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Mohammad Rouhani and Angel Sappa. 2010. A Fast accurate Implicit Polynomial Fitting Approach. 17th IEEE International Conference on Image Processing.1429–1432.
Abstract: This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons.
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Mohammad Rouhani and Angel Sappa. 2011. Correspondence Free Registration through a Point-to-Model Distance Minimization. 13th IEEE International Conference on Computer Vision.2150–2157.
Abstract: This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework.
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Javier Marin, David Vazquez, Antonio Lopez, Jaume Amores and Bastian Leibe. 2013. Random Forests of Local Experts for Pedestrian Detection. 15th IEEE International Conference on Computer Vision. IEEE, 2592–2599.
Abstract: Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.
Keywords: ADAS; Random Forest; Pedestrian Detection
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Gemma Roig, Xavier Boix, R. de Nijs, Sebastian Ramos, K. Kühnlenz and Luc Van Gool. 2013. Active MAP Inference in CRFs for Efficient Semantic Segmentation. 15th IEEE International Conference on Computer Vision.2312–2319.
Abstract: Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
Keywords: Semantic Segmentation
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Naveen Onkarappa, Sujay M. Veerabhadrappa and Angel Sappa. 2012. Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture. 4th International Conference on Signal and Image Processing.257–267.
Abstract: 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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Monica Piñol, Angel Sappa and Ricardo Toledo. 2012. MultiTable Reinforcement for Visual Object Recognition. 4th International Conference on Signal and Image Processing. Springer India, 469–480. (LNCS.)
Abstract: 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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Diego Cheda, Daniel Ponsa and Antonio Lopez. 2012. Pedestrian Candidates Generation using Monocular Cues. IEEE Intelligent Vehicles Symposium. IEEE Xplore, 7–12.
Abstract: Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.
Keywords: pedestrian detection
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Naveen Onkarappa and Angel Sappa. 2012. An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed. IEEE Intelligent Vehicles Symposium. IEEE Xplore, 1138–1143.
Abstract: Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow.
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Miguel Oliveira, Angel Sappa and V. Santos. 2012. Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models. IEEE Intelligent Vehicles Symposium. IEEE Xplore, 299–303.
Abstract: The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
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Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin and Daniel Ponsa. 2013. Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection. IEEE Intelligent Vehicles Symposium. IEEE, 467–472.
Abstract: State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster).
Keywords: Pedestrian Detection; Virtual World; Part based
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