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Development and future trends in the application of visualization toolkit (VTK): The case for medical image 3D reconstruction

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AIP conference Proceedings 2019年第1期2092卷

作者： Welsey Daniel C. Advincula Jose Antonio G. Choco Kryzz Amiel G. Magpantay Leonard Angelo N. Sabellina, III Jolo Gerard Miel F. Tolentino Renann G. Baldovino Nilo T. Bugtai Aaron Raymond See Yi-Chun Du 1Manufacturing Engineering and Management (MEM) Department De La Salle University 2401 Taft Avenue 0922 Manila Philippines 2Department of Electrical Engineering Southern Taiwan University of Science and Technology No. 1 Nantai Street Yongkang District Tainan City Taiwan 710

Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The developed 3D model offers a faster and accurate approach in clinical diagnosis and therapy. Before, the development of 3D reconstruction programs it has always been a challenge to visualize in a 3D perspective but with the advent of powerful computing and graphics processing, medical imaging equipment development is on the rise. The advancement of 3D image reconstruction is not only due to the innovative hardware, but also because of the increase in open-source platforms or libraries, such as visualization toolkit (VTK), that assist on its development to provide an information visualization framework and advanced 3D modeling techniques. VTK is an object-oriented 3D graphics software program that is free to obtain and used both for open- and closed-source applications such as 3D computer graphics, image processing, and visualization. In medical imaging, data from ultrasound systems, magnetic resonance imaging (MRI), and computed tomography (CT) scans can be managed in VTK. Different algorithms of image processing such as color mapping, image re-slicing or resampling, iso-contouring, thresholding, and other algorithms can be utilized to make volume images from the data. Subsequently, data presentation is simplified with the data reconstruction through the use of supplemental software for analysis. VTK library contains a large number of filters, which are multi-threaded to facilitate parallel processing and interpretation. In this paper, recent technologies on the application of VTK for 3D reconstruction of biomedical images will be presented. Moreover, current methodologies will be discussed to clearly understand the different integral steps needed in image data conversion.

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Development of an Embedded Atmospheric Turbulence Mitigation Engine 2

Development of an Embedded Atmospheric Turbulence Mitigation...

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conference on Long-Range Imaging II

作者： Paolini, Aaron Bonnett, James Kozacik, Stephen Kelmelis, Eric EM Photon Inc 51 East Main StSuite 203 Newark DE 19711 USA Univ Delaware Dept Elect & Comp Engn 140 Evans Hall Newark DE 19716 USA

ISBN: (数字)9781510609105

ISBN: (纸本)9781510609099;9781510609105

Methods to reconstruct pictures from imagery degraded by atmospheric turbulence have been under development for decades. The techniques were initially developed for observing astronomical phenomena from the Earth's surface, but have more recently been modified for ground and air surveillance scenarios. Such applications can impose significant constraints on deployment options because they both increase the computational complexity of the algorithms themselves and often dictate a requirement for low size, weight, and power (SWaP) form factors. Consequently, embedded implementations must be developed that can perform the necessary computations on low-SWaP platforms. Fortunately, there is an emerging class of embedded processors driven by the mobile and ubiquitous computing industries. We have leveraged these processors to develop embedded versions of the core atmospheric correction engine found in our ATCOM software. In this paper, we will present our experience adapting our algorithms for embedded systems on a chip (SoCs), namely the NVIDIA Tegra that couples general-purpose ARM cores with their graphics processing unit (GPU) technology and the Xilinx Zynq which pairs similar ARM cores with their field-programmable gate array (FPGA) fabric.

关键词： Atmospheric Turbulence Turbulence Mitigation Atmospheric Compensation image processing LongRange imagery image Enhancement Real-Time Embedded

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Machine-learning algorithms for helicopter hydraulic faults detection: model based research

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Journal of Physics: conference Series 2019年第5期1368卷

作者： A M Gareev E Yu Minaev D M Stadnik N S Davydov V I Protsenko I A Popelniuk A V Nikonorov A G Gimadiev Samara National Research University Moskovskoe Shosse 34A Samara Russia 443086 Image Processing Systems Institute of RAS - Branch of the FSRC 'Crystallography and Photonics' RAS Molodogvardejskaya street 151 Samara Russia 443001

The problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a hydraulic system. The first three approaches are based on SVM classifiers with linear, polynomial and RBF kernels and the last one is a gradient boosting on oblivious decision trees. We evaluate algorithms on the synthetic dataset generated by our simulation model of the helicopter hydraulic system and show that high accuracy fault detection can be achieved.

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Estimation of the particle concentration in hydraulic liquid by the in-line automatic particle counter based on the CMOS image sensor 10

Estimation of the particle concentration in hydraulic liquid...

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conference on Optical Measurement systems for Industrial Inspection X part of the SPIE Optical Metrology Symposium

作者： Kornilin, Dmitriy V. Kudryavtsev, Ilya A. McMillan, Alison J. Osanlou, Ardeshir Ratcliffe, Ian Samara Natl Res Univ 34 Moskovskoe Shosse Samara 443086 Russia Glyndwr Univ Mold Rd Wrexham LL11 2AW Wales

ISBN: (纸本)9781510611047;9781510611030

Modern hydraulic systems should be monitored on the regular basis. One of the most effective ways to address this task is utilizing in-line automatic particle counters (APC) built inside of the system. The measurement of particle concentration in hydraulic liquid by APC is crucial because increasing numbers of particles should mean functional problems. Existing automatic particle counters have significant limitation for the precise measurement of relatively low concentration of particle in aerospace systems or they are unable to measure higher concentration in industrial ones. Both issues can be addressed by implementation of the CMOS image sensor instead of single photodiode used in the most of APC. CMOS image sensor helps to overcome the problem of the errors in volume measurement caused by inequality of particle speed inside of tube. Correction is based on the determination of the particle position and parabolic velocity distribution profile. Proposed algorithms are also suitable for reducing the errors related to the particles matches in measurement volume. The results of simulation show that the accuracy increased up to 90 per cent and the resolution improved ten times more compared to the single photodiode sensor.

关键词： CMOS matrix sensor image processing automatic particles counters hydraulic systems diagnostic concentration measurement

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Parallel and Efficient Sensitivity Analysis of Microscopy image Segmentation Workflows in Hybrid systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

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IEEE International conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： image segmentation Sensitivity analysis Optimization Algorithm design and analysis Runtime image analysis

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Lane Detection and Prediction under Hazy Situations for Autonomous Vehicle Navigation

Lane Detection and Prediction under Hazy Situations for Auto...

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International conference on Advances in ICT for Emerging Regions

作者： Bandarage Shehani Sanketha Rathnayake Lochandaka Ranathunga Department of Information Technology University of Moratuwa Katubedda.Moratuwa Sri Lanka

ISBN: (纸本)9781538673539;9781538673522

Lane detection and tracking is essential concern in vision based autonomous vehicle navigation. This paper proposes a novel method based on probabilistic Hough transform and motion vector based analysis for detecting and tracking lane and lane departures. It addresses drawbacks of current systems under hazy situations with learning method based on previous tracking records of the system. In this method relevant lane mark features are extracted based on color variations. Therefore used HSL color model which gives higher color contrast of road surface and the markings. In order to reduce computation, processing algorithms are applied only to the region of interest (ROI). Edges of the selected regions are extracted with Canny Edge Detection. Since lane markings are made with set of straight lines, straight lines are extracted with Probabilistic Hough Transform. Extracted lines are analyzed to detect lane. To monitor lane departures vehicle's motion is detected and tracked based on motion vector analysis with Lucas Kanade Optical Flow algorithm. Previous detection records are used to track the lane continuously in hazy situations and provide prediction on approximate lane for the roads without lane markings. Experiments were performed on set of videos taken from vehicles' front camera mounted on dashboard. According to the experiments the proposed algorithm has 81.9% of sensitivity in lane detection and 63.5% in approximate lane predictions for the roads without lane markings. Lane detection algorithm has 83% precision, 68% recall and 0.75 F1 score with threshold brightness value 140. Algorithm can further improved to guide the autonomous vehicles to take accurate decisions on selecting safety lane to drive ahead in different situations.

关键词： Feature extraction Roads image color analysis Prediction algorithms Tracking image edge detection Transforms

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Underwater image Dehazing with a Light Field Camera 30

Underwater Image Dehazing with a Light Field Camera

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

作者： Skinner, Katherine A. Johnson-Roberson, Matthew Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781538607336

Underwater vision is subject to effects of underwater light propagation that act to absorb, scatter, and attenuate light rays between the scene and the imaging platform. Backscattering has been shown to have a strong impact on underwater images. As light interacts with particulate matter in the water column, it is scattered back towards the imaging sensor, resulting in a hazy effect across the image. A similar effect occurs in terrestrial applications in images of foggy scenes due to interaction with the atmosphere. Prior work on multi-image dehazing has relied on multiple cameras, polarization filters, or moving light sources. Single image dehazing is an ill-posed problem;proposed solutions rely on strong priors of the scene. This paper presents a novel method for underwater image dehazing using a light field camera to capture both the spatial and angular distribution of light across a scene. First, a 2D dehazing method is applied to each sub-aperture image. These dehazed images are then combined to produce a smoothed central view. Lastly, the smoothed central view is used as a reference to perform guided image filtering, resulting in a 4D dehazed underwater light field image. The developed method is validated on real light field data collected in a controlled in-lab water tank, with images taken in air for reference. This dataset is made publicly available.

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

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Deep Learning for SAR image Formation 24

Deep Learning for SAR Image Formation

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conference on algorithms for Synthetic Aperture Radar imagery XXIV

作者： Mason, Eric Yonel, Bariscan Yazici, Birsen Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

ISBN: (数字)9781510609044

ISBN: (纸本)9781510609037;9781510609044

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

关键词： synthetic aperture radar SAR deep learning inverse problems recurrent neural networks RNN radar imaging autofocus neural network

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TwinFusion: High Framerate Non-rigid Fusion through Fast Correspondence Tracking

TwinFusion: High Framerate Non-rigid Fusion through Fast Cor...

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International conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Kaiwen Guo Jonathan Taylor Sean Fanello Andrea Tagliasacchi Mingsong Dou Philip Davidson Adarsh Kowdle Shahram Izadi Google Inc.

Real time non-rigid reconstruction pipelines are extremely computationally expensive and easily saturate the highest end GPUs currently available. This requires careful strategic choices to be made about a set of highly interconnected parameters that divide up the limited compute. At the same time, offline systems, prove the value of increasing voxel resolution, more iterations, and higher frame rates. To this end, we demonstrate a set of remarkably simple but effective modifications to these algorithms that significantly reduce the average per-frame computation cost allowing these parameters to be increased. Specifically, we divide the depth stream into sub-frames and fusion-frames, disabling both model accumulation (fusion) and non-rigid alignment (model tracking) on the former. Instead, we efficiently track point correspondences across neighboring sub-frames. We then leverage these correspondences to initialize the standard non-rigid alignment to a fusion-frame where data can then be accumulated into the model. As a result, compute resources in the modified non-rigid reconstruction pipeline can be immediately re-purposed to increase voxel resolution, use more iterations or to increase the frame rate. To demonstrate the latter, we leverage recent high frame rate depth algorithms to build a novel “twin” sensor consisting of a low-res/highfps sub-frame camera and a second low-fps/high-res fusion camera.

关键词： Cameras Tracking image reconstruction Computational modeling Deformable models Data models Strain

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A Fast Method to Extract Focal Length of Camera Based on Parallel Particle Swarm Optimization

A Fast Method to Extract Focal Length of Camera Based on Par...

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Chinese Control conference (CCC)

作者： Chao Zheng Huangbin Qiu Chenning Liu Xin Zheng Chang Zhou Zeqing Liu Jiayuan Yang China Academy of Launch Vehicle Technology Beijing

The intrinsic parameters of the camera are mainly composed of the focal length, coordinate of the main point and the tilting factor. The focal length is an important parameter for the camera to obtain the three-dimensional information from the two-dimensional image, which determines the shooting angle of the lens. It can be obtained by the camera calibration, such as the Zhang's calibration method, but the process is tedious and takes a long time. In this paper, a fast method to extract focal length of camera is proposed. To improve and simplify the camera self-calibration algorithm, parallel particle swarm optimization (PSO) algorithm is applied to solve the improved Kruppa equation, making the pole computation converted to the fitness function of cost function. In addition, in the process of PSO calculation, parallel computing experiments are carried out on the multi-core computer platform, and the advantages of parallel computing are fully used. The experimental results show that the precision of the method's focal length is close to Zhang's calibration method, and the time of the algorithm is greatly reduced compared with other calibration algorithms.

关键词： Cameras Calibration Cost function Mathematical model Particle swarm optimization Parallel processing Robot vision systems

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