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Author |
Miguel Oliveira; Angel Sappa; V.Santos |
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Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
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Conference Article |
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Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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201-208 |
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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ADAS |
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Admin @ si @ OSS2011; ADAS @ adas @ |
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1766 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
3D Scene Priors for Road Detection |
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Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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57–64 |
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Keywords |
road detection |
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Abstract |
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS @ adas @ AGL2010a |
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1302 |
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Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
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Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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137–144 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
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San Francisco; CA; USA; June 2010 |
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English |
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English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS |
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Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
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Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Pages |
1046–1053 |
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We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS |
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no |
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Call Number |
Admin @ si @ ARL2010a |
Serial |
1311 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
Type |
Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
3066-3072 |
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This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS |
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ADAS @ adas @ RoS2010a |
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1303 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
Type |
Conference Article |
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Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Pages |
565–572 |
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Keywords |
road detection |
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Abstract |
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
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Miami (USA) |
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1063-6919 |
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978-1-4244-3992-8 |
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ADAS;ISE |
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Call Number |
ADAS @ adas @ AGL2009 |
Serial |
1169 |
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Author |
Mohammad Rouhani; E. Boyer; Angel Sappa |
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Title |
Non-Rigid Registration meets Surface Reconstruction |
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Conference Article |
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Year |
2014 |
Publication |
International Conference on 3D Vision |
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Pages |
617-624 |
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Abstract |
Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers. |
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Tokyo; Japan; December 2014 |
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3DV |
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ADAS; 600.055; 600.076 |
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no |
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Call Number |
Admin @ si @ RBS2014 |
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2534 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
MultiTable Reinforcement for Visual Object Recognition |
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Conference Article |
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Year |
2012 |
Publication |
4th International Conference on Signal and Image Processing |
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221 |
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469-480 |
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This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach. |
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Coimbatore, India |
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Springer India |
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LNCS |
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1876-1100 |
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978-81-322-0996-6 |
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ICSIP |
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ADAS |
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Admin @ si @ PST2012 |
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2157 |
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Author |
Naveen Onkarappa; Sujay M. Veerabhadrappa; Angel Sappa |
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Title |
Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture |
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Conference Article |
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2012 |
Publication |
4th International Conference on Signal and Image Processing |
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221 |
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257-267 |
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Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow. |
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Coimbatore, India |
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1876-1100 |
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978-81-322-0996-6 |
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ICSIP |
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ADAS |
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Admin @ si @ OVS2012 |
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2356 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
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Conference Article |
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2013 |
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20th International Conference On Neural Information Processing |
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8228 |
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III |
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368-375 |
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An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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Admin @ si @ DFD2013 |
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2439 |
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