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Christophe Rigaud, Dimosthenis Karatzas, Jean-Christophe Burie and Jean-Marc Ogier. 2014. Color descriptor for content-based drawing retrieval. 11th IAPR International Workshop on Document Analysis and Systems.267–271.
Abstract: Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette.
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Christophe Rigaud, Clement Guerin, Dimosthenis Karatzas, Jean-Christophe Burie and Jean-Marc Ogier. 2015. Knowledge-driven understanding of images in comic books. IJDAR, 18(3), 199–221.
Abstract: Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way.
Keywords: Document Understanding; comics analysis; expert system
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Christophe Rigaud and Clement Guerin. 2014. Localisation contextuelle des personnages de bandes dessinées. Colloque International Francophone sur l'Écrit et le Document.
Abstract: Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%.
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Chenyang Fu, Kaida Xiao, Dimosthenis Karatzas and Sophie Wuerger. 2011. Investigation of Unique Hue Setting Changes with Ageing. COL, 9(5), 053301-5.
Abstract: Clromatic sensitivity along the protan, deutan, and tritan lines and the loci of the unique hues (red, green, yellow, blue) for a very large sample (n = 185) of colour-normal observers ranging from 18 to 75 years of age are assessed. Visual judgments are obtained under normal viewing conditions using colour patches on self-luminous display under controlled adaptation conditions. Trivector discrimination thresholds show an increase as a function of age along the protan, deutan, and tritan axes, with the largest increase present along the tritan line, less pronounced shifts in unique hue settings are also observed. Based on the chromatic (protan, deutan, tritan) thresholds and using scaled cone signals, we predict the unique hue changes with ageing. A dependency on age for unique red and unique yellow for predicted hue angle is found. We conclude that the chromatic sensitivity deteriorates significantly with age, whereas the appearance of unique hues is much less affected, remaining almost constant despite the known changes in the ocular media.
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Chee-Kheng Chng and 13 others. 2019. ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT. 15th International Conference on Document Analysis and Recognition.1571–1576.
Abstract: This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 – 82.65%, ii) T2.1 – 74.3%, iii) T2.2 – 85.32%, iv) T3.1 – 53.86%, and v) T3.2 – 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.
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Carlos David Martinez Hinarejos and 10 others. 2016. Context, multimodality, and user collaboration in handwritten text processing: the CoMUN-HaT project. 3rd IberSPEECH.
Abstract: Processing of handwritten documents is a task that is of wide interest for many
purposes, such as those related to preserve cultural heritage. Handwritten text recognition techniques have been successfully applied during the last decade to obtain transcriptions of handwritten documents, and keyword spotting techniques have been applied for searching specific terms in image collections of handwritten documents. However, results on transcription and indexing are far from perfect. In this framework, the use of new data sources arises as a new paradigm that will allow for a better transcription and indexing of handwritten documents. Three main different data sources could be considered: context of the document (style, writer, historical time, topics,. . . ), multimodal data (representations of the document in a different modality, such as the speech signal of the dictation of the text), and user feedback (corrections, amendments,. . . ). The CoMUN-HaT project aims at the integration of these different data sources into the transcription and indexing task for handwritten documents: the use of context derived from the analysis of the documents, how multimodality can aid the recognition process to obtain more accurate transcriptions (including transcription in a modern version of the language), and integration into a userin-the-loop assisted text transcription framework. This will be reflected in the construction of a transcription and indexing platform that can be used by both professional and nonprofessional users, contributing to crowd-sourcing activities to preserve cultural heritage and to obtain an accessible version of the involved corpus.
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Carlos Boned Riera and Oriol Ramos Terrades. 2022. Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph. 26th International Conference on Pattern Recognition.2186–2191.
Abstract: Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.
Keywords: Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition
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Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, Jaume Rocarias and Debora Gil. 2014. Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías.
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Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, J.Roncaries and Debora Gil. 2015. Automatic evaluation of practices in Moodle for Self Learning in Engineering.
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C. Alejandro Parraga, Jordi Roca, Dimosthenis Karatzas and Sophie Wuerger. 2014. Limitations of visual gamma corrections in LCD displays. Dis, 35(5), 227–239.
Abstract: A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.
Keywords: Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
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