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Raul A. Mira, Patricia L. Suarez, Rafael E. Rivadeneira, & Angel D. Sappa. (2019). PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization. In IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador (pp. 1–6).
Abstract: This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Image patch similarity through a meta-learning metric based approach. In 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia (pp. 511–517).
Abstract: Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results
have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Xavier Soria, Edgar Riba, & Angel D. Sappa. (2020). Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1912–1921).
Abstract: This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.
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Angel D. Sappa, P. L. S., Henry O. Velesaca, Darío Carpio. (2022). Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. In 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 (pp. 85–92).
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Patricia L. Suarez, D. C., Angel D. Sappa and Henry O. Velesaca. (2022). Transformer based Image Dehazing. In 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. (pp. 148–154).
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Gisel Bastidas-Guacho, P. M. - V., Boris Vintimilla, Angel D. Sappa. (2023). Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches. In IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7.
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Rafael E. Rivadeneira, H. O. V., Angel D. Sappa. (2023). Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach. In 17th International Conference On Signal Image Technology & Internet Based System.
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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 (pp. 1356–1365).
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