This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction ...
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This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.
This work develops and investigates a formalism for accurate motion-compensated reconstruction, including elaborate consideration of scattered and random coincidences, which at the same time is particularly feasible i...
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This work develops and investigates a formalism for accurate motion-compensated reconstruction, including elaborate consideration of scattered and random coincidences, which at the same time is particularly feasible in the context of high-resolution PET. The method takes into consideration normally-detected projection data which are not detected due to motion. Furthermore, it incorporates information from all detected events, particularly those which, following correction for motion, fall outside the Fov (e.g. axially or through detector gaps), thus satisfying a mathematical requirement, elaborated in the text, that would allow accurate motion averaging of sensitivity factors in image-space (as opposed to projection-space). The proposed method has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work.
We address the reconstruction of a 3D imagefrom a set of incomplete X-ray tomographic data. In the case where the image is composed of one or several objects lying in a uniform background, we define a sparse paramete...
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ISBN:
(纸本)0780391349
We address the reconstruction of a 3D imagefrom a set of incomplete X-ray tomographic data. In the case where the image is composed of one or several objects lying in a uniform background, we define a sparse parameterization by considering the active voxels, i.e., the voxels that do not lay inside the background. Estimation of the active voxel densities is performed using the maximum a posteriori (MAP) estimator. In order to implement sparse parameter estimation, we design an original multiresolution scheme, which handles coarse to fine resolution images. This scheme affords automatic selection of active voxels at each resolution level, and provides a drastic decrease of the computation time. We finally show the performance of our method on synthetic data.
We analyze how an action of a qubit channel (map) can be estimated from the measured data that are incomplete or even inconsistent. That is, we consider situations when measurement statistics is insufficient to determ...
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We analyze how an action of a qubit channel (map) can be estimated from the measured data that are incomplete or even inconsistent. That is, we consider situations when measurement statistics is insufficient to determine consistent probability distributions. As a consequence either the estimation (reconstruction) of the channel completely fails or it results in an unphysical channel (i.e., the corresponding map is not completely positive). We present a regularization procedure that allows us to derive physically reasonable estimates (approximations) of quantum channels. We illustrate our procedure on specific examples and we show that the procedure can be also used for a derivation of optimal approximations of operations that are forbidden by the laws of quantum mechanics (e.g., the universal NOT gate).
In this paper, we present a novel method for intra-operative registration directly from monocular endoscopic images. This technique has the potential to provide a more accurate surface registration at the surgical sit...
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In this paper, we present a novel method for intra-operative registration directly from monocular endoscopic images. This technique has the potential to provide a more accurate surface registration at the surgical site than existing methods. It can operate autonomously from as few as two images and can be particularly useful in revision cases where surgical landmarks may be absent. A by-product of video registration is an estimate of the local surface structure of the anatomy, thus providing the opportunity to dynamically update anatomical models as the surgery progresses. Our approach is based on a previously presented method [Burschka, D., Hager, G.D., 2004. v-GPS (SLAM): - vision-based inertial system for mobile robots. In: Proceedings of ICRA, 409-415] for reconstruction of a scaled 3D model of the environment from unknown camera motion. We use this scaled reconstruction as input to a PCA-based algorithm that registers the reconstructed data to the CT data and recovers the scale and pose parameters of the camera in the coordinate frame of the CT scan. The result is used in an ICP registration step to refine the registration estimates. The details of our approach and the experimental results with a phantom of a human skull and a head of a pig cadaver are presented in this paper. (c) 2005 Elsevier B.v. All rights reserved.
We analyze how an action of a qubit channel (map) can be estimated from the measured data that are incomplete or even inconsistent. That is, we consider situations when measurement statistics is insufficient to determ...
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We analyze how an action of a qubit channel (map) can be estimated from the measured data that are incomplete or even inconsistent. That is, we consider situations when measurement statistics is insufficient to determine consistent probability distributions. As a consequence either the estimation (reconstruction) of the channel completely fails or it results in an unphysical channel (i.e., the corresponding map is not completely positive). We present a regularization procedure that allows us to derive physically reasonable estimates (approximations) of quantum channels. We illustrate our procedure on specific examples and we show that the procedure can be also used for a derivation of optimal approximations of operations that are forbidden by the laws of quantum mechanics (e.g., the universal NOT gate).
The aim of seismic imaging is to reconstruct subsurface reflectivity from scattered acoustic data. In standard reconstruction techniques, the reflectivity model parameters are usually defined as a grid of point scatte...
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ISBN:
(纸本)0819456470
The aim of seismic imaging is to reconstruct subsurface reflectivity from scattered acoustic data. In standard reconstruction techniques, the reflectivity model parameters are usually defined as a grid of point scatterers over the area or volume of the subsurface to be imaged. We propose an approach to subsurface imaging using the Dual Tree Complex Wavelet Transform (DT-CWT) as a basis for the reflectivity. This basis is used in conjunction with an iterative optimization which frames the problem as a linearized inverse scattering problem. We demonstrate the method on synthetic data and a marine seismic data set acquired over the Gippsland Basin near Australia. The technique is shown to reduce noise and processing artifacts while preserving discontinuities. It is likely to be particularly useful in cases where the acquired date is incomplete.
Previously we have developed a 3D statistical method, which incorporates point spread function in the system matrix. Comparing to our standard 3D reconstruction algorithm with simple line integral model, this method r...
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ISBN:
(纸本)0780392213
Previously we have developed a 3D statistical method, which incorporates point spread function in the system matrix. Comparing to our standard 3D reconstruction algorithm with simple line integral model, this method resulted in improved resolution and noise behavior of phantom images. reconstruction of clinical data is usually performed from rebinned projection data, resulting in downsampled patient image. Efficient algorithm implementation allows reconstruction of patient datafrom rebinned data in time close to clinically acceptable. Then numerical observer study to evaluate algorithms performance is feasible. We use channelized Hotelling observer to establish hot spot delectability in noisy images for each method. Patient-derived phantom was used in computer simulations when a few known location tumors of various size and contrasts were added to patient image. Number of iterations and postsmoothing amount, optimal for hot spot detectability, were also investigated.
When we are interested to the detection of the roughness features by means of the 3D reconstruction, based on photometric stereo techniques, an important problem is the elimination of the brightness variation due to d...
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When we are interested to the detection of the roughness features by means of the 3D reconstruction, based on photometric stereo techniques, an important problem is the elimination of the brightness variation due to different light conditions which can alter the response. This paper will concentrate on presenting results of a new method for eliminating this problem. Every pixel of a picture gives only one number: the brightness of the corresponding point on the object, whereas the surface orientation is described by a normal vector that has two degrees of freedom. The level of brightness depends on many factors as well as the homogeneity of reflection properties of the material or its physical continuity and the surface smoothness or roughness. In this work we will show how the application of the Discrete Wavelet Transform (DWT) to the processing of some images, captured on different light conditions, permits to solve the problem of emphasizing roughness features of a metallic surface. Wavelet transforms can model irregular data patterns such as sharp changes, better than the Fourier transforms and standard statistical procedures (e.g., parametric and non-parametric regressions) and provide a multiresolution approximation to the data. Here we propose, also, a non-parametric method, based on the wavelet theory, for the estimation of the threshold level of a gray levels distribution, obtained from the intensity image matrix. (c) 2005 Elsevier B.v. All rights reserved.
In this paper we present an image-based algorithm for surface reconstruction of moving garment from multiple calibrated video cameras. Using a color-coded cloth texture, we reliably match circular features between dif...
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In this paper we present an image-based algorithm for surface reconstruction of moving garment from multiple calibrated video cameras. Using a color-coded cloth texture, we reliably match circular features between different camera views. As surface model we use air a priori known triangle mesh. By identifying the mesh vertices with texture elements we obtain a consistent parameterization of the surface over time without further processing. Missing data points resulting from self-shadowing are plausibly interpolated by minimizing a thin-plate functional. The deforming geometry can be used for different graphics applications, e.g. for realistic retexturing. We show results for real garments demonstrating the accuracy of the recovered flexible shape.
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