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检索条件"任意字段=Intelligent Robotics and Industrial Applications using Computer Vision 2017, IRIACV 2017"

共 126 条记录，以下是31-40 订阅

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Sky segmentation for enhanced depth reconstruction and bokeh rendering with efficient architectures

Sky segmentation for enhanced depth reconstruction and bokeh...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Nuanes, Tyler Elsey, Matt Grzeszczuk, Radek Shen, John Paul Carnegie Mellon University PittsburghPA United States Light Labs Inc. Redwood CityCA United States

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, high-quality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost. © 2020 Society for Imaging Science and Technology. All rights reserved.

关键词： computer vision

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Imaging through scattering media with a learning based prior

Imaging through scattering media with a learning based prior

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Schiffers, Florian Fiske, Lionel Ruiz, Pablo Katsaggelos, Aggelos K. Cossairt, Oliver Computer Science Northwestern University ChicagoIL United States Electrical and Computer Engineering Northwestern University ChicagoIL United States

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (ST-PSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem. © 2020, Society for Imaging Science and Technology.

关键词： Optical transfer function

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Rare-class extraction using cascaded pretrained networks applied to crane classification

Rare-class extraction using cascaded pretrained networks app...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Klomp, Sander R. Brouwers, Guido M.Y.E. Wijnhoven, Rob G.J. De With, Peter H.N. Eindhoven University of Technology SPS-VCA group of Electr. Eng. Eindhoven Netherlands ViNotion b.v. Eindhoven Netherlands

Overweight vehicles are a common source of pavement and bridge damage. Especially mobile crane vehicles are often beyond legal per-axle weight limits, carrying their lifting blocks and ballast on the vehicle instead of on a separate trailer. To prevent road deterioration, the detection of overweight cranes is desirable for law enforcement. As the source of crane weight is visible, we propose a camera-based detection system based on convolutional neural networks. We iteratively label our dataset to vastly reduce labeling and extensively investigate the impact of image resolution, network depth and dataset size to choose optimal parameters during iterative labeling. We show that iterative labeling with intelligently chosen image resolutions and network depths can vastly improve (up to 70×) the speed at which data can be labeled, to train classification systems for practical surveillance applications. The experiments provide an estimate of the optimal amount of data required to train an effective classification system, which is valuable for classification problems in general. The proposed system achieves an AUC score of 0.985 for distinguishing cranes from other vehicles and an AUC of 0.92 and 0.77 on lifting block and ballast classification, respectively. The proposed classification system enables effective road monitoring for semi-automatic law enforcement and is attractive for rare-class extraction in general surveillance classification problems. © 2020 Society for Imaging Science and Technology.

关键词： Deterioration

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Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

Estimating vehicle fuel economy from overhead camera imagery...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Karnowski, Thomas Tokola, Ryan Oesch, Sean Eicholtz, Matthew Price, Jeff Gee, Tim Electrical and Electronics Systems Research Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science Florida Southern College LakelandFL33801 United States GRIDSMART Technologies Inc. KnoxvilleTN37931 United States

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Data set (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the data set utility through three applications: estimation of fuel consumption based on segmentation bounding boxes, vehicle discrimination for vehicles with large bounding boxes, and fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set of web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results demonstrate the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras. © 2020 Society for Imaging Science and Technology.

关键词： computer vision

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Connected-tube MPP model for unsupervised 3D fiber detection

Connected-tube MPP model for unsupervised 3D fiber detection

引用

2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Li, Tianyu Aguilar, Camilo G. Agyei, Ronald F. Hanhan, Imad A. Sangid, Michael D. Comer, Mary L. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Aeronautics and Astronautics Purdue University West LafayetteIN United States

In this paper, we extend our previous 2D connected-tube marked point process (MPP) model to a 3D connected-tube MPP model for fiber detection. In the 3D case, a tube is represented by a cylinder model with two spherical areas at its ends. The spherical area is used to define connection priors that encourage connection of tubes that belong to the same fiber. Since each long fiber can be fitted by a series of connected short tubes, the proposed model is capable of detecting curved long tubes. We present experimental results on fiber-reinforced composite material images to show the performance of our method. © 2020, Society for Imaging Science and Technology.

关键词： Fibers

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A splitting-based iterative algorithm for GPU-accelerated statistical dual-energy x-ray CT reconstruction

A splitting-based iterative algorithm for GPU-accelerated st...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Li, Fangda Manerikar, Ankit Prakash, Tanmay Kak, Avinash C. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

When dealing with material classification in baggage at airports, Dual-Energy Computed Tomography (DECT) allows characterization of any given material with coefficients based on two attenuative effects: Compton scattering and photoelectric absorption. However, straightforward projection-domain decomposition methods for this characterization often yield poor reconstructions due to the high dynamic range of material properties encountered in an actual luggage scan. Hence, for better reconstruction quality under a timing constraint, we propose a splitting-based, GPU-accelerated, statistical DECT reconstruction algorithm. Compared to prior art, our main contribution lies in the significant acceleration made possible by separating reconstruction and decomposition within an Alternating Direction Method of Multipliers (ADMM) framework. Experimental results, on both synthetic and real-world baggage phantoms, demonstrate a significant reduction in time required for convergence. © 2020, Society for Imaging Science and Technology.

关键词： Domain decomposition methods

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Background subtraction in diffraction X-ray images using deep CNN

Background subtraction in diffraction X-ray images using dee...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Carmona, Rodrigo Aranguren Agam, Gady Irving, Thomas Illinois Institute of Technology ChicagoIL60616 United States

Diffraction X-ray images provide molecular level information in tissue under hydrated, physiological conditions at the physiologically relevant millisecond time scale. When processing diffraction x-ray images there is a need to subtract background produced during the capture process prior to making measurements. This is a non-uniform background that is strongest at the diffraction center and decays with increased distance from the center. Existing methods require careful parameter selection or assume a specific background model. In this paper we propose a novel approach for background subtraction in which we learn to subtract background based on labeled examples. The labeled examples are image pairs where in each pair one of the images has diffraction background and the second has the background removed. using a deep convolutional neural network (CNN) we learn to map an image with background to an image without it. Experimental results demonstrate that the proposed approach is capable of learning background removal with results close to ground truth data (PSNR > 68, SSIM > 0.99) and without having to manually select background parameters. © 2020, Society for Imaging Science and Technology.

关键词： Diffraction

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A spectrum-adaptive decomposition method for effective atomic number estimation using dual energy CT

A spectrum-adaptive decomposition method for effective atomi...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Manerikar, Ankit Li, Fangda Kak, Avinash C. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

Dual Energy Computed Tomography (DECT) is expected to become a significant tool for voxel-based detection of hazardous materials in airport baggage screening. The traditional approach to DECT imaging involves collecting the projection data using two different X-ray spectra and then decomposing the data thus collected into line integrals of two independent characterizations of the material properties. Typically, one of these characterizations involves the effective atomic number (Ze f f ) of the materials. However, with the X-ray spectral energies typically used for DECT imaging, the current best-practice approaches for dual-energy decomposition yield Ze f f values whose accuracy range is limited to only a subset of the periodic-table elements, more specifically to (Z e (SIRZ) space, the SIRZ transformation does not efficiently model the polychromatic nature of the X-ray spectra typically used in physical CT scanners. In this paper, we present a new decomposition method, AdaSIRZ, that corrects this shortcoming by adapting the SIRZ decomposition to the entire spectrum of an X-ray source. The method reformulates the X-ray attenuation equations as direct functions of (?e, Ze) and solves for the coefficients using bounded nonlinear least-squares optimization. Performance comparison of AdaSIRZ with other Ze f f estimation methods on different sets of real DECT images shows that AdaSIRZ provides a higher output accuracy for Ze f f image reconstructions for a wider range of object materials. © 2020, Society for Imaging Science and Technology.

关键词： computerized tomography

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Single-shot coded diffraction system for 3D object shape estimation

Single-shot coded diffraction system for 3D object shape est...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Pinilla, Samuel Galvis, Laura Egiazarian, Karen Arguello, Henry Universidad Industrial de Santander Bucaramanga Colombia Tampere University Tampere Finland

The three-dimensional (3D) shape reconstruction problem of an object is a task of high interest in autonomous vehicles, detection of moving objects, and precision agriculture. A common methodology to recover the 3D shape of an object is using its optical phase. However, this approach involves solving a non-convex computationally demanding inverse problem known as phase retrieval (PR) in a setup that records coded diffraction patterns (CDP). Usually, the acquisition of several snapshots from the scene is required to solve the PR problem. This work proposes a single-shot 3D shape estimation technique using the optical phase of the object from CDP. The presented approach consists on accurately estimating the optical phase of the object by low-pass-filtering the leading eigenvector of a carefully constructed matrix. Then, the estimated phase is used to infer the 3D object shape. It is important to mention that the estimation procedure does not involve a full time demanding reconstruction of the objects. Numerical results on synthetic data demonstrate that the proposed methodology closely estimates the 3D surface of an object with a normalized Mean-Square-Error of up to 0.27, under both noiseless and noisy scenarios. Additionally, the proposed method requires up to 60% less measurements to accurately estimate the 3D surface compared to a state-of-the-art methodology. © 2020, Society for Imaging Science and Technology.

关键词： Inverse problems

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Reconstruction of 2D seismic wavefields from nonuniformly sampled sources

Reconstruction of 2D seismic wavefields from nonuniformly sa...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Galvis, Laura Ramírez, Juan M. Vargas, Edwin Villarreal, Ofelia Agudelo, William Arguello, Henry Universidad Industrial de Santander Santander Colombia Cooperativa de Tecnólogos e Ingenieros de la Industria del Petróleo y Afines TIP Instituto Colombiano del Petróleo ICP Ecopetrol S.A. Santander Colombia

In a 3D seismic survey, the source sampling in a regular grid is commonly limited by economic costs, geological constraints, and environmental challenges. This non-uniform sampling cannot be ignored since the lack of regularity leads to incomplete seismic data with missing 2D wavefields. Notice that the post-processing tasks have been developed under the assumption that 3D seismic data are obtained from a regular sampling. Therefore, signal recovery from incomplete data becomes a crucial step in the seismic imaging processing flow. In this work, we propose a pre-processing step that includes the nonuniformly acquired wavefields in a finer regular grid, such that shot gathers are stacked considering the actual spatial location of the sources. Then, based on the 3D curvelet transform, a sparse signal recovery algorithm that considers an interpolation operator is employed in order to reconstruct the missing wavefields in a regular grid. The performance of the proposed seismic reconstruction approach is evaluated on a real data set. © 2020, Society for Imaging Science and Technology.

关键词： Seismic waves

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