Current Issue : January - March Volume : 2013 Issue Number : 1 Articles : 5 Articles
This article introduces a novel approach for finding a rigid transformation that coarsely aligns two 3D point clouds.\r\nThe algorithm performs an iterative comparison between 2D descriptors by using a purpose-designed similarity\r\nmeasure in order to find correspondences between two 3D point clouds sensed from different positions of a freeform\r\nobject. The descriptors (named with the acronym CIRCON) represent an ordered set of radial contours that\r\nare extracted around an interest-point within the point cloud. The search for correspondences is done iteratively,\r\nfollowing a cell distribution that allows the algorithm to converge toward a candidate point. Using a single\r\ncorrespondence an initial estimation of the Euclidean transformation is computed and later refined by means of a\r\nmultiresolution approach. This coarse alignment algorithm can be used for 3D modeling and object manipulation\r\ntasks such as ââ?¬Å?Bin Pickingââ?¬Â when free-form objects are partially occluded or present symmetries...
A feature extraction algorithm is introduced for face recognition, which efficiently exploits the local spatial variations in a face\r\nimage utilizing curvelet transform. Although multi-resolution ideas have been profusely employed for addressing face recognition\r\nproblems, theoretical studies indicate that digital curvelet transform is an even better method due to its directional properties.\r\nInstead of considering the entire face image, an entropy-based local band selection criterion is developed for feature extraction,\r\nwhich selects high-informative horizontal bands from the face image. These bands are segmented into several small spatial modules\r\nto capture the local spatial variations precisely. The effect of modularization in terms of the entropy content of the face images has\r\nbeen investigated. Dominant curvelet transform coefficients corresponding to each local region residing inside the horizontal\r\nbands are selected, based on the proposed threshold criterion, as features, which not only drastically reduces the feature dimension\r\nbut also provides high within-class compactness and high between-class separability. A principal component analysis is performed\r\nto further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases\r\nand a very high degree of recognition accuracy is achieved even with a simple Euclidean distance based classifie...
This paper investigates 3D positioning in an indoor line of sight (LOS) and nonline of sight (NLOS) combined environment.\r\nIt is a known fact that time-of-arrival-(TOA-) based positioning outperforms other techniques in LOS environments; however,\r\nmultipath in an indoor environment, especially NLOS multipath, significantly decreases the accuracy of TOA positioning. On\r\nthe other hand, received-signal-strength-(RSS-) based positioning is not affected so much by NLOS multipath as long as the\r\npropagation attenuation can be correctly estimated and the multipath effects have been compensated for. Based on this fact, a\r\nhybrid weighted least square (HWLS) RSS/TOA method is proposed for target positioning in an indoor LOS/NLOS environment.\r\nThe identification of LOS/NLOS path is implemented by using Nakagami distribution. An experiment is conducted in the iRadio\r\nlab, in the ICT building at the University of Calgary, in order to (i) demonstrate the availability of Nakagami distribution for the\r\nidentification of LOS and NLOS path, (ii) estimate the pass loss exponent for RSS technique, and (iii) verify our proposed scheme....
This article presents a novel method to obtain a sparse representation of multiview images. The method is based\r\non the fact that multiview data is composed of epipolar-plane image lines which are highly redundant. We extend\r\nthis principle to obtain the layer-based representation, which partitions a multiview image dataset into redundant\r\nregions (which we call layers) each related to a constant depth in the observed scene. The layers are extracted\r\nusing a general segmentation framework which takes into account the camera setup and occlusion constraints. To\r\nobtain a sparse representation, the extracted layers are further decomposed using a multidimensional discrete\r\nwavelet transform (DWT), first across the view domain followed by a two-dimensional (2D) DWT applied to the\r\nimage dimensions. We modify the viewpoint DWT to take into account occlusions and scene depth variations.\r\nSimulation results based on nonlinear approximation show that the sparsity of our representation is superior to the\r\nmulti-dimensional DWT without disparity compensation. In addition we demonstrate that the constant depth\r\nmodel of the representation can be used to synthesise novel viewpoints for immersive viewing applications and\r\nalso de-noise multiview images....
In communication systems, efficient use of the spectrum is an indispensable concern. Recently the use of\r\ncompressed sensing for the purpose of estimating orthogonal frequency division multiplexing (OFDM) sparse\r\nmultipath channels has been proposed to decrease the transmitted overhead in form of the pilot subcarriers which\r\nare essential for channel estimation. In this article, we investigate the problem of deterministic pilot allocation in\r\nOFDM systems. The method is based on minimizing the coherence of the submatrix of the unitary discrete fourier\r\ntransform (DFT) matrix associated with the pilot subcarriers. Unlike the usual case of equidistant pilot subcarriers,\r\nwe show that non-uniform patterns based on cyclic difference sets are optimal. In cases where there are no\r\ndifference sets, we perform a greedy method for finding a suboptimal solution. We also investigate the\r\nperformance of the recovery methods such as orthogonal matching pursuit (OMP) and iterative method with\r\nadaptive thresholding (IMAT) for estimation of the channel taps....
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