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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple-target tracking for the intelligent headlights control |
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Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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903–910 |
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Intelligent Headlights |
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Abstract |
TA7.4
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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Madeira Island (Portugal) |
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ITSC |
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ADAS |
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ADAS @ adas @ RSL2010 |
Serial |
1422 |
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Author |
Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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Title |
Nightime Vehicle Detecion for Intelligent Headlight Control |
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Conference Article |
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Year |
2008 |
Publication |
Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, |
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5259 |
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113–124 |
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Keywords |
Intelligent Headlights; vehicle detection |
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Juan-les-Pins, France |
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ACIVS |
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ADAS;CIC |
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ADAS @ adas @ LHB2008a |
Serial |
1098 |
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Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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lane markings |
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Girona (Spain) |
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J. Marti et al |
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IbPRIA |
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ADAS |
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ADAS @ adas @ LSC2007 |
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881 |
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Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Visible-Thermal Fusion based Monocular Visual Odometry |
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Conference Article |
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Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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517-528 |
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Keywords |
Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
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Abstract |
The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Lisboa; Portugal; November 2015 |
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Springer International Publishing |
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2194-5357 |
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978-3-319-27145-3 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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Admin @ si @ PAD2015 |
Serial |
2663 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
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Conference Article |
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2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Keywords |
Multiple Instance Learning; Active Learning; Object-class segmentation. |
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Abstract |
In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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Hong-Kong |
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ICMV |
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ADAS |
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ADAS @ adas @ AGL2010b |
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1429 |
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Author |
Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent System |
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Multispectral Imaging; Free Sensor Model; Neural Network |
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This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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Porto; Portugal; June 2017 |
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PAAMS |
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ADAS; MSIAU; 600.118; 600.122 |
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Admin @ si @ ASS2017 |
Serial |
2918 |
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Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
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Conference Article |
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2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
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Keywords |
multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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Admin @ si @ CAV2015 |
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2629 |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Real-time Object Segmentation using a Bag of Features Approach |
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Conference Article |
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2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
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220 |
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321–329 |
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Keywords |
Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors |
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In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. |
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IOS Press Amsterdam, |
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In R.Alquezar, A.Moreno, J.Aguilar. |
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CCIA |
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ADAS |
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no |
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Admin @ si @ ARL2010b |
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1417 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann |
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Title |
When Is A Confidence Measure Good Enough? |
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Conference Article |
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2013 |
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9th International Conference on Computer Vision Systems |
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7963 |
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344-353 |
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Keywords |
Optical flow, confidence measure, performance evaluation |
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Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality. |
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St Petersburg; Russia; July 2013 |
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Springer Link |
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0302-9743 |
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978-3-642-39401-0 |
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ICVS |
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IAM;ADAS; 600.044; 600.057; 600.060; 601.145 |
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IAM @ iam @ MGH2013a |
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2218 |
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Patricia Marquez;Debora Gil;Aura Hernandez-Sabate |
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Title |
A Complete Confidence Framework for Optical Flow |
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2012 |
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12th European Conference on Computer Vision – Workshops and Demonstrations |
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7584 |
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2 |
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124-133 |
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Optical flow, confidence measures, sparsification plots, error prediction plots |
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Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations. |
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Springer-Verlag |
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Florence, Italy, October 7-13, 2012 |
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Andrea Fusiello, Vittorio Murino ,Rita Cucchiara |
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LNCS |
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978-3-642-33867-0 |
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ECCVW |
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IAM;ADAS; |
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IAM @ iam @ MGH2012b |
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1991 |
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