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Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey, Sayan Chakraborty, Luca Saba, Dinesh Kumar, et al. (2015). A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. CMPB - Computer Methods and Programs in Biomedicine, 118(2), 158–172.
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Javier Vazquez, G. D. Finlayson, & Maria Vanrell. (2010). A compact singularity function to predict WCS data and unique hues. In 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science (33–38).
Abstract: Understanding how colour is used by the human vision system is a widely studied research field. The field, though quite advanced, still faces important unanswered questions. One of them is the explanation of the unique hues and the assignment of color names. This problem addresses the fact of different perceptual status for different colors.
Recently, Philipona and O'Regan have proposed a biological model that allows to extract the reflection properties of any surface independently of the lighting conditions. These invariant properties are the basis to compute a singularity index that predicts the asymmetries presented in unique hues and basic color categories psychophysical data, therefore is giving a further step in their explanation.
In this paper we build on their formulation and propose a new singularity index. This new formulation equally accounts for the location of the 4 peaks of the World colour survey and has two main advantages. First, it is a simple elegant numerical measure (the Philipona measurement is a rather cumbersome formula). Second, we develop a colour-based explanation for the measure.
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Alicia Fornes, Josep Llados, Gemma Sanchez, Xavier Otazu, & Horst Bunke. (2010). A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores. IJDAR - International Journal on Document Analysis and Recognition, 13(4), 243–259.
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 an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers.
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Javier Vazquez, Maria Vanrell, Anna Salvatella, & Eduard Vazquez. (2007). A colour space based on the image content. In Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press.
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Robert Benavente, & Maria Vanrell. (2001). A colour naming experiment.
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados, & Alicia Fornes. (2014). A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance. In 22nd International Conference on Pattern Recognition (pp. 3074–3079).
Abstract: Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
Keywords: word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
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Jon Almazan, David Fernandez, Alicia Fornes, Josep Llados, & Ernest Valveny. (2012). A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 453–458).
Abstract: In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.
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Marco Pedersoli, Andrea Vedaldi, & Jordi Gonzalez. (2011). A Coarse-to-fine Approach for fast Deformable Object Detection. In IEEE conference on Computer Vision and Pattern Recognition (pp. 1353–1360).
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Marco Pedersoli, Andrea Vedaldi, Jordi Gonzalez, & Xavier Roca. (2015). A coarse-to-fine approach for fast deformable object detection. PR - Pattern Recognition, 48(5), 1844–1853.
Abstract: We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
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Diego Velazquez, Pau Rodriguez, Josep M. Gonfaus, Xavier Roca, & Jordi Gonzalez. (2022). A Closer Look at Embedding Propagation for Manifold Smoothing. JMLR - Journal of Machine Learning Research, 23(252), 1–27.
Abstract: Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and
self-supervised learning performance.
Keywords: Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification
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Lubomir Latchev, Maya Dimitrova, & David Rotger. (2006). A Classifier of Technical Diagnostic States of Electrocardiograph.
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Onur Ferhat, Fernando Vilariño, & F. Javier Sanchez. (2014). A cheap portable eye-tracker solution for common setups. JEMR - Journal of Eye Movement Research, 7(3), 1–10.
Abstract: We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system which can work on a computer with 30Hz sampling rate. After assessing the accuracy of our eye-tracker in elaborated experiments involving 12 subjects under 4 different system setups, we install it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.42° horizontal accuracy with 3Hz refresh rate for a building cost of 70 Euros.
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Onur Ferhat, & Fernando Vilariño. (2013). A Cheap Portable Eye-Tracker Solution for Common Setups. In 17th European Conference on Eye Movements.
Abstract: We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system. After assessing the accuracy of our eye-tracker in elaborated experiments involving 18 subjects under 4 different system setups, we developed a simple game to see how it performs in practice and we also installed it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.62° horizontal accuracy with 3 fps refresh rate for a building cost of 70 Euros.
Keywords: Low cost; eye-tracker; software; webcam; Raspberry Pi
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Josep Llados, Ernest Valveny, Gemma Sanchez, & Enric Marti. (2003). A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa. In Proceedings of Pattern Recognition in Information Systems (pp. 1–13). ICEIS Press.
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Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, et al. (2016). A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. In AutoML Workshop (pp. 1–8).
Abstract: The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Keywords: AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
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