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

共 1581 条记录，以下是351-360 订阅

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Model-based reconstruction of organ surfaces from two-dimensional CT or MRT data of the head

Model-based reconstruction of organ surfaces from two-dimens...

引用

Medical Imaging 1999 conference - image Processing

作者： von Klinski, S Glausch, A Tolxdorff, T Free Univ Berlin Hosp Benjamin Franklin Dept Med Informat D-1000 Berlin Germany

ISBN: (纸本)0819431338

Surface-based interpolation and registration, radiation treatment, and three-dimensional visualization of two-dimensional sliced data from CT or MRT require a precise reconstruction of three-dimensional organ surfaces from two-dimensional segmentation results. Current surface-reconstruction algorithms are based on surface triangulations using heuristics to correlate and connect adjacent object slices. The approaches described in the literature can be divided into triangulations using optimization procedures, Delauny triangulations, and topology-based correlations. All approaches assume a global and invariant vertically oriented correlation strategy that can be applied equally to every organ and every slice. Surface and correlation characteristics vary greatly among bony structures and organs such as the eyes and the brain. An adjusted reconstruction of each organ according to its individual tissue characteristics is necessary to avoid errors in following processing steps such as interpolation, registration, and radiation treatment. To this end, we have designed a model-based surface-reconstruction algorithm that takes individual surface characteristics into account and allows the integration of anatomical knowledge. Three-dimensional surface models are generated from sliced data or any other source of anatomical knowledge. These models are later adjusted to the segmentations, compensating for artifacts and incomplete data.

关键词： reconstruction triangulation warping interpolation

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Phase imaging from noisy interferometric observations via sparse coding of phase and amplitude 1

Phase imaging from noisy interferometric observations via sp...

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1st International Scientific School on Methods of Digital image Processing in Optics and Photonics

作者： Katkovnik, v. Tampere Univ Technol Dept Signal Proc Tampere 33720 Finland

Phase-shifting interferometry is a coherent optical method that combines high accuracy with high measurement speeds. However, despite its advantageous properties, the inference of the object amplitude and the phase, herein termed wavefront reconstruction is not a trivial task owing to, namely, the Poissonian noise associated with the measurement process and to the 27 phase periodicity of the observation mechanism. In this paper, we formulate the wavefront reconstruction (phase and amplitude) as an inverse problem where the amplitude and the absolute phase are assumed to admit sparse linear representations in suitable sparsifying transforms. We introduce the sparse phase and amplitude reconstruction (SPAR) algorithm. SPAR takes into full consideration the Poissonian (photon counting) measurements and uses data adaptive BM3D frames as a sparse representation for amplitude and absolute phase.

关键词： Inverse problems

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Photoacoustic image reconstruction from full field data in heterogeneous media

Photoacoustic image reconstruction from full field data in h...

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conference on Photons Plus Ultrasound - Imaging and Sensing

作者： Haltmeier, Markus Zangerl, Gerhard Nguyen, Linh v. Nuster, Robert Univ Innsbruck Dept Math Technikerstr 13 A-6020 Innsbruck Austria Univ Idaho Dept Math 875 Perimeter Dr Moscow ID 83844 USA Karl Franzens Univ Graz Dept Phys Univ Pl 5 Graz Austria

ISBN: (纸本)9781510623996

We consider image reconstruction in full-field photoacoustic tomography, where 2D projections of the full 3D acoustic pressure distribution at a given time T > 0 are collected. We discuss existing results on the stability and uniqueness of the resulting image reconstruction problem and review existing reconstruction algorithms. Open challenges are also mentioned. Additionally, we introduce novel one-step reconstruction methods allowing for a variable speed of sound. We apply preconditioned iterative and variational regularization methods to the one-step formulation. Numerical results using the one-step formulation are presented, together with a comparison with the previous two-step approach for full-field photoacoustic tomography.

关键词： Photoacoustic Tomography image reconstruction full field detection forward-backward splitting one-step reconstruction heterogeneous medium

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USCT reference database: conclusions from the first SPIE USCT data challenge and future directions

USCT reference database: conclusions from the first SPIE USC...

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conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Ruiter, Nicole v. Zapf, Michael Hopp, Torsten Gemmeke, Hartmut van Dongen, Koen W. A. Camacho, Jorge Herraiz, Joaquin L. Liva, Mailyn Perez Udias, Jose M. Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Delft Univ Technol Lab Acoust Wavefield Imaging Delft Netherlands Spanish Natl Res Council Ultrasound Syst & Technol Grp Madrid Spain Univ Complutense Madrid Nucl Phys Grp Madrid Spain

ISBN: (数字)9781510616509

ISBN: (纸本)9781510616509;9781510616493

Ultrasound Computer Tomography is an exciting new technology mostly aimed at breast cancer imaging. Due to the complex interaction of ultrasound with human tissue, the large amount of raw data, and the large volumes of interest, both image acquisition and image reconstruction are challenging. Following the idea of open science, the long term goal of the USCT reference database is establishing open and easy to use data and code interfaces and stimulating the exchange of available reconstruction algorithms and raw data sets of different USCT devices. The database was established with freely available and open licensed USCT data for comparison of reconstruction algorithms, and will be maintained and updated. Additionally, the feedback about data and system architecture of the scientists working on reconstruction methods will be published to help to drive further development of the various measurement setups.

关键词： Ultrasound computer tomography data challenge open science open data

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FPGA Implementation of Satellite image Fusion Using Wavelet Substitution Method

FPGA Implementation of Satellite Image Fusion Using Wavelet ...

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Science and Information conference (SAI)

作者： Mamatha, G. Sumalatha, v. Lakshmaiah, M. v. JNTUACEA Dept ECE Ananthapuramu India

ISBN: (纸本)9781479985470

image fusion is a process of merging the relevant information from a set of images into a single image. The goal of image fusion in remote sensing is to create new images that contain both low spatial resolution multispectral data (color information) and high spatial resolution panchromatic data (details).. The choice of appropriate wavelet filters for the design and implementation on FPGA, a detailed analysis has been carried out in MATLAB Simulink R2010b software using averaging, additive and substitutive fusion rules. In this paper substitutive rule using the Haar, Daubechies 3 (db3) and Cohen Daubechies Feauveau (CDF) 9/7 filters are used for image decomposition and reconstruction. CDF 9/7 is found to be the best filter and is chosen for FPGA implementation. Single level 2D-DWT based image fusion has been performed using substitutive method and then the hardware software co-simulation design has been synthesized in Xilinx ISE 13.1 and implemented on ML605 virtex 6 FPGA kit. from the results, it is observed that the design consumes a total power of 4.349W and operates at a maximum frequency of 849.618MHz.

关键词： image Fusion image Resampling image Registration DWT FPGA

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Multibody vSLAM with Relative Scale Solution for Curvilinear Motion reconstruction

Multibody VSLAM with Relative Scale Solution for Curvilinear...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Namdev, Rahul Kumar Krishna, K. Madhava Jawahar, C. v. IIIT Hyderabad Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467356411;9781467356435

A solution to the relative scale problem where reconstructed moving objects and the stationary world are represented in a unified common scale has proven equivalent to a conjecture. Motion reconstruction from a moving monocular camera is considered ill posed due to known problems of observability. We show for the first time several significant motion reconstruction of outdoor vehicles moving along non-holonomic curves and straight lines. The reconstructed motion is represented in the unified frame which also depicts the estimated camera trajectory and the reconstructed stationary world. This is possible due to our Multibody vSLAM framework with a novel solution for relative scale proposed in the current paper. Two solutions that compute the relative scale are proposed. The solutions provide for a unified representation within four views of reconstruction of the moving object and are thus immediate. In one, the solution for the scale is that which satisfies the planarity constraint of the object motion. The assumption of planar object motion while being generic enough is subject to stringent degenerate situations that are more widespread. To circumvent such degeneracies we assume that the object motion to be locally circular or linear and find the relative scale solution for such object motions. Precise reconstruction is achieved in synthetic data. The fidelity of reconstruction is further vindicated with reconstructions of moving cars and vehicles in uncontrolled outdoor scenes.

关键词： SLAM (robots) cameras constraint satisfaction problems image motion analysis image reconstruction

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Can you tell a face from a HEvC bitstream? 1

Can you tell a face from a HEVC bitstream?

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1st IEEE conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Alvar, Saeed Ranjbar Choi, Hyomin Bajic, Ivan v. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781538618578

image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics - face detection - and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency video Coding (HEvC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEvC entropy decoder.

关键词： face detection HEvC deep learning convolutional neural network

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Loss Function Optimization Based on Adversarial Networks 5

Loss Function Optimization Based on Adversarial Networks

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5th International conference on Fuzzy Systems and data Mining (FSDM) held in conjunction with the 9th International conference on Electronics, Communications and Networks (CECNet)

作者： Liu, Wei Meng, Fan-Yang Liang, Yong-Sheng Yang, Huo-Xiang Wang, Chang-Wei Shenzhen Inst Informat Technol Shenzhen 518172 Guangdong Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Shenzhen Univ Shenzhen 518055 Peoples R China

ISBN: (纸本)9781643680194;9781643680187

Autoencoder networks have been successfully applied to dimensionality reduction and information retrieval tasks. Lower-dimensional representations can improve performance on many tasks, such as image compression, reconstruction and clustering. The Mean Square Error (MSE) is commonly used as a loss function for autoencoder networks. The loss function suffers from performance degradation because it lacks the component distribution information of the input image. In the deep learning literature, recent works have shown the benefits of using adversarialbased losses to improve the performance on various image reconstruction and clustering tasks. This paper proposes a new algorithm which is called GAN-WMSE to generate weights for the MSE based on adversarial networks. With the distribution information integrated into the loss function, the autoencoder network and the adversarial weight network are jointly trained. Experiments on different image datasets show that the improved autoencoder networks employing our loss function can increase performance by 9.8% for PSNR, 10.3% for SSIM in image reconstruction, and 6.2% for image clustering.

关键词： Adversarial networks Autoencoder network image clustering image reconstruction Loss function

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Effects of prior MRI information on image reconstruction in diffuse optical tomography.

Effects of prior MRI information on image reconstruction in ...

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conference on Novel Optical Instrumentation for Biomedical Applications

作者： Torregrossa, M Zint, Cv Poulet, P UMR 7004 CNRS Inst Phys Biol F-67085 Strasbourg France

ISBN: (纸本)0819450138

We describe a method to increase the speed of convergence for the simultaneous reconstruction of absorption and scattering images in Diffuse Optical Tomography (DOT). We used the diffusion approximation of the radiative transfer equation and the Finite Element Method (FEM) to solve the forward problem. The absorption and reduced scattering images are reconstructed by inverting the distribution of the moments of the time-dependent detected light flux. The inverse problem is solved with an optimization algorithm like ART or Conjugate Gradient. This ill-posed inverse problem can be simplified by using a priori knowledge of the studied objects. In this paper, we consider that DOT is a functional imaging technique that can be complemented by an anatomical imaging technique like Magnetic Resonance Imaging (MRI). We used anatomic information obtained from MRI as prior knowledge to compute optical absorption and scattering images. In a first step, MRI segmented images were only used to mesh our phantoms, with a finer resolution around boundaries. In a second step, we computed optical images with homogeneous properties from the segmented MRI image, in order to initialise our optimisation process. These two initialisations yield better reconstructed images. reconstruction from simulated and experimental data will be presented.

关键词： optical tomography image reconstruction a-priori knowledge

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Wave front distortion based fluid flow imaging

Wave front distortion based fluid flow imaging

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conference on image Processing - Machine vision Applications vI

作者： Iffa, Emishaw Heidrich, Wolfgang Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada

ISBN: (纸本)9780819494344

In this paper, a transparent flow surface reconstruction based on wave front distortion is investigated. A camera lens is used to focus the image formed by the micro-lens array to the camera imaging plane. The irradiance of the captured image is transformed to frequency spectrum and then the x and y spatial components are separated. A rigid spatial translation followed by low pass filtering yields a single frequency component of the image intensity. Index of refraction is estimated from the inverse Fourier transform of the spatial frequency spectrum of the irradiance. The proposed method is evaluated with synthetic data of a randomly generated index of refraction value and used to visualize a fuel injection volumetric data.

关键词： flow visualization Fourier demodulation index of refraction light field transparent flow wavefront reconstruction

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