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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2006. An Iterative Multiresolution Scheme for SFM. International Conference on Image Analysis and Recognition.804–815.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2006. Factorization with Missing and Noisy Data. 6th International Conference on Computational Science.555–562.
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Miguel Oliveira, Victor Santos, Angel Sappa and P. Dias. 2015. Scene Representations for Autonomous Driving: an approach based on polygonal primitives. 2nd Iberian Robotics Conference ROBOT2015.503–515.
Abstract: In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Keywords: Scene reconstruction; Point cloud; Autonomous vehicles
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J.Poujol, Cristhian A. Aguilera-Carrasco, E.Danos, Boris X. Vintimilla, Ricardo Toledo and Angel Sappa. 2015. Visible-Thermal Fusion based Monocular Visual Odometry. 2nd Iberian Robotics Conference ROBOT2015. Springer International Publishing, 517–528.
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Keywords: Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
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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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David Aldavert, Arnau Ramisa, Ramon Lopez de Mantaras and Ricardo Toledo. 2010. Real-time Object Segmentation using a Bag of Features Approach. In In R.Alquezar, A.M., J.Aguilar., ed. 13th International Conference of the Catalan Association for Artificial Intelligence. IOS Press Amsterdam,, 321–329.
Abstract: In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.
Keywords: Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors
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Arnau Ramisa, Shrihari Vasudevan, David Aldavert, Ricardo Toledo and Ramon Lopez de Mantaras. 2009. Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications. 12th International Conference of the Catalan Association for Artificial Intelligence.9–18.
Abstract: General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.
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Alex Goldhoorn, Arnau Ramisa, Ramon Lopez de Mantaras and Ricardo Toledo. 2007. Using the Average Landmark Vector Method for Robot Homing. Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA.331–338.
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Daniel Hernandez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure and Antonio Lopez. 2016. Embedded real-time stereo estimation via Semi-Global Matching on the GPU. 16th International Conference on Computational Science.143–153.
Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Keywords: Autonomous Driving; Stereo; CUDA; 3d reconstruction
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