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Author |
Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo |
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Title |
Multispectral Image Feature Points |
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2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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12 |
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9 |
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12661-12672 |
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Keywords |
multispectral image descriptor; color and infrared images; feature point descriptor |
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Abstract |
Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art. |
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ADAS |
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no |
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Admin @ si @ ABL2012 |
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2154 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation |
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2012 |
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IEEE Journal of Selected Topics in Signal Processing |
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J-STSP |
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6 |
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5 |
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437-446 |
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This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain. |
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1932-4553 |
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Admin @ si @ BLS2012b |
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2155 |
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Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple target tracking for intelligent headlights control |
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2012 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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13 |
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2 |
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594-605 |
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Intelligent Headlights |
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Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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1524-9050 |
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ADAS |
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Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g |
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1877 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Implicit Polynomial Representation through a Fast Fitting Error Estimation |
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Journal Article |
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2012 |
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IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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21 |
Issue |
4 |
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2089-2098 |
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Impact Factor
This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach. |
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1057-7149 |
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ADAS |
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Admin @ si @ RoS2012b; ADAS @ adas @ |
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1937 |
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Author |
Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester |
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Title |
Multilocal Creaseness Measure |
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2012 |
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The Insight Journal |
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IJ |
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Ridges, Valley, Creaseness, Structure Tensor, Skeleton, |
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This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission. |
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Alma IT Systems |
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english |
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english |
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IAM;ADAS; |
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IAM @ iam @ VGL2012 |
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1840 |
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