|
|
A. Martinez, & Jordi Vitria. (1996). Designing and Implementing Real Walking Agents using Virtual Environments. In Applications of Artificial Intelligence (pp. 105–114).
|
|
|
|
A.Kesidis, & Dimosthenis Karatzas. (2014). Logo and Trademark Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 591–646). Springer London.
Abstract: The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images.
Keywords: Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
|
|
|
|
Abel Gonzalez-Garcia, Robert Benavente, Olivier Penacchio, Javier Vazquez, Maria Vanrell, & C. Alejandro Parraga. (2013). Coloresia: An Interactive Colour Perception Device for the Visually Impaired. In Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 47–66). Springer Berlin Heidelberg.
Abstract: A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves.
|
|
|
|
Agata Lapedriza, David Masip, & Jordi Vitria. (2005). The contribution of external features to face recognition. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544.
|
|
|
|
Agata Lapedriza, & Jordi Vitria. (2005). Experimental Study of the Usefulness of External Face Features for Face Classification. In Artificial Intelligence Research and Development, IOS Press, 99–106.
|
|
|
|
Agnes Borras, & Josep Llados. (2007). Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 (Vol. 4478, 33–39).
Abstract: This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications.
|
|
|
|
Agnes Borras, & Josep Llados. (2005). Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints. In Pattern Recognition And Image Analysis (Vol. 3522, 325–332). Springer Link.
Abstract: This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling.
|
|
|
|
Alicia Fornes, & Gemma Sanchez. (2014). Analysis and Recognition of Music Scores. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. E, pp. 749–774). Springer London.
Abstract: The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented.
|
|
|
|
Alicia Fornes, Josep Llados, & Gemma Sanchez. (2008). Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 52–60). LNCS.
|
|
|
|
Alicia Fornes, Josep Llados, & Gemma Sanchez. (2006). Primitive Segmentation in Old Handwritten Music Scores. In Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299.
|
|
|
|
Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2012). Writer Identification in Old Handwritten Music Scores. In Copnstantin Papaodysseus (Ed.), Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology (pp. 27–63). IGI-Global.
Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
|
|
|
|
Alicia Fornes, Josep Llados, & Joana Maria Pujadas-Mora. (2020). Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images. In Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends. World Scientific.
|
|
|
|
Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez, & Joan Mas. (2008). Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 30–40). LNCS.
|
|
|
|
Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez, Petia Radeva, & Oriol Pujol. (2007). Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:13–21.
|
|
|
|
Alicia Fornes, V.C.Kieu, M. Visani, N.Journet, & Anjan Dutta. (2014). The ICDAR/GREC 2013 Music Scores Competition: Staff Removal. In B.Lamiroy, & J.-M. Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 207–220). LNCS. Springer Berlin Heidelberg.
Abstract: The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.
Keywords: Competition; Graphics recognition; Music scores; Writer identification; Staff removal
|
|
