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Patricia Suarez, A. D. S. (2024). A Generative Model for Guided Thermal Image Super-Resolution. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) Rome 27 – 29 February 2024 (Vol. Vol. 3: VISAPP, pp. 765–771).
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Xavier Soria, Y. L., Mohammad Rouhani & Angel D. Sappa. (2023). Tiny and Efficient Model for the Edge Detection Generalization. In Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023) Paris 2-6 October 2023 (pp. 1356–1365).
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Xavier Soria, A. S., Patricio Humanante, Arash Akbarinia. (2023). Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139.
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Patricia L. Suarez, D. C., Angel D. Sappa. (2024). Enhancement of Guided Thermal Image Super-Resolution Approaches (Vol. 573).
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Henry O. Velesaca, G. B., Mohammad Rouhani, Angel D. Sappa. (2024). Multimodal image registration techniques: a comprehensive survey. Multimedia Tools and Applications, Vol. 83, 63919–63947.
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Emmanuel F. Morán, B. X. V., Miguel A. Realpe. (2024). Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023 Coimbra 27 – 30 November 2023 (Vol. Vol. 14470, 257–271).
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Suarez Patricia, Carpio Dario, & Sappa Angel D. (2023). A Deep Learning Based Approach for Synthesizing Realistic Depth Maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 (Vol. 14234 LNCS, pp. 369–380).
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Juan A. Carvajal, Dennis G. Romero, & Angel D. Sappa. (2017). Fine-tuning deep convolutional networks for lepidopterous genus recognition. Lecture Notes in Computer Science, Vol. 10125 LNCS, pp. 467–475.
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Marjorie Chalen, & Boris X. Vintimilla. (2019). Towards Action Prediction Applying Deep Learning. Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019, , pp. 1–3.
Abstract: Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
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Mónica Villavicencio, & Alain Abran. (2011). Facts and Perceptions Regarding Software Measurement in Education and in Practice: Preliminary Results. Journal of Software Engineering and Application, , pp. 227–234.
Abstract: How is software measurement addressed in undergraduate and graduate programs in universities? Do organizations consider that the graduating students they hire have an adequate knowledge of software measurement? To answer these and related questions, a survey was administered to participants who attended the IWSM-MENSURA 2010 conference in Stuttgart, Germany. Forty-seven of the 69 conference participants (including software development practitioners, software measurement consultants, university professors, and graduate students) took part in the survey. The results indicate that software measurement topics are: A) covered mostly at the graduate level and not at the undergraduate level, and B) not mandatory. Graduate students and professors consider that, of the measurement topics covered in university curricula, specific topics, such as measures for the requirements phase, and measurement techniques and tools, receive more attention in the academic context. A common observation of the practitioners who participated in the survey was that students hired as new employees bring limited software measurement-related knowledge to their organizations. Discussion of the findings and directions for future research are presented.
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