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Arnau Ramisa, Ramon Lopez de Mantaras, & Ricardo Toledo. (2007). Comparing Combinations of Feature Regions for Panoramic VSLAM. In 4th International Conference on Informatics in Control, Automation and Robotics (292–297).
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Jialuo Chen, Mohamed Ali Souibgui, Alicia Fornes, & Beata Megyesi. (2021). Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images. In 4th International Conference on Historical Cryptology (pp. 34–37).
Abstract: Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering.
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Ana Maria Ares, Jorge Bernal, Maria Jesus Nozal, F. Javier Sanchez, & Jose Bernal. (2018). Results of the use of Kahoot! gamification tool in a course of Chemistry. In 4th International Conference on Higher Education Advances (pp. 1215–1222).
Abstract: The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
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F. Javier Sanchez, & Jorge Bernal. (2018). Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject. In 4th International Conference of Higher Education Advances (pp. 1359–1366).
Abstract: The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject.
Keywords: Monitoring; Evaluation tool; Gamification; Student motivation
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Carlo Gatta, Juan Diego Gomez, Francesco Ciompi, O. Rodriguez-Leor, & Petia Radeva. (2009). Toward robust myocardial blush grade estimation in contrast angiography. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 249–256). LNCS. Springer Berlin Heidelberg.
Abstract: The assessment of Myocardial Blush Grade after primary angioplasty is a precious diagnostic tool to understand if the patient needs further medication or the use of specifics drugs. Unfortunately, the assessment of MBG is difficult for non highly specialized staff. Experimental data show that there is poor correlation between MBG assessment of low and high specialized staff, thus reducing its applicability. This paper proposes a method able to achieve an objective measure of MBG, or a set of parameters that correlates with the MBG. The method tracks the blush area starting from just one single frame tagged by the physician. As a consequence, the blush area is kept isolated from contaminating phenomena such as diaphragm and arteries movements. We also present a method to extract four parameters that are expected to correlate with the MBG. Preliminary results show that the method is capable of extracting interesting information regarding the behavior of the myocardial perfusion.
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Francesco Ciompi, Oriol Pujol, O. Rodriguez-Leor, Carlo Gatta, Angel Serrano, & Petia Radeva. (2009). Enhancing In-Vitro IVUS Data for Tissue Characterization. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 241–248). LNCS. Springer Berlin Heidelberg.
Abstract: Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%.
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Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2009). Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 346–353). LNCS. Springer Berlin Heidelberg.
Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
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Albert Gordo, & Ernest Valveny. (2009). The diagonal split: A pre-segmentation step for page layout analysis & classification. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 290–297). LNCS. Springer Berlin Heidelberg.
Abstract: Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives.
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2009). Face-to-face social activity detection using data collected with a wearable device. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 56–63). LNCS. Springer Berlin Heidelberg.
Abstract: In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level
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Marco Pedersoli, Jordi Gonzalez, & Juan J. Villanueva. (2009). High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one.
Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated.
Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration.
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Bhaskar Chakraborty, Andrew Bagdanov, & Jordi Gonzalez. (2009). Towards Real-Time Human Action Recognition. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524). LNCS. Springer Berlin Heidelberg.
Abstract: This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art.
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Murad Al Haj, Andrew Bagdanov, Jordi Gonzalez, & Xavier Roca. (2009). Robust and Efficient Multipose Face Detection Using Skin Color Segmentation. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we describe an efficient technique for detecting faces in arbitrary images and video sequences. The approach is based on segmentation of images or video frames into skin-colored blobs using a pixel-based heuristic. Scale and translation invariant features are then computed from these segmented blobs which are used to perform statistical discrimination between face and non-face classes. We train and evaluate our method on a standard, publicly available database of face images and analyze its performance over a range of statistical pattern classifiers. The generalization of our approach is illustrated by testing on an independent sequence of frames containing many faces and non-faces. These experiments indicate that our proposed approach obtains false positive rates comparable to more complex, state-of-the-art techniques, and that it generalizes better to new data. Furthermore, the use of skin blobs and invariant features requires fewer training samples since significantly fewer non-face candidate regions must be considered when compared to AdaBoost-based approaches.
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Eduard Vazquez, & Ramon Baldrich. (2008). Colour Image Segmentation in Presence of Shadows. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (383–387).
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Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2008). Edge Classification for Color Constancy. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (231–234).
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Javier Vazquez, Maria Vanrell, & Ramon Baldrich. (2008). Towards a Psychophysical Evaluation of Colour Constancy Algorithms. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (372–377).
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