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Rafael E. Rivadeneira, A. D. S., Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang. (2024). Thermal Image Super Resolution Challenge Results – PBVS 2024. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Angel D. Sappa, S. L., Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich. (2024). Multi-modal Aerial View Image Challenge: SAR Classification. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Angel D. Sappa, S. L., Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich. (2024). Multi-modal Aerial View Image Challenge: Sensor Domain Translation. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Patricia Suarez & Angel D. Sappa. (2024). Haze-Free Imaging through Haze-Aware Transformer Adaptations. In In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024).
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Patricia Suarez, A. D. S. (2024). A Generative Model for Guided Thermal Image Super-Resolution. In In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024.
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Xavier Soria, Angel D. Sappa, & Arash Akbarinia. (2017). Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities. In The 7th International Conference on Image Processing Theory, Tools and Application (pp. 1–6).
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Angel D. Sappa, Juan A. Carvajal, Cristhian A. Aguilera, Miguel Oliveira, Dennis G. Romero, & Boris X. Vintimilla. (2016). Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study. Sensors Journal, Vol. 16, pp. 1–15.
Abstract: This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR).
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Juan A. Carvajal, Dennis G. Romero, & Angel D. Sappa. (2016). Fine-tuning based deep covolutional networks for lepidopterous genus recognition. In XXI IberoAmerican Congress on Pattern Recognition (pp. 1–9).
Abstract: This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Cross-spectral Image Patch Similarity using Convolutional Neural Network. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–5).
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Angel J. Valencia, Roger M. Idrovo, Angel D. Sappa, Douglas Plaza G., & Daniel Ochoa. (2017). A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–6).
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