Many automatic target recognition, detection, and identification problems usually suffer from lack of adequate resolution of the imagedata, especially among infrared imaging systems. A number of superresolution recon...
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ISBN:
(纸本)0819440809
Many automatic target recognition, detection, and identification problems usually suffer from lack of adequate resolution of the imagedata, especially among infrared imaging systems. A number of superresolution reconstruction algorithms have been proposed. The challenge is how to recapture additional high-frequency information from adjacent frames in an image sequence that contains slightly different, but unique, information. In addition, real-world infrared sequence images are noisy with low contrast, and low spatial resolution. Since broad-banded noise mainly affects high-frequency information to be recaptured, the challenge is how to avoid smoothing out the high-frequency data by the regularization are not smoothed out. This paper presents a new superresolution reconstruction approach based on wavelet domain for superresolution imagereconstruction of infra-red (IR) sequences. Minimizing the regularization cost function in wavelet domain forms a multi-scale high-resolution estimate. The effects of noise are incorporated into the iterative process in the proposed method. The estimation errors in high- and low-frequency bands are processed separately to solve the problem of variable correlations of observed images and slow convergence. The proposed approach was tested on the infrared aerial image sequences provided by Defence Research Establishment in valcartier. Experiment results show that a significant increase in the spatial resolution can be achieved by the proposed approach while the noise is smoothed out.
The paper presents a novel algorithm for object space reconstructionfrom the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is ...
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ISBN:
(纸本)0780370414
The paper presents a novel algorithm for object space reconstructionfrom the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by using a hierarchical multiresolution strategy and imposing additional conditions to the sought solution. The hierarchisation strategy and the two-phase adaptive constrained 3D-reconstruction algorithm based on the use of two sigmoid functions are presented. Finally, illustrative simulation results are given.
Under ideal circumstances the problem of tomographic reconstruction is well-posed, and measured data are sufficient to obtain accurate estimates of volume densities. In such cases segmentation and surface estimation f...
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The total variation (Tv) norm has been described in literature as a method for reducing noise in two-dimensional (2D) images. At the same time. the Tv-norm is very good at recovering edges in images, without introduci...
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The total variation (Tv) norm has been described in literature as a method for reducing noise in two-dimensional (2D) images. At the same time. the Tv-norm is very good at recovering edges in images, without introducing ringing or edge artefacts. It has also been proposed as a 2D regularisation function in Bayesian reconstruction, implemented in an expectation maximisation (EM) algorithm, and called Tv-EM. The Tv-EM was developed for 2D SPECT imaging, and the algorithm is capable of smoothing noise while maintaining edges without introducing artefacts. We have extended the Tv-norm to take into account the third spatial dimension, and developed an iterative EM algorithm based on the three-dimensional (3D) Tv-norm, which we call Tv3D-EM. This takes into account the correlation between transaxial sections in SPECT, due to system resolution. We have compared the 2D and 3D algorithms using reconstructed images from simulated projection data. Phantoms used were a homogeneous sphere, and a 3D head phantom based on the Shepp-Logan phantom. The Tv3D-EM algorithm yielded somewhat lower noise levels than Tv-EM. The noise in the Tv3D-EM had similar correlation in transaxial and longitudinal sections, which was not the case for Tv-EM, or any 2D reconstruction method. In particular, longitudinal sections from Tv3D-EM were perceived as less noisy when compared to Tv-EM. The use of 3D reconstruction should also be advantageous if compensation for distant dependent collimator blurring is incorporated in the iterative algorithm. (C) 2001 Elsevier Science B.v. All rights reserved.
作者:
Ferrari, SBorghese, NAPiuri, vPolitecn Milan
Dept Elect & Informat I-20133 Milan Italy CNR
San Raffaele Sci Inst Ist Neurosci & Bioimmagini MAVRLab Human Mot Anal & Virtual RealLITA Segrate MI Italy Univ Milan
Dept Informat Technol Crema CR Italy
Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale imagereconstructionfrom range data. These are based on local operation on the data and are able to...
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Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale imagereconstructionfrom range data. These are based on local operation on the data and are able to give a sparse approximation. In this paper, HRBFs are reframed for the regular sampling case, and they are compared with wavelet decomposition. Results show that HRBFs, thanks to their constructive approach to approximation, are much more tolerant on errors in the parameters when errors occur in the configuration phase.
作者:
Ferrari, SBorghese, NAPiuri, vPolitecn Milan
Dept Elect & Informat I-20133 Milan Italy CNR
San Raffaele Sci Inst Ist Neurosci & Bioimmagini MAVRLab Human Mot Anal & Virtual RealLITA Segrate MI Italy Univ Milan
Dept Informat Technol Crema CR Italy
Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale imagereconstructionfrom range data. These are based on local operation on the data and are able to...
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Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale imagereconstructionfrom range data. These are based on local operation on the data and are able to give a sparse approximation. In this paper, HRBFs are reframed for the regular sampling case, and they are compared with wavelet decomposition. Results show that HRBFs, thanks to their constructive approach to approximation, are much more tolerant on errors in the parameters when errors occur in the configuration phase.
In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper, we start a...
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In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper, we start a performance analysis of stereo algorithms with respect to the task of immersive visualization. As opposed to usual monocular image based rendering, we are also interested in the depth error in novel views because our rendering is stereoscopic. We describe an evaluation test-bed which provides a world-wide first available set of registered dense "ground-truth" laser data and imagedatafrom multiple views. We establish metrics for novel depth views that reflect discrepancies both in the image and in 3D-space. It is well known that stereo performance is affected by both erroneous matching as well as incorrect depth triangulation. We experimentally study the effects of occlusion and low texture on the distributions of the error metrics. Then, we algebraically predict the behavior of depth and novel projection error as a function of the camera set-up and the error in the disparity. These are first steps towards building a laboratory for psychophysical judgement of depth estimates which is the ultimate performance test of tele-presence stereo.
Automatic Target Recognition (ATR) is difficult in general., but especially with RADAR. However, the problem can be greatly simplified by using the 3-D reconstruction techniques presented at SPIE1,2 the previous 2 yea...
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ISBN:
(纸本)0819440779
Automatic Target Recognition (ATR) is difficult in general., but especially with RADAR. However, the problem can be greatly simplified by using the 3-D reconstruction techniques presented at SPIE1,2 the previous 2 years. Now, instead of matching seemingly random signals in I-D or 2-D, one must match scattering centers in 3-D. This method tracks scattering centers through an image collection sequence that would typically be used for SAR image formation. A major difference is that this approach naturally allows object motion (in fact the more the object moves, the better) and the resulting 'image' is a 3-D set of scattering centers! This paper discusses a preliminary study into extracting the scattering centers directly from synthetic data to build a database in anticipation of comparing the relative separability of these reconstructed scattering centers against more traditional approaches for doing ATR.
A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to lik...
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A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA's fundamental components area a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which is our case are examples of hand joint configurations and their corresponding visual features. The joint angle data in the training set is obtained via a CyberGlove, a glove with 22 sensors that monitor the angular motions of the palm and fingers. In training, the visual features are generated using a computer graphics module that renders the hand from arbitrary viewpoints given the 22 joint angles. The viewpoint is encoded by two real values, therefore 24 real values represent a hand pose. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both hands of the user, calculates the appropriate features, and estimates the 3D hand joint angles and viewpoint from those features. Results are encouraging given the complexity of the task.
An iterative algorithm that reconstructs the attenuation map and emission activity distribution from SPECT emission data is proposed. The algorithm is based on the quasilinearized attenuated Radon transform. At each i...
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An iterative algorithm that reconstructs the attenuation map and emission activity distribution from SPECT emission data is proposed. The algorithm is based on the quasilinearized attenuated Radon transform. At each iteration of the algorithm, emission activity distribution is reconstructed using the attenuation map from the previous iteration. Then, the attenuation map is estimated by using the current emission image. To effectively locate the attenuation map within a possible class of solutions the attenuation map is constrained by an optimal basis set, which is derived from a cross-correlation of a priori images known as a "knowledge set." Computer simulations show that the proposed algorithm has the capability to reconstruct the emission activity distribution and the transmission map without the use of transmission measurements. The proposed method has been tested using clinically acquired data.
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