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检索条件"任意字段=International Symposium on 3D Data Processing Visualization and Transmission"

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3d Object detection and Localization Using Multimodal Point Pair Features

3D Object Detection and Localization Using Multimodal Point ...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Bertram drost Slobodan Ilic MVTec Software GmbH Munchen Germany Department of Computer Science CAMP Technische Universität München Germany

Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3d objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the object's silhouette and surface appearance. The object's position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions.

关键词： Image edge detection Feature extraction Vectors Cameras Solid modeling Robustness Clutter

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An Error Correcting 3d Scanning Technique Using dual Pseudorandom Arrays

An Error Correcting 3D Scanning Technique Using Dual Pseudor...

引用

international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Udaya Wijenayake Seung-Hae Baek Soon-Yong Park School of Computer Science and Engineering Kyungpook National University Daegu South Korea

designing a low cost structured light system which can acquire 3d data in real time with great accuracy is still an ongoing topic among computer vision community as it is hard to achieve all these features together. Among various structured light systems, pseudorandom array is the most suitable technique for real time 3d reconstruction as it tends to concentrate the entire coding scheme in a single pattern. But it has the difficulty of decoding the pattern when it loses few pattern symbols. In this paper, we address this problem by introducing an error correcting 3d scanning technique. This technique consists of a two way decoding method which can decode a dual pseudorandom array and a hole-filling algorithm. In the experimental results, we show that our 3d scanning technique largely improves the reconstruction compared to the conventional methods.

关键词： Image color analysis decoding Image reconstruction Image coding Cameras Encoding Equations

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A Patch Prior for dense 3d Reconstruction in Man-Made Environments

A Patch Prior for Dense 3D Reconstruction in Man-Made Enviro...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Christian Häne Christopher Zach Bernhard Zeisl Marc Pollefeys ETH Zürich Switzerland Microsoft Research Cambridge UK

dense 3d reconstruction in man-made environments has to contend with weak and ambiguous observations due to texture-less surfaces which are predominant in such environments. This challenging task calls for strong, domain-specific priors. These are usually modeled via regularization or smoothness assumptions. Generic smoothness priors, e.g. total variation are often not sufficient to produce convincing results. Consequently, we propose a more powerful prior directly modeling the expected local surface-structure, without the need to utilize expensive methods such as higher-order MRFs. Our approach is inspired by patch-based representations used in image processing. In contrast to the over-complete dictionaries used e.g. for sparse representations our patch dictionary is much smaller. The proposed energy can be optimized by utilizing an efficient first-order primal dual algorithm. Our formulation is in particular very natural to model priors on the 3d structure of man-made environments. We demonstrate the applicability of our prior on synthetic data and on real data, where we recover dense, piece-wise planar 3dmodels using stereo and fusion of multiple depth images.

关键词： dictionaries TV Solid modeling data models Noise measurement Image reconstruction Approximation methods

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Interactive disparity Map Post-processing

Interactive Disparity Map Post-processing

引用

international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Caizhang Lin Chris Varekamp Karel Hinnen Gerard de Haan Department of Electrical Engineering Technische Universiteit Eindhoven Eindhoven Netherlands TP Vision Eindhoven Netherlands

disparity estimation has been investigated for decades. Fully automatic methods have problems in texture-less regions, around object boundaries and in occlusions regions. In this paper, we exploit user input to address these problematic areas interactively. By drawing contours and polygons, we achieve sharp disparity discontinuities and smooth disparity planes in the disparity maps. Annotations are tracked quite accurately over a number of frames. Experimental results on Middlebury data set and our own stereo video suggest that the accuracy of disparity maps can be improved significantly with limited user input.

关键词： Accuracy Vectors Stereo vision Estimation Reliability Algorithm design and analysis Tracking

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Watertight As-Built Architectural Floor Plans Generated from Laser Range data

Watertight As-Built Architectural Floor Plans Generated from...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Eric Turner Avideh Zakhor Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA USA

In this paper we describe an approach to automatically generate a floor plan for the interior environment of a building using laser scan data. This floor plan is meant to accurately indicate the positions of walls within an area of interest in the building. This proposed algorithm separates the floors of a building scan, selects a representative sampling of wall scans for each floor, and triangulates these samples to develop a watertight representation of the walls for each of the scanned areas. Curves and straight line segments are fit to these walls, in order to mitigate any registration errors from the original scans. This method is not dependent on the scanning system and can successfully process noisy scans with non-zero registration error. Most of the processing is performed after a dramatic dimensionality reduction, yielding a scalable approach. We demonstrate the effectiveness of our approach on a three-story point cloud from a commercial building as well as on the lobby and hallways of a hotel.

关键词： Buildings Vectors Smoothing methods Computational modeling Solid modeling Labeling Histograms

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Extension of Tamura Texture Features for 3d Fluorescence Microscopy

Extension of Tamura Texture Features for 3D Fluorescence Mic...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Tomas Majtner david Svoboda Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic

The image descriptors are a very useful tool in the task of classification. In biomedical image analysis, they may characterize either the shape or the internal structure of studied objects. Both characteristics are very important. When analysing cells, their shape is usually determined first. In the second step, their mask may be used for the selection of the area where the texture descriptor should be applied. In this paper, we are going to focus on the texture-based image descriptors called Tamura features. For their basic properties, they seem to be a very promising tool applicable to the biomedical image data. We will apply them to selected types of cell lines and test how they perform. We will also introduce their extension to higher dimensions and show that they give even better results than in the 2d case.

关键词： Biomedical imaging Histograms Biomedical measurements Kernel Shape Microscopy Vectors

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Lung nodule detection by KNN classifier and active contour modelling and 3d visualization

Lung nodule detection by KNN classifier and active contour m...

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international symposium on Artificial Intelligence and Signal processing (AISP)

作者： Sarah Soltaninejad Mohsen Keshani Farshad Tajeripour School of Computer and Electrical Engineering Shiraz University Shiraz Iran School of Computer and Electrical Engineering Yazd University Yazd Iran School of Engineering Shiraz University Shiraz Iran

In this paper, an automatic computer-aided detection (CAd) scheme for lung nodules detection and 3d visualization of them is proposed. There are four steps in this method. In the first step, we use median filter for removing noise from slices and morphological operation for image enhancement then we segment lung regions from the CT data by using adaptive threshold algorithm and active contour modeling. The second step is nodule detection that contains two parts: feature extraction and classification. For extracting feature, we use a 2d stochastic features for accurate nodule detection and a 3d anatomical features for reducing the value of false positive(FP). Furthermore, KNN classifier is used in this paper. In the third step, the nodule contours is extracted by active contour modeling. In the final step, 3d visualization technique is applied on the segmented nodule to represent better visual results. At the end, experimental results of our method are indicated with good performance comparing with some other efficient methods. We achieved 90% detection rate and 5.63FP/Scan.

关键词： Lungs Image segmentation Feature extraction Computed tomography Active contours data visualization Solid modeling

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A Robust Multi-camera 3d Ellipse Fitting for Contactless Measurements

A Robust Multi-camera 3D Ellipse Fitting for Contactless Mea...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Filippo Bergamasco Luca Cosmo Andrea Albarelli Andrea Torsello Dipartimento di Scienze Ambientali Informatica e Statistica Universitá Ca' Foscari Venice Italy

Ellipses are a widely used cue in many 2d and 3d object recognition pipelines. In fact, they exhibit a number of useful properties. First, they are naturally occurring in many man-made objects. Second, the projective invariance of the class of ellipses makes them detectable even without any knowledge of the acquisition parameters. Finally, they can be represented by a compact set of parameters that can be easily adopted within optimization tasks. While a large body of work exists in the literature about the localization of ellipses as 2d entities in images, less effort has been put in the direct localization of ellipses in 3d, exploiting images coming from a known camera network. In this paper we propose a novel technique for fitting elliptical shapes in 3d space, by performing an initial 2d guess on each image followed by a multi-camera optimization refining a 3d ellipse simultaneously on all the calibrated views. The proposed method is validated both with synthetic data and by measuring real objects captured by a specially crafted imaging head. Finally, to evaluate the feasibility of the approach within real-time industrial scenarios, we tested the performance of a GPU-based implementation of the algorithm.

关键词： Cameras Equations Noise Level set Optimization Lips

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Online Algorithms for Classification of Urban Objects in 3d Point Clouds

Online Algorithms for Classification of Urban Objects in 3D ...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Ioannis Stamos Olympia Hadjiliadis Hongzhong Zhang Thomas Flynn Hunter College CUNY New York NY USA Brooklyn College CUNY Brooklyn NY USA Columbia University New York NY USA Graduate Center CUNY New York NY USA

The current technology in stationary laser range-scanning enables high-resolution acquisition of 3d data in a sequential fashion. Traditionally, range scans are processed offline after acquisition, which significantly slows down the procedure. In this work we alleviate this limitation by developing low-complexity, online detection and classification algorithms. These algorithms are innovative in that they classify points into 5 distinct classes (vegetation, vertical, horizontal, car and curb regions) and robustly determine the level of the ground without requiring any prior training or parameter estimation. To construct these algorithms we extract cleverly chosen summary statistics which significantly reduce the dimensionality of the data. This reduction enables us to contrast the different classes by appropriately chosen Markov models and then use online techniques to detect a transition from one Markov model to the other. The identification of the ground level is further achieved by taking advantage of statistical properties of the distribution of the summary statistics. Our algorithms also use contextual cues to verify the existence of specific classes of objects. All our algorithms take advantage of the sequential nature of data acquisition by running in parallel and labeling points on-the-fly. Thus, these algorithms can be potentially integrated with the scanner's hardware and provide the foundation for the construction of high-resolution 3d data scanners that classify data as acquired. We have run experiments using complex urban range scans and have evaluated the classification accuracy against ground-truth.

关键词： Vegetation mapping Hidden Markov models Classification algorithms Vectors Vegetation Markov processes Surface emitting lasers

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Handling Occlusions for Accurate 3d Urban Cartography: A New Approach Based on Characterization and Multiple Passages

Handling Occlusions for Accurate 3D Urban Cartography: A New...

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international Conference on 3d Imaging, Modeling, processing, visualization and transmission (3dIMPVT)

作者： Ahmad K. Aijazi Paul Checchin Laurent Trassoudaine Institut Pascal Clermont Universite Universite Blaise Pascal Clermont Ferrand France CNRS UMR 6602 Institut Pascal Aubiere France

In this paper we present a new occlusion handling technique which successfully addresses the intricate problem of extraction of occluded features for urban landscape analysis and cartography. This new method is based on temporal integration in which multiple sessions or passages are used to complete occluded features in a 3d cartographic image. 3d image obtained from each passage is first characterized and classified into three main object classes: Permanently static, Temporarily static and Mobile using inference based on basic reasoning and a new point matching technique, intelligently exploiting the different viewing angles of the mounted Lidar sensors. All the Temporarily static and Mobile objects, considered as occluding objects, are removed from the image/scene leaving behind a perforated 3d image of the cartography. This perforated image is then updated by similar subsequent perforated images, obtained on different days and hours of the day, filling in the holes and completing the missing features of the urban cartography. This ensures that the resulting 3d image of the cartography is most accurate containing only the exact and actual permanent features. Separate update and reset functions are specially added to increase robustness of the method. The proposed method is evaluated on a standard data set demonstrating its efficacy and suitability for different applications.

关键词： Buildings Mobile communication Sensors Laser radar Lasers Global Positioning System Roads

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