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Cristina Cañero, Petia Radeva, M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, et al. (2002). Modelo experimental para la reconstruccion tridimensional de las arterias coronarias a partir de imagenes de coronariografia.
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Mikhail Mozerov, V. Kober, & I.A. Ovseyevich. (2007). Robust Dynamic Programming Algorithm for Motion Detection and Estimation.
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V. Kober, Mikhail Mozerov, Josue Albarez, & I.A. Ovseyevich. (2007). Algorithms for Impulse Noise Renoval from Corrupted Color Images.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes.
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Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2008). Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems.
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Xavier Otazu, & Maria Vanrell. (2006). Several lightness induction effects with a computational multiresolution wavelet framework. 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56.
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S. Chanda, Umapada Pal, & Oriol Ramos Terrades. (2009). Word-Wise Thai and Roman Script Identification. TALIP - ACM Transactions on Asian Language Information Processing, 1–21.
Abstract: In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.
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Xiangyang Li, Luis Herranz, & Shuqiang Jiang. (2020). Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition. ACM - ACM Transactions on Data Science.
Abstract: In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.
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Patricia Suarez, Henry Velesaca, Dario Carpio, & Angel Sappa. (2023). Corn kernel classification from few training samples. Artificial Intelligence in Agriculture, 89–99.
Abstract: This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.
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O. Fors, A. Richichi, Xavier Otazu, & J. Nuñez. (2008). A new wavelet-based approach for the automated treatment of large sets of lunar occultation data. Astronomy and Astrohysics, 297–304.
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A. Richichi, O. Fors, M.T. Merino, Xavier Otazu, J. Nuñez, A. Prades, et al. (2006). The Calar Alto lunar occultation program: update and new results. Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088.
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Francesca Vidal, et al. (2017). Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals. JBR - Biologia de la Reproduccio, 73–78.
Abstract: In somatic cells, chromosomes occupy specific nuclear regions called chromosome territories which are involved in the
maintenance and regulation of the genome. Preliminary data in male germ cells also suggest the importance of chromosome
territoriality in cell functionality. Nevertheless, the specific characteristics of testicular tissue (presence of different
cell types with different morphological characteristics, in different stages of development and with different ploidy)
makes difficult to achieve conclusive results. In this study we have developed a methodology to approach the threedimensional
study of all chromosome territories in male germ cells from C57BL/6J mice (Mus musculus). The method
includes the following steps: i) Optimized cell fixation to obtain an optimal preservation of the three-dimensionality cell
morphology, ii) Chromosome identification by FISH (Chromoprobe Multiprobe® OctoChrome™ Murine System; Cytocell)
and confocal microscopy (TCS-SP5, Leica Microsystems), iii) Cell type identification by immunofluorescence
iv) Image analysis using Matlab scripts, v) Numerical data extraction related to chromosome features, chromosome
radial position and chromosome relative position. This methodology allows the unequivocally identification and the
analysis of the chromosome territories of all spermatogenic stages. Results will provide information about the features
that determine chromosomal position, preferred associations between chromosomes, and the relationship between chromosome
positioning and genome regulation.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2006). A data set for fuzzy colour naming. Color Research & Application, 31(1):48–56.
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