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Author |
Idoia Ruiz; Joan Serrat |
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Title |
Rank-based ordinal classification |
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Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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8069-8076 |
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Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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Virtual; January 2021 |
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ADAS; 600.118; 600.124 |
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Admin @ si @ RuS2020 |
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3549 |
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Author |
Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
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Title |
Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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Conference Article |
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Year |
2023 |
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International Conference on Intelligent Robots and Systems |
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On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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IROS |
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ADAS |
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Admin @ si @ XCP2023 |
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3930 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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Pages |
363-370 |
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Keywords |
Pedestrian Detection; Color; Part Based Models |
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Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
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Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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English |
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English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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ADAS |
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no |
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ADAS @ adas @ RVL2011a |
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1666 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
A Novel Approach to Geometric Fitting of Implicit Quadrics |
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Conference Article |
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Year |
2009 |
Publication |
8th International Conference on Advanced Concepts for Intelligent Vision Systems |
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5807 |
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121–132 |
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This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. |
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Bordeaux, France |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04696-4 |
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ACIVS |
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ADAS |
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ADAS @ adas @ RoS2009 |
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1194 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing |
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Conference Article |
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Year |
2009 |
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5th International Symposium on Visual Computing |
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5875 |
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44–55 |
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In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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ADAS |
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Admin @ si @ ATR2009a |
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1246 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Visual Registration Method For A Low Cost Robot: Computer Vision Systems |
Type |
Conference Article |
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Year |
2009 |
Publication |
7th International Conference on Computer Vision Systems |
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5815 |
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204–214 |
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An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. |
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Belgica |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04666-7 |
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ICVS |
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ADAS |
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Admin @ si @ ATR2009b |
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1247 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
Type |
Conference Article |
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Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
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6111 |
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230-239 |
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This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Povoa de Varzim (Portugal) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13771-6 |
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ICIAR |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ OnS2010 |
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1342 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
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Volume |
6855 |
Issue |
II |
Pages |
463-470 |
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Keywords |
Pedestrian Detection; Color |
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Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. |
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Seville, Spain |
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Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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Language |
English |
Summary Language |
english |
Original Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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0302-9743 |
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978-3-642-23677-8 |
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CAIP |
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ADAS |
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no |
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ADAS @ adas @ RVL2011b |
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1665 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
Space Variant Representations for Mobile Platform Vision Applications |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
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6855 |
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II |
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146-154 |
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The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow. |
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Seville, Spain |
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Springer Berlin Heidelberg |
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P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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0302-9743 |
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978-3-642-23677-8 |
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CAIP |
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ADAS |
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no |
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NaS2011; ADAS @ adas @ |
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1686 |
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Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
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Title |
Improving HOG with Image Segmentation: Application to Human Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
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Volume |
7517 |
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178-189 |
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Segmentation; Pedestrian Detection |
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In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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Brno, Czech Republic |
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Springer Berlin Heidelberg |
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J. Blanc-Talon et al. |
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English |
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0302-9743 |
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978-3-642-33139-8 |
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ADAS;ISE |
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ADAS @ adas @ SLV2012 |
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1980 |
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