A vision based method to recover human faces from video sequences is presented. Although video sequences acquired from multiple static synchronized CCD cameras have been used as a tool for 3-D reconstruction of the fa...
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ISBN:
(纸本)0769525210
A vision based method to recover human faces from video sequences is presented. Although video sequences acquired from multiple static synchronized CCD cameras have been used as a tool for 3-D reconstruction of the face before, the precision and reliability remain as concerning issues, which are addressed in this paper Moreover, the presented matching algorithm is invariant to scaling, rotation and insufficient calibration. A geometric primitive guides the matching approach through object space and therefore allows to compare gray values in an unique coordinate system
The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended expectation m...
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The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended expectation maximization (EM) algorithm. This algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge framework. Determining the fitting quality of the gained patches, the approach then allows for segmentation of planar surfaces out of the 3D environment. The result is a set of 2D objects, which can be used as input for classical computer vision applications, in particular for object recognition. Our approach makes it possible to apply classical tools of 2D imageprocessing to solve problems of 3D robot mapping, e.g. landmark recognition
In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification probl...
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In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.
Due to the wide existence of mixed pixels, the derivation of constituent components (endmembers) and their proportions (abundances) at subpixel scales has become an important research topic. In this paper, we propose ...
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ISBN:
(纸本)0769525210
Due to the wide existence of mixed pixels, the derivation of constituent components (endmembers) and their proportions (abundances) at subpixel scales has become an important research topic. In this paper, we propose a novel unsupervised decomposition method based on the classical maximum entropy principle, termed uMaxEnt. The algorithm integrates a global least square error-based endmember detection and a per-pixel maximum entropy learning to find the most possible proportions. We apply the proposed method to the subject of spectral unmixing. The experimental results obtained from both simulated and real hyper-spectral data demonstrate the effectiveness of the uMaxEnt method
Knowing the locations of nodes in wireless sensor networks (WSN) is essential for many applications. Nodes in a WSN can have multiple capabilities and exploiting one or more of the capabilities can help to solve the l...
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Knowing the locations of nodes in wireless sensor networks (WSN) is essential for many applications. Nodes in a WSN can have multiple capabilities and exploiting one or more of the capabilities can help to solve the localization problem. In this paper, we assume that each node in a WSN has the capability of distance measurement and present a location computation technique called linear intersection for node localization. We also propose an applied localization model using linear intersection and do some concerned experiments to estimate the location computation algorithm.
When computer vision technique is used in robotics, robotic hand-eye calibration is a very important research task. Many algorithms have been proposed for hand-eye calibration. Based on these algorithms, we introduce ...
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When computer vision technique is used in robotics, robotic hand-eye calibration is a very important research task. Many algorithms have been proposed for hand-eye calibration. Based on these algorithms, we introduce a new hand-eye calibration algorithm in this paper, which employs the screw motion theory to establish a hand-eye matrix equation by using quaternion and gets a simultaneous result for rotation and translation by solving linear equations. The algorithm proposed in this paper has high and stable computational efficiency without non-linear minimization and can be understood easily. Both simulations and real experiments show the superiority of our algorithm over the comparative algorithms
Some peculiarities of modified vector sigma filter are studied. In particular, its edge preservation ability is considered in case of processing multichannel remotesensing (RS) images. Such a problem is of high impor...
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Some peculiarities of modified vector sigma filter are studied. In particular, its edge preservation ability is considered in case of processing multichannel remotesensing (RS) images. Such a problem is of high importance for many scene recognition and segmentation tasks. It is demonstrated through comparative quantitative and visual processing data that the proposed filter simultaneously provides efficient noise suppression and excellent edge preservation. Edge detection results that prove this fact are also depicted
In image database retrieval there are many classical similarity measures that can be used to find the target image, these measures are mostly belong to geometry model from the point of view of the data model, while li...
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In image database retrieval there are many classical similarity measures that can be used to find the target image, these measures are mostly belong to geometry model from the point of view of the data model, while little attention has been devoted to the studies on methods based on probability density distribution. In this paper we experimental investigate some probabilistic similarity measures, present two methods for design of the similarity function of two mixture Gaussian distributions, on the basis of the nearest neighbor rule and K nearest neighbor rule respectively. An experimental study was conducted to examine and evaluate the measures for application to image databases, and the experiment results show that the methods based on K nearest neighbor rule achieve better performance.
Hyperspectral imaging is a new technique which has become increasingly important in many remotesensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard pr...
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Hyperspectral imaging is a new technique which has become increasingly important in many remotesensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, etc. Hyperspectral imaging applications generate massive volumes of data and require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of workstations represent a very promising cost-effective solution expected to play a major role in the design of highperformance computing platforms for many on-going and planned remotesensing missions. This paper explores innovative techniques for mapping hyperspectral analysis algorithms onto heterogeneous networks of workstations available at NASA’s Goddard Space Flight Center and University of Maryland. Experimental results reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in hyperspectral imaging studies.
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