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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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DEPTH MAP CONCEALMENT USING INTER-vIEW WARPING vECTORS from GEOMETRIC TRANSFORMS

DEPTH MAP CONCEALMENT USING INTER-VIEW WARPING VECTORS FROM ...

引用

IEEE International conference on image Processing

作者： S. Marcelino P. Assuncao S. M. M. Faria S. Soares Instituto de Telecomunica??es Universidade de Trás-os-Montes e Alto Douro/ECT Engineering Department

ISBN: (纸本)9781479923427

This paper deals with reconstruction of corrupted depth maps received by multivew video-plus-depth (MvD) decoders from error prone channels. An interview-based method is proposed using warping vectors obtained through a block matching approach with geometric transforms (BMGT) between two colour views. It is shown that BMGT is able to find efficient warping vectors for reconstruction of lost regions in the depth maps associated with the colour views. The proposed concealment method uses an additional contour reconstruction technique, applied to arbitrary shapes within the lost regions, which is used for weighted interpolation. In comparison to a reference method based on simple weighted interpolation, the proposed method is able to achieve PSNR gains in synthesised views up to 5.61dB, at data loss ratios up to 40%.

关键词： Error concealment depth map geometric transforms STRUCTURE MAP Warping data Security weighted interpolation error prone Peak signal-to-noise ratio

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Range image super-resolution via reconstruction of sparse range data

Range image super-resolution via reconstruction of sparse ra...

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International conference on Intelligent Systems and Signal Processing (ISSP)

作者： Arnav v. Bhavsar Department of Electrical Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781479903160

We propose a method for super-resolution of range image. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we build upon a recent approach which reconstructs dense range images from sparse range data. We notice certain shortcomings of this approach and propose some improvements, particularly, to address the super-resolution problem. Our method only uses a single colour image in addition to the range observation in the super-resolution process. Using the proposed approach, we demonstrate super-resolution results for large factors (e.g. 4 and 8) with good localization and accuracy.

关键词： image color analysis image segmentation Signal resolution Spatial resolution Accuracy image reconstruction

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A 3D reconstruction OF THE HUMAN JAW from A SINGLE image

A 3D RECONSTRUCTION OF THE HUMAN JAW FROM A SINGLE IMAGE

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IEEE International conference on image Processing

作者： Aly Abdelrahim Ahmed Shalaby Shireen Elhabian James Graham Aly Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville Louisville KY 40292 USA. Faculty of Computers and Information Cairo Univeristy Egypt ECE Dept. University of Louisville Louisville KY 40292 USA.

ISBN: (纸本)9781479923427

Accurate 3D modeling of the human teeth/jaw helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. In this paper, we attempt to improve the limitations of previous 3D tooth reconstruction algorithms by developing a new approach for shape reconstruction from single image shading with Two-Dimensional Principle Component Analysis (2D-PCA) shape priors. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case and we experimentally prove that the teeth surface follows the microfacet theory. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human jaws. Our experiments provide promising quantitative metric results for the proposed approach. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw that is inexpensive and does not use ionizing radiation.

关键词： jaw modeling Oren-Nayar-Wolff reflectance model 2D-PCA SFS shape priors Jaw three-dimensional displays surface reflectance data Accuracy images shape reconfiguration Tooth tooth flank HUMAN TOOTH retro-reflection artificial shade IONIZING RADIATIONS

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DIRECT AND PROGRESSIvE reconstruction OF DUAL PHOTOGRAPHY imageS

DIRECT AND PROGRESSIVE RECONSTRUCTION OF DUAL PHOTOGRAPHY IM...

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IEEE International conference on image Processing

作者： Binh-Son Hua Imari Sato Kok-Lim Low National University of Singapore National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781479923427

Dual photography is a well-known application of light transport acquired by a projector-camera system. By applying compressive sensing, compressive dual photography [1] is a fast approach to acquire the light transport for dual photography. However, the reconstruction step in compressive dual photography can still take several hours before dual images can be synthesized because the entire light transport needs to be reconstructed from measured data. In this paper, we present a novel reconstruction approach that can directly and progressively synthesize dual images from measured data without the need of first reconstructing the light transport. We show that our approach can produce high-quality dual images in the order of minutes using only a thousand of samples. Our approach is most useful for previewing a few dual images, e.g., during light transport acquisition. As a by-product, our method can also perform low-resolution relighting of dual images. We also hypothesize that our method is applicable to reconstructing dual images in a single projector - multiple cameras system.

关键词： dual photography compressive sensing Compressed sensing photography Dual images light transport aircraft observational data Mentoring transport Light

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Dense reconstruction Using 3D Object Shape Priors

Dense Reconstruction Using 3D Object Shape Priors

引用

IEEE conference on Computer vision and Pattern Recognition

作者： Amaury Dame victor A. Prisacariu Carl Y. Ren Ian Reid University of Oxford University of Adelaide

ISBN: (纸本)9781467364102

We propose a formulation of monocular SLAM which combines live dense reconstruction with shape priors-based 3D tracking and reconstruction. Current live dense SLAM approaches are limited to the reconstruction of visible surfaces. Moreover, most of them are based on the minimisation of a photo-consistency error, which usually makes them sensitive to specularities. In the 3D pose recovery literature, problems caused by imperfect and ambiguous image information have been dealt with by using prior shape knowledge. At the same time, the success of depth sensors has shown that combining joint image and depth information drastically increases the robustness of the classical monocular 3D tracking and 3D reconstruction approaches. In this work we link dense SLAM to 3D object pose and shape recovery. More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery. This leads to a system that allows for full scaled 3D reconstruction with the known object(s) segmented from the scene. The segmentation enhances the clarity, accuracy and completeness of the maps built by the dense SLAM system, while the dense 3D data aids the segmentation process, yielding faster and more reliable convergence than when using 2D image data alone.

关键词： supersonic low altitude missile Dense image data three-dimensional displays Reconstructive Surgical Procedures image information Three Dimensions Imperfect reconstructions

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A large area cosmic ray detector for the inspection of hidden high-Z materials inside containers

A large area cosmic ray detector for the inspection of hidde...

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23rd European Cosmic Ray Symposium (ECRS) / 32nd Russian Cosmic Ray conference

作者： Antonuccio, v. Bandieramonte, M. Becciani, U. Belluomo, F. Belluso, M. Billotta, S. Bonanno, G. Carbone, B. Costa, A. Fallica, G. La Monaca, v. La Rocca, P. Leonora, E. Longhitano, F. Lo Presti, D. Massimino, P. Mazzillo, M. Pappalardo, G. S. Petta, C. Piana, A. Pistagna, C. Pugliatti, C. Puglisi, M. Randazzo, N. Riggi, F. Riggi, S. Russo, G. v. Sanfilippo, D. Santagati, G. valvo, G. Zaia, A. INAF Osservatorio Astrofis Catania Catania Italy Dipartimento Fis & Astronomia Catania Italy INFN Catania Italy STMicroelect Catania Italy Insirio Srl Messina Italy Meridionale Impianti Welding Technol Catania Italy

Traditional inspection methods are of limited use to detect the presence of fissile (U, Pu) samples inside containers. To overcome such limitations, prototypes of detection systems based on cosmic muon scattering from high-Z materials are being tested worldwide. This technique does not introduce additional radiation levels, and each event contributes to the tomographic image, since the scattering process is sensitive to the charge of the atomic nuclei being traversed. A new Project, started by the Muon Portal Collaboration, plans to build a large area muon detector able to reconstruct muon tracks with good spatial and angular resolution. Experimental tests of the individual detection modules are already in progress. The design and operational parameters of the muon portal under construction are here described, together with the preliminary simulation and test results. Due to the large acceptance of the detector for cosmic rays, coupled to the good angular reconstruction of the muon tracks, it is also planned to employ such detector for cosmic ray studies, complementing its detection capabilities with a set of trigger detectors located some distance apart, in order to measure multiple muon events associated to extensive air showers.

关键词： Cosmic rays

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Spatial Super Resolution Based image reconstruction using HIBP

Spatial Super Resolution Based Image Reconstruction using HI...

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Annual IEEE India conference

作者： Rajashree Nayak S Monalisa Dipti Patra Electrical Engineering Department NIT Rourkela Odisha India Electrical Engineering Department NIT Rourkela Odisha India

ISBN: (纸本)9781479922765

Spatial image resolution explains about the pixel density in a digital image. As a result more the number of pixels more detailed visibility of information contained in the image. Hardware limitations restrict the increase in number of sensor elements per unit area in camera. Therefore an imaging system with inadequate sensor array will generate low resolution image which causes pixelization effect in them. This problem is solved in software level using signal processing techniques called super resolution based image reconstruction. In this paper super resolution based image reconstruction problem is addressed, which is used for resolution enhancement. Unlike interpolation, it takes information from multiple number of low resolution images with sub-pixel shifts and contain non-redundant data to generate a high resolution image. In this proposed reconstruction method, a hybrid iterative back projection technique is developed exploiting the notion of cuckoo search optimization algorithm in iterative back projection method. The high resolution solution from iterative back projection method is optimized using Cuckoo optimization algorithm. The performance of the proposed algorithm is found to be outperforming that of existing IBP and other interpolation based reconstruction techniques.

关键词： Super resolution Iterative Back Projection Cuckoo optimization algorithm Levy flights

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A NEW TRAJECTORY CLUSTERING ALGORITHM USING TEMPORAL SMOOTHNESS FOR MOTION SEGMENTATION

A NEW TRAJECTORY CLUSTERING ALGORITHM USING TEMPORAL SMOOTHN...

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IEEE International conference on image Processing

作者： F. Shi Z. Zhou J. Xiao W. Wu State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Ningbo Industrial Technology Research Institute CAS China

ISBN: (纸本)9781479923427

In this paper, a new trajectory clustering algorithm for motion segmentation is proposed. Our key contribution is to use temporal smoothness constraint to facilitate segmentation of incomplete trajectories, which leads to high robustness to missing data. We further show that most motions in foreground of a scene can be approximately represented by a set of translational motion models. Based on this assumption, a new clustering strategy is proposed to separate foreground objects from background. Finally, a series of experiments show that our approach is more effective and outperforms several state-of-the-art methods.

关键词： Motion Segmentation Trajectory Clustering Temporal Smoothness Motion segmentation trajectory Smoothness algorithms Clustering algorithms Cluster Analysis missing data

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X-Ray and 3D data Fusion for 3D reconstruction of Closed Receptacle Contents

X-Ray and 3D Data Fusion for 3D Reconstruction of Closed Rec...

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International conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPvT)

作者： Pierluigi Taddei vítor Sequeira European Commission Joint Research Centre Institute for Transuranium Elements Ispra Varese Italy

Closed receptacle contents can be reconstructed without invasive approaches by coupling X-ray and 3D data acquisitions. We present a solution to the reconstruction problem that exploits 3D data acquisitions of the environment to initialize the X-ray projection calibration and to align the final reconstruction to a global frame. In particular we show that the problem can be solved, without employing calibration patterns, by precisely locating the X-ray emitters and receiver plates w.r.t. a global reference frame. We then exploit matching contours extracted from the X-ray images to calibrate the projection system and to perform the reconstruction of parametric objects. We provide experimental results to measure the reconstruction precision of the overall system using both synthetic data and real experiments.

关键词： Three-dimensional displays X-ray imaging Receivers Calibration image reconstruction X-ray lasers Solid modeling

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Learning Multiple Non-Linear Sub-Spaces using K-RBMs

Learning Multiple Non-Linear Sub-Spaces using K-RBMs

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IEEE conference on Computer vision and Pattern Recognition

作者： Siddhartha Chandra Shailesh Kumar C. v. Jawahar CVIT IIIT Hyderabad Google

ISBN: (纸本)9781467364102

Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, preserving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for natural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple nonlinear subspaces. We propose a framework that uses K Restricted Boltzmann Machines (K-RBMS) to learn multiple non-linear subspaces in the raw image space. Projections of the image patches into these subspaces gives us features, which we use to build image representations. Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular image classification datasets show that representations based on our framework outperform the traditional feature representations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.

关键词： Subspace image patch image representation coupled problem Bayesian networks reconstruction error representations images Frameworks dimensionality reduction

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