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Jose Manuel Alvarez, Ferran Diego, Joan Serrat, & Antonio Lopez. (2009). Automatic Ground-truthing using video registration for on-board detection algorithms. In 16th IEEE International Conference on Image Processing (pp. 4389–4392).
Abstract: Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate.
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Jose Manuel Alvarez, Theo Gevers, & Antonio Lopez. (2009). Learning Photometric Invariance from Diversified Color Model Ensembles. In 22nd IEEE Conference on Computer Vision and Pattern Recognition (565–572).
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.
Keywords: road detection
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Jose Manuel Alvarez, & Antonio Lopez. (2009). Model-based road detection using shadowless features and on-line learning.
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Niki Aifanti, Angel Sappa, N. Grammalidis, & Sotiris Malassiotis. (2009). Advances in Tracking and Recognition of Human Motion. In Encyclopedia of Information Science and Technology (Vol. I, 65–71).
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Xavier Boix, Josep M. Gonfaus, Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, Marco Pedersoli, et al. (2009). Combining local and global bag-of-word representations for semantic segmentation. In Workshop on The PASCAL Visual Object Classes Challenge.
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Fadi Dornaika, & Angel Sappa. (2009). Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression. PRL - Pattern Recognition Letters, 30(5), 535–543.
Abstract: This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.
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Fadi Dornaika, & Angel Sappa. (2009). A Featureless and Stochastic Approach to On-board Stereo Vision System Pose. IMAVIS - Image and Vision Computing, 27(9), 1382–1393.
Abstract: This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.
Keywords: On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping
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Ferran Diego, Daniel Ponsa, Joan Serrat, & Antonio Lopez. (2009). Video alignment for automotive applications.
Keywords: video alignment
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2009). An iterative multiresolution scheme for SFM with missing data. JMIV - Journal of Mathematical Imaging and Vision, 34(3), 240–258.
Abstract: Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2009). Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach. International Journal of Electronic Commerce, 14(1), 89–108.
Abstract: The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.
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Daniel Ponsa, & Antonio Lopez. (2009). Variance reduction techniques in particle-based visual contour Tracking. PR - Pattern Recognition, 42(11), 2372–2391.
Abstract: This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Keywords: Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
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Daniel Ponsa, & Antonio Lopez. (2009). Seguimiento Visual de Contornos Computerizado.
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Mohammad Rouhani, & Angel Sappa. (2009). A Novel Approach to Geometric Fitting of Implicit Quadrics. In 8th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 5807, 121–132). LNCS. Springer Berlin Heidelberg.
Abstract: 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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Angel Sappa, Niki Aifanti, Sotiris Malassiotis, & Michael G. Strintzis. (2009). Prior Knowledge Based Motion Model Representation. In Horst Bunke, JuanJose Villanueva, & Gemma Sanchez (Eds.), Progress in Computer Vision and Image Analysis (Vol. 16).
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Angel Sappa, & Mohammad Rouhani. (2009). Efficient Distance Estimation for Fitting Implicit Quadric Surfaces. In 16th IEEE International Conference on Image Processing (3521–3524).
Abstract: This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach.
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