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David Masip, & Jordi Vitria. (2005). Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition. International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657).
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A. Pujol, & Juan J. Villanueva. (2002). A supervised Modification of the Hausdorff distance for visual shape classification. International Journal of Pattern Recognition and Artificial Intelligence, 349–359.
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Josep Llados, & Gemma Sanchez. (2004). Graph Matching vs. Graph Parsing in Graphics Recognition: A Combined Approach. IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, 455–473.
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Bogdan Raducanu, & Jordi Vitria. (2008). Face Recognition by Artificial Vision Systems: A Cognitive Perspective. IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, 899–913.
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Lluis Barcelo, & X. Binefa. (2002). Bayesian Video Mosaicing with moving objects. International Journal of Pattern Recognition and Artificial Intelligence, 16(3): 341–348 (IF: 0.359).
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M. Gonzalez-Audicana, Xavier Otazu, O. Fors, & A. Seco. (2005). Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925).
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Aura Hernandez-Sabate, Meritxell Joanpere, Nuria Gorgorio, & Lluis Albarracin. (2015). Mathematics learning opportunities when playing a Tower Defense Game. IJSG - International Journal of Serious Games, 57–71.
Abstract: A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
Keywords: Tower Defense game; learning opportunities; mathematics; problem solving; game design
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Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, & Xavier Roca. (2019). Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits. IJSSH - International Journal of Social Science and Humanity, 6–12.
Abstract: Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones.
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Ernest Valveny, & Philippe Dosch. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition.
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Josep Llados, & Dorothea Blostein. (2007). Special Issue on Graphics Recognition. IJDAR - International Journal on Document Analysis and Recognition, 1–2.
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Ernest Valveny, Philippe Dosch, Adam Winstanley, Yu Zhou, Su Yang, Luo Yan, et al. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74.
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Ernest Valveny, & Philippe Dosch. (2007). A General Framework for the Evaluation of Symbol Recognition Methods. International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74.
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Jaume Amores, & Petia Radeva. (2005). Retrieval of IVUS Images Using Contextual Information and Elastic Matching. International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657).
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Petia Radeva, & Jordi Vitria. (2004). Corkinspect: Statistical Learning of Natural Material. Italian Beverage Technology, 13(38):11–18.
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Lasse Martensson, Ekta Vats, Anders Hast, & Alicia Fornes. (2019). In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora. HUMAN IT - Journal for Information Technology Studies as a Human Science, 95–120.
Abstract: In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples.
Keywords: Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts
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