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Arash Akbarinia, & C. Alejandro Parraga. (2015). Biologically Plausible Colour Naming Model. In European Conference on Visual Perception ECVP2015.
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Anton Cervantes. (2005). Biometric Newborn Identification.
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Anton Cervantes, Gemma Sanchez, Josep Llados, Agnes Borras, & A. Rodriguez. (2005). Biometric Recognition Based on Line Shape Descriptors. In Sixth IAPR International Workshop on Graphics Recognition (GREC 2005) (335–344).
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Anton Cervantes, Gemma Sanchez, Josep Llados, Agnes Borras, & Ana Rodriguez. (2006). Biometric Recognition Based on Line Shape Descriptors. In Lecture Notes in Computer Science (Vol. 3926, 346–357,). Springer Link.
Abstract: Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.
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Sonia Baeza, Debora Gil, Carles Sanchez, Guillermo Torres, Ignasi Garcia Olive, Ignasi Guasch, et al. (2023). Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung. In SEPAR.
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David Rotger, Petia Radeva, E Fernandez-Nofrerias, & J. Mauri. (2007). Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration. In Computer Analysis of Images and Patterns, 12th International Conference (Vol. 4673, 285–292). LNCS.
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David Rotger, Petia Radeva, E Fernandez-Nofrerias, & J. Mauri. (2007). Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion. In Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA (Vol. 163, 197–204).
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Joan M. Nuñez, Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2013). Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization. In Proceedings of the International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 162–171). SciTePress.
Abstract: This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects,such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization
algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance.
Keywords: Colonoscopy; Blood vessel; Linear features; Valley detection
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Hugo Bertiche, Niloy J Mitra, Kuldeep Kulkarni, Chun Hao Paul Huang, Tuanfeng Y Wang, Meysam Madadi, et al. (2023). Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images. In 36th IEEE Conference on Computer Vision and Pattern Recognition (pp. 459–468).
Abstract: Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images.
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Sophie Wuerger, Kaida Xiao, Dimitris Mylonas, Q. Huang, Dimosthenis Karatzas, & Galina Paramei. (2012). Blue green color categorization in mandarin english speakers. JOSA A - Journal of the Optical Society of America A, 29(2), A102–A1207.
Abstract: Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.
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Enric Marti, G.Estape, & S.Fernandez. (2008). Bluestar: Diseño e implementación de una aplicación para la visualización interactiva en 3D de funciones reales como herramienta de aprendizaje. Lleida.
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Enric Marti, S.Fernandez, & G.Estape. (2007). BlueStar: implementació d’una aplicació per a la visualització 3D de funcions reals, com a eina d’aprenentatge. Bellaterra (Spain).
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Sergio Escalera, Alicia Fornes, O. Pujol, Petia Radeva, Gemma Sanchez, & Josep Llados. (2009). Blurred Shape Model for Binary and Grey-level Symbol Recognition. PRL - Pattern Recognition Letters, 30(15), 1424–1433.
Abstract: Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.
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Kai Wang, Luis Herranz, Anjan Dutta, & Joost Van de Weijer. (2020). Bookworm continual learning: beyond zero-shot learning and continual learning. In Workshop TASK-CV 2020.
Abstract: We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem.
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David Masip, & Jordi Vitria. (2006). Boosted discriminant projections for nearest neighbor classification. Pattern Recognition, 39(2): 164–170.
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