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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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Jose Carlos Rubio, Joan Serrat and Antonio Lopez. 2012. Video Co-segmentation. 11th Asian Conference on Computer Vision. Springer Berlin Heidelberg, 13–24. (LNCS.)
Abstract: Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
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Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras and Josep Llados. 2013. Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. 2nd International ACM Workshop on Crowdsourcing for Multimedia.21–22.
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
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Monica Piñol, Angel Sappa, Angeles Lopez and Ricardo Toledo. 2012. Feature Selection Based on Reinforcement Learning for Object Recognition. Adaptive Learning Agents Workshop.33–39.
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David Aldavert, Ricardo Toledo, Arnau Ramisa and Ramon Lopez de Mantaras. 2009. Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing. 5th International Symposium on Visual Computing. Springer Berlin Heidelberg, 44–55.
Abstract: 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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Daniel Ponsa and Antonio Lopez. 2007. Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion. 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477.47–54.
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Aura Hernandez-Sabate, Lluis Albarracin, Daniel Calvo and Nuria Gorgorio. 2016. EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game. 5th International Conference Games and Learning Alliance.50–59. (LNCS.)
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Jose Manuel Alvarez, Theo Gevers and Antonio Lopez. 2010. 3D Scene Priors for Road Detection. 23rd IEEE Conference on Computer Vision and Pattern Recognition.57–64.
Abstract: Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.
Keywords: road detection
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Joan Serrat, Antonio Lopez and David Lloret. 2000. On ridges and valleys. 15 th International Conference on Pattern Recognition.59–66.
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Marçal Rusiñol, David Aldavert, Ricardo Toledo and Josep Llados. 2011. Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. 11th International Conference on Document Analysis and Recognition.63–67.
Abstract: In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts.
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