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Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio Lopez and Andrew Bagdanov. 2018. Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting. 24th International Conference on Pattern Recognition.2262–2268.
Abstract: In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
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Antonio Lopez, Joan Serrat, Cristina Cañero and Felipe Lumbreras. 2007. Robust Lane Lines Detection and Quantitative Assessment. In J. Marti et al, ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.274–281. (LNCS.)
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Jose Manuel Alvarez, Theo Gevers, Y. LeCun and Antonio Lopez. 2012. Road Scene Segmentation from a Single Image. 12th European Conference on Computer Vision. Springer Berlin Heidelberg, 376–389. (LNCS.)
Abstract: Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined
Keywords: road detection
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Angel Sappa, Rosa Herrero, Fadi Dornaika, David Geronimo and Antonio Lopez. 2007. Road Approximation in Euclidean and v-Disparity Space: A Comparative Study. Computer Aided Systems Theory,.1105–1112. (LNCS.)
Abstract: This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge.
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Angel Sappa, Rosa Herrero, Fadi Dornaika, David Geronimo and Antonio Lopez. 2007. Road Approximation in Euclidean and v-Disparity Space: A Comparative Study. EUROCAST2007, Workshop on Cybercars and Intelligent Vehicles.368–369.
Abstract: This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge.
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Cristhian Aguilera, Xavier Soria, Angel Sappa and Ricardo Toledo. 2017. RGBN Multispectral Images: a Novel Color Restoration Approach. 15th International Conference on Practical Applications of Agents and Multi-Agent System.
Abstract: 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.
Keywords: Multispectral Imaging; Free Sensor Model; Neural Network
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Gioacchino Vino and Angel Sappa. 2013. Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. 10th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 354–363. (LNCS.)
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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David Lloret, Joan Serrat, Antonio Lopez, A. Soler and Juan J. Villanueva. 2000. Retinal image registration using creases as anatomical landmarks. 15 th International Conference on Pattern Recognition.207–2010.
Abstract: Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
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Mohammad Rouhani and Angel Sappa. 2010. Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting. 23rd IEEE Conference on Computer Vision and Pattern Recognition.3066–3072.
Abstract: 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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Angel Sappa, Fadi Dornaika, David Geronimo and Antonio Lopez. 2008. Registration-based Moving Object Detection from a Moving Camera. IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles.65–69.
Abstract: This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach.
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