the widely used image reconstruction algorithms in CS techniques. Traditionally, OMP is divided on two main tasks: optimization stage and least square problem (LSP) resolution stage. The LSP resolution is the most complex and time consuming step in OMP. QRD is one of the main techniques used to solve the LSP in a very short time. However, the increasing image and video resolutions (UHDTV) makes the real time constraints very tight. In this paper, a novel implementation of QRD on VLIW DSP architecture with an efficient memory access approach is introduced. An instruction, data and loop levels parallelism (ILP, DLP and LLP) based Givens QRD kernel is designed. A robust memory access management based on intelligent loading/storing strategies and proficient data alignment techniques is adopted for the proposed parallel implementation. Therefore, memory read misses and CPU stalls are significantly diminished. As a final result the processing time is greatly reduced. The proposed scheme is implemented on C6678 DSP and reaches 2.22 GFLOPS. The performances achieved are 7.4, 3.83 and 2 times faster than standard Givens, Texas Instrument optimized QRD and state of the art, respectively. © 2017 Association for Computing Machinery.

关键词： Compressed sensing

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Which Players will Leave Their Community? Predicting Guild Abandonments in World of Warcraft Game Data 17

Which Players will Leave Their Community? Predicting Guild A...

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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： Posfai, Gergely Magyar, Gabor Koczy, Laszlo T. Budapest Univ Technol & Econ Dept Telecommun & Media Informat Budapest Hungary

ISBN: (纸本)9781509049172

World of Warcraft (WoW) is one of the most popular massively multiplayer online role-playing games (MMORPGs) having more than 10 million subscribers over the world. In order to engage and retain users understanding and predicting their behavior can be very useful for game developers. An important component of WoW are so-called guilds, which are social communities whose members can act together efficiently to accomplish more difficult goals and also provide a social atmosphere in which the game might be more entertaining. In this paper, we build predictive models to forecast which of the players will leave their guild in the close future. Our best model uses fuzzy c-means clustering to capture groups of similar guilds, that serve as the basis of an ensemble model, which computes predictions for each cluster separately and combines individual predictions into one final prediction using the memberships of the fuzzy clusters. Empirical analysis on WoW game data shows that our methods convincingly outperform the only existing method in the literature. To ensure transparency and reproducibility we publish the source codes of the research and also provide a Docker image, which makes it possible for anyone who has Docker installed to reproduce all of our results with a single command.

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

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Human Action Recognition Using Tensor Dynamical System Modeling 30

Human Action Recognition Using Tensor Dynamical System Model...

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Lee, Chan-Su Yeungnam Univ Dept Elect Engn 280 Daehak Ro Gyongsan 38541 Gyeongbuk Do South Korea

ISBN: (纸本)9781538607336

This paper presents a new framework for human action classification using a tensor dynamical model of human action from 3-dimensional (3D) volume sequences and distance measurement on Grassmann manifold. The tensor dynamical model is an extension of linear dynamical models for multi-dimensional sequence analysis. Each sub-dimensional linear dynamic model is estimated from tensor sequences using an iterative expectation-maximization (EM) algorithm after projection of tensor sequence to each dimensional axis. The combination of distances on Grassmann manifold of linear dynamic systems in each dimension of the tensor dynamic model provides similarity measurement between two tensor dynamical systems. The proposed approach can be applied to 3D depth or convex hull data as well as 2D video image sequences. Experimental results show good performance in human action recognition from INRIA multiview human action database.

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

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Training Sparse Neural Networks 30

Training Sparse Neural Networks

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Srinivas, Suraj Subramanya, Akshayvarun Babu, R. Venkatesh Indian Inst Sci Dept Computat & Data Sci Video Analyt Lab Bangalore Karnataka India

ISBN: (纸本)9781538607336

The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to dense matrix multiplications and convolutions. As a result, these architectures are highly memory intensive, making them less suitable for embedded vision applications. Sparse Computations are known to be much more memory efficient. In this work, we train and build neural networks which implicitly use sparse computations. We introduce additional gate variables to perform parameter selection and show that this is equivalent to using a spike-and-slab prior. We experimentally validate our method on both small and large networks which result in highly sparse neural network models.

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

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