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Shida Beigpour, Christian Riess, Joost Van de Weijer, & Elli Angelopoulou. (2014). Multi-Illuminant Estimation with Conditional Random Fields. TIP - IEEE Transactions on Image Processing, 23(1), 83–95.
Abstract: Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.
Keywords: color constancy; CRF; multi-illuminant
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Pau Torras, Mohamed Ali Souibgui, Sanket Biswas, & Alicia Fornes. (2023). Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images. In Document Analysis and Recognition – ICDAR 2023 Workshops (Vol. 14193, pp. 83–93). LNCS.
Abstract: Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
Keywords: Historical Manuscripts; Symbol Alignment
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Ernest Valveny, Salvatore Tabbone, Oriol Ramos Terrades, & Emilie Jean-Marie Odile. (2007). Performance Characterization of Shape Descriptors for Symbol Representation. In Seventh IAPR International Workshop on Graphics Recognition (82–83).
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Jose Antonio Rodriguez, & Florent Perronnin. (2008). Score Normalization for Hmm-based Word Spotting Using Universal Background Model. In International Conference on Frontiers in Handwriting Recognition (82–87).
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N. Serrano, L. Tarazon, D. Perez, Oriol Ramos Terrades, & S. Juan. (2010). The GIDOC Prototype. In 10th International Workshop on Pattern Recognition in Information Systems (pp. 82–89).
Abstract: Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions.
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Jaume Amores. (2013). Multiple Instance Classification: review, taxonomy and comparative study. AI - Artificial Intelligence, 201, 81–105.
Abstract: Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods.
Keywords: Multi-instance learning; Codebook; Bag-of-Words
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Hamdi Dibeklioglu, M.O. Hortas, I. Kosunen, P. Zuzánek, Albert Ali Salah, & Theo Gevers. (2011). Design and implementation of an affect-responsive interactive photo frame. JMUI - Journal on Multimodal User Interfaces, 81–95.
Abstract: This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user’s affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem.
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza, & Alicia Fornes. (2018). Optical Music Recognition by Long Short-Term Memory Networks. In B. L. A. Fornes (Ed.), Graphics Recognition. Current Trends and Evolutions (Vol. 11009, pp. 81–95). LNCS. Springer.
Abstract: Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Keywords: Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
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Asma Bensalah, Pau Riba, Alicia Fornes, & Josep Llados. (2019). Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning. In 13th IAPR International Workshop on Graphics Recognition (pp. 80–85).
Abstract: With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
Keywords: Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich, & Umapada Pal. (2007). A System to Retrieve Text/Symbols from Color Maps using Connected Component and Skeleton Analysis. In J.M. Ogier W. L. J. Llados (Ed.), Seventh IAPR International Workshop on Graphics Recognition (79–78).
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Ognjen Rudovic, & Xavier Roca. (2008). Building Temporale Templates for Human Behaviour Classification. In First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, (79–88).
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Jaume Garcia, Debora Gil, Sandra Pujades, & Francesc Carreras. (2008). Valoracion de la Funcion del Ventriculo Izquierdo mediante Modelos Regionales Hiperparametricos. Revista Española de Cardiologia, 61(3), 79.
Abstract: La mayoría de la enfermedades cardiovasculares afectan a las propiedades contráctiles de la banda ventricular helicoidal. Esto se refleja en una variación del comportamiento normal de la función ventricular. Parámetros locales tales como los strains, o la deformación experimentada por el tejido, son indicadores capaces de detectar anomalías funcionales en territorios específicos. A menudo, dichos parámetros son considerados de forma separada. En este trabajo presentamos un marco computacional (el Dominio Paramétrico Normalizado, DPN) que permite integrarlos en hiperparámetros funcionales y estudiar sus rangos de normalidad. Dichos rangos permiten valorar de forma objetiva la función regional de cualquier nuevo paciente. Para ello, consideramos secuencias de resonancia magnética etiquetada a nivel basal, medio y apical. Los hiperparámetros se obtienen a partir del movimiento intramural del VI estimado mediante el método Harmonic Phase Flow. El DPN se define a partir de en una parametrización del Ventrículo Izquierdo (VI) en sus coordenadas radiales y circunferencial basada en criterios anatómicos. El paso de los hiperparámetros al DPN hace posible la comparación entre distintos pacientes. Los rangos de normalidad se definen mediante análisis estadístico de valores de voluntarios sanos en 45 regiones del DPN a lo largo de 9 fases sistólicas. Se ha usado un conjunto de 19 (14 H; E: 30.7±7.5) voluntarios sanos para crear los patrones de normalidad y se han validado usando 2 controles sanos y 3 pacientes afectados de contractilidad global reducida. Para los controles los resultados regionales se han ajustado dentro de la normalidad, mientras que para los pacientes se han obtenido valores anormales en las zonas descritas, localizando y cuantificando así el diagnóstico empírico.
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Enric Marti, Jaume Rocarias, Debora Gil, Marc Vivet, & Carme Julia. (2008). Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Graficos por Computador.
Abstract: En esta comunicación presentamos una experiencia en Aprendizaje Basado en Proyectos (Project
Based Learning – PBL) realizada los últimos cuatro años (cursos del 2004-05 al 2007-08) en Gráficos
por Computador 2, asignatura optativa de tercer curso de Ingeniería Informática, titulación impartida
en la Escuela Técnica Superior de Ingeniería (ETSE) de la Universidad Autónoma de Barcelona
(UAB).
Fruto de la constante voluntad de mejora de la organización ABP de nuestra asignatura nos decidimos
a utilizar una herramienta LMS (Learning Management System) basada en Moodle y adaptada por
nosotros llamada Caronte para poder gestionar la documentación generada en ABP, y añadir una
componente semipresencial a la asignatura.
En primer lugar se presenta la organización de nuestra asignatura, basada proponer al alumno dos
itinerarios para cursarla: el itinerario ABP y el itinerario basado en clases magistrales i examen que
llamaremos TPPE (Teoría, Problemas, Prácticas, Examen). La dinámica ABP nos genera una cantidad
importante de documentación entre los grupos y el profesor, aparte de el feedback que el profesor
genera a los alumnos.
En la segunda parte del artículo presentamos los espacios docentes electrónicos de ambos itinerarios,
con los que trabajan los alumnos.
Finalmente, mostramos los resultados obtenidos de alumnos matriculados y de encuestas de valoración
realizados por los alumnos para finalmente exponer las conclusiones de estos cuatro años de
experiencia en ABP y en el uso de recursos virtuales en ABP, así como plantear mejoras y temas de
discusión sobre ABP.
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David Roche, Debora Gil, & Jesus Giraldo. (2012). Assessing agonist efficacy in an uncertain Em world. In A. Christopoulus and M. Bouvier (Ed.), 40th Keystone Symposia on mollecular and celular biology (79). Keystone Symposia.
Abstract: The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed.
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T.O. Nguyen, Salvatore Tabbone, Oriol Ramos Terrades, & A.T. Thierry. (2008). Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles. In Colloque International Francophone sur l'Ecrit et le Document (pp. 79–84).
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