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Sensor data Fusion for Textured reconstruction and Virtual Representation of Alpine Scenes 8

Sensor Data Fusion for Textured Reconstruction and Virtual R...

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17th SPIE conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Haufel, Gisela Bulatov, Dimitri Solbrig, Peter Fraunhofer Inst Optron Syst Technol & Image Explo Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510613218;9781510613201

The concept of remote sensing is to provide information about a wide-range area without making physical contact with this area. If, additionally to satellite imagery, images and videos taken by drones provide a more up-to-date data at a higher resolution, or accurate vector data is downloadable from the Internet, one speaks of sensor data fusion. The concept of sensor data fusion is relevant for many applications, such as virtual tourism, automatic navigation, hazard assessment, etc. In this work, we describe sensor data fusion aiming to create a semantic 3D model of an extremely interesting yet challenging dataset: An alpine region in Southern Germany. A particular challenge of this work is that rock faces including overhangs are present in the input airborne laser point cloud. The proposed procedure for identification and reconstruction of overhangs from point clouds comprises four steps: Point cloud preparation, filtering out vegetation, mesh generation and texturing. Further object types are extracted in several interesting subsections of the dataset: Building models with textures from UAV (Unmanned Aerial Vehicle) videos, hills reconstructed as generic surfaces and textured by the orthophoto, individual trees detected by the watershed algorithm, as well as the vector data for roads retrieved from openly available shape files and GPS-device tracks. We pursue geo-specific reconstruction by assigning texture and width to roads of several pre-determined types and modeling isolated trees and rocks using commercial software. For visualization and simulation of the area, we have chosen the simulation system Virtual Battlespace 3 (VBS3). It becomes clear that the proposed concept of sensor data fusion allows a coarse reconstruction of a large scene and, at the same time, an accurate and up-to-date representation of its relevant subsections, in which simulation can take place.

关键词： Alpine Area Sensor data Fusion 3D-object extraction Rock face Overhang Road Visualisation Simulation

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3DMatch: Learning Local Geometric Descriptors from RGB-D reconstructions 30

3DMatch: Learning Local Geometric Descriptors from RGB-D Rec...

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

作者： Zeng, Andy Song, Shuran Niessner, Matthias Fisher, Matthew Xiao, Jianxiong Funkhouser, Thomas Princeton Univ Princeton NJ 08544 USA Stanford Univ Stanford CA 94305 USA AutoX San Jose CA USA Adobe Syst San Jose CA USA

ISBN: (纸本)9781538604571

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3D Match, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3D Match consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://***.

关键词： Three-dimensional displays image reconstruction Solid modeling Training Geometry Surface reconstruction

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Feature-enhanced Surfaces from incomplete Point Cloud with Segmentation and Curve Skeleton Information 7

Feature-enhanced Surfaces from Incomplete Point Cloud with S...

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7th International conference on Virtual Reality and Visualization (ICVRV)

作者： Wang, Meili Fan, Yuling Guo, Shihui Liao, Minghong Chang, Jian He, Dongjian Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Xiamen Univ Sch Software Xiamen Fujian Peoples R China Bournemouth Univ Natl Ctr Comp Animat Bournemouth Dorset England Northwest A&F Univ Coll Mech & Elect Engn Yangling Shaanxi Peoples R China

ISBN: (纸本)9781538626368

Raw data of point cloud is often noisy and with topological defects ( such as holes), which cause problems including faulty connection and inaccurate structure. As a result, the surface reconstruction of point cloud data is a highly challenging problem. This work proposes a novel method, which improves the surface quality compared with existing methods. Our method combines both the local detailed features and the global topological information during the reconstruction process. To facilitate the feature refinement, we first pre-process the point cloud data by relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details. We then identify the topological information by segmenting the geometry and constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.

关键词： Surface reconstruction Segmentation Enhanced features Curve skeleton

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PERFORMANCE EVALUATION OF PATTERNS FOR image-BASED 3D MODEL reconstruction OF TEXTURELESS OBJECTS 7

PERFORMANCE EVALUATION OF PATTERNS FOR IMAGE-BASED 3D MODEL ...

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7th International conference on 3D Immersion (IC3D)

作者： Hafeez, Jahanzeb Hamacher, Alaric Kwon, SoonChul Lee, SeungHyun Kwangwoon Univ Grad Sch Informat Contents Seoul South Korea Gachon Univ Dept Comp Sci Seongnam South Korea

ISBN: (纸本)9781538646557

This paper evaluates the performance of patterns used to solve a very challenging problem in close range photogrammetry and computer vision when the surface of the object or scene is textureless. Three dimensional surface modeling from arbitrary viewpoints is an active field of research due to its wide range of applications. However, structure-from-motion, a common approach for surface modeling of the objects that are not well textured fails due to insufficient discriminative features in the images and hence results in incomplete and inaccurate three dimensional model of the surface. Mainly, two approaches have been used widely for 3D reconstruction of such kind of objects. First uses a structured light or coded pattern, and second uses a random pattern that provides artificial markers on the surface of interesting object. In this paper, second approach is implemented that helps point-based features, such as SIFT, to find discriminative features from arbitrary viewpoints taken from the same object surface. We evaluate the performance of patterns with respect to quality of reconstruction of the surface of a textureless object. At the end, a comparison scheme between reconstructed model and the ground truth data is also presented and results are evaluated.

关键词： Structure-from-motion SIFT 3D reconstruction patterns analysis surface comparison

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Simultaneous Dose Reduction and Scatter Correction for 4D Cone-Beam Computed Tomography

Simultaneous Dose Reduction and Scatter Correction for 4D Co...

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IEEE Nuclear Science Symposium and Medical Imaging conference (NSS/MIC) / 24th International Symposium on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Zhao, Cong Zhong, Yuncheng Wang, Jing Jin, Mingwu Univ Texas Arlington Dept Phys POB 19059 Arlington TX 76019 USA Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA

ISBN: (纸本)9781538622827

Four dimensional (4D) X-ray cone-beam computed tomography (CBCT) can lead to a precise radiation therapy through accurate tumor motion modeling. However, the repeated use of 4D CBCT in a fractional treatment session raises the concern of radiation dose. In addition, the scatter contamination results in shading artifacts and low contrast in reconstructed images if not corrected. In this work, we propose a novel method to use a moving blocker (MB) to achieve the dose reduction and scatter correction simultaneously for 4D CBCT, termed "MB 4D". The lead blocker is placed between the X-ray source and the patient and moving back and forth in a certain direction during the CBCT data collection. Not only is part of radiation blocked, but also the scatter signal can be estimated from the blocked region for scatter correction (SC). The spatial total variation (TV) minimization and motion correlations are used to improve 4D reconstruction with incomplete data and to derive the motion fields. As an initial test of this novel method, we used a simulation study using the 4D non-uniform rational B-spline cardiac-torso (NCAT) phantom. Our results show that with 1/3 dose reduction the proposed MB-4D method can achieve much better image quality than 3D reconstruction with SC ("MB-3D") and 4D reconstruction without SC ("4D"), measured by structural similarity (SSIM) index, root mean square error (RMSE) of CT numbers, and time attenuation coefficient (TAC) curves.

关键词： Computed tomography image reconstruction Physics Biomedical imaging X-ray imaging Three-dimensional displays Tumors

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Quick multi-temporal approach to get cloudless improved multispectral imagery for large geographical areas 8

Quick multi-temporal approach to get cloudless improved mult...

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17th SPIE conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Colaninno, Nicola Marambio Castillo, Alejandro Roca Cladera, Josep UPC Ctr Land Policy & Valuat CPSV Av Diagonal 649 Barcelona Spain Univ Nacl Autonoma Mexico Fac Arquitectura Div Estudios Posgrad Cd Univ Mexico City DF Mexico

ISBN: (纸本)9781510613218;9781510613201

The demand for remotely sensed data is growing increasingly, due to the possibility of managing information about huge geographic areas, in digital format, at different time periods, and suitable for analysis in GIS platforms. However, primary satellite information is not such immediate as desirable. Beside geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical images. In terms of land cover, such a contamination is intended as missing information and should be replaced. Generally, image reconstruction is classified according to three main approaches, i.e. in-painting-based, multispectral-based, and multitemporal-based methods. This work relies on a multitemporal-based approach to retrieve uncontaminated pixels for an image scene. We explore an automatic method for quickly getting daytime cloudless and shadow-free image at moderate spatial resolution for large geographical areas. The process expects two main steps: a multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase, based on automatic selection of uncontaminated pixels from an image stack. The result is a composite image based on middle values of the stack, over a year. The assumption is that, for specific purposes, land cover changes at a coarse scale are not significant over relatively short time periods. Because it is largely recognized that satellite imagery along tropical areas are generally strongly affected by clouds, the methodology is tested for the case study of the Dominican Republic at the year 2015;while Landsat 8 imagery are employed to test the approach.

关键词： Landsat cloudless imagery image enhancement land cover monitoring

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2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

2nd International Workshop on Simulation and Synthesis in Me...

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2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

ISBN: (纸本)9783319681269

The proceedings contain 11 papers. The special focus in this conference is on Simulation and Synthesis in Medical Imaging. The topics include: Adversarial image synthesis for unpaired multi-modal cardiac data;deep MR to CT synthesis using unpaired data;synthesizing CT from ultrashort echo-time MR images via convolutional neural networks;a supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis;region-enhanced joint dictionary learning for cross-modality synthesis in diffusion tensor imaging;virtual PET images from CT data using deep convolutional networks;semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets;high order slice interpolation for medical images;a monte carlo framework for low dose CT reconstruction testing;multimodal simulations in live cell imaging;medical image processing and numerical simulation for digital hepatic parenchymal blood flow.

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Comparative Evaluation of Hand-Crafted and Learned Local Features 30

Comparative Evaluation of Hand-Crafted and Learned Local Fea...

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

作者： Schonberger, Johannes L. Hardmeier, Hans Sattler, Torsten Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781538604571

Matching local image descriptors is a key step in many computer vision applications. For more than a decade, hand- crafted descriptors such as SIFT have been used for this task. Recently, multiple new descriptors learned from data have been proposed and shown to improve on SIFT in terms of discriminative power. This paper is dedicated to an extensive experimental evaluation of learned local features to establish a single evaluation protocol that ensures comparable results. In terms of matching performance, we evaluate the different descriptors regarding standard criteria. However, considering matching performance in isolation only provides an incomplete measure of a descriptor's quality. For example, finding additional correct matches between similar images does not necessarily lead to a better performance when trying to match images under extreme viewpoint or illumination changes. Besides pure descriptor matching, we thus also evaluate the different descriptors in the context of image-based reconstruction. This enables us to study the descriptor performance on a set of more practical criteria including image retrieval, the ability to register images under strong viewpoint and illumination changes, and the accuracy and completeness of the reconstructed cameras and scenes. To facilitate future research, the full evaluation pipeline is made publicly available.

关键词： images Descriptors Subject Headings Lighting illumination image retrieval Sifting BENCHMARKS COMPARATIVE EVALUATIONS computer vision completeness

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Dictionary Learning from incomplete data for Efficient image Restoration

Dictionary Learning from Incomplete Data for Efficient Image...

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European Signal Processing conference

作者： Valeriya Naumova Karin Schnass Section for Computing and Software Simula Research Laboratory Fornebu Norway Department of Mathematics University of Innsbruck Innsbruck Austria

In real-world image processing applications, the data is high dimensional but the amount of high-quality data needed to train the model is very limited. In this paper, we demonstrate applicability of a recently presented method for dictionary learning from incomplete data, the so-called Iterative Thresholding and K residual Means for Masked data, to deal with high-dimensional data in an efficient way. In particular, the proposed algorithm incorporates a corruption model directly at the dictionary learning stage, also enabling reconstruction of the low-rank component again from corrupted signals. These modifications circumvent some difficulties associated with the efficient dictionary learning procedure in the presence of limited or incomplete data. We choose an image inpainting problem as a guiding example, and further propose a procedure for automatic detection and reconstruction of the low-rank component from incomplete data and adaptive parameter selection for the sparse image reconstruction. We benchmark the efficacy and efficiency of our algorithm in terms of computing time and accuracy on colour, 3D medical, and hyperspectral images by comparing it to its dictionary learning counterparts.

关键词： Dictionaries Signal processing algorithms Machine learning Manganese image color analysis image reconstruction Matching pursuit algorithms

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Synergy-driven performance enhancement of vision-based 3D hand pose reconstruction 1

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6th International conference on Wireless Mobile Communication and Healthcare, MobiHealth 2016

作者： Ciotti, Simone Battaglia, Edoardo Oikonomidis, Iason Makris, Alexandros Tsoli, Aggeliki Bicchi, Antonio Argyros, Antonis A. Bianchi, Matteo Research Center E. Piaggio University of Pisa Largo L. Lazzarino 1 Pisa56126 Italy via Morego 30 Genova16163 Italy Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece

ISBN: (数字)9783319588773

ISBN: (纸本)9783319588766

In this work we propose, for the first time, to improve the performance of a Hand Pose reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

关键词： image reconstruction

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