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Jorge L. Charco, A. D. S., Boris X. Vintimilla. (2022). Human Pose Estimation through A Novel Multi-View Scheme. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 (Vol. 5, pp. 855–862).
Abstract: This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations.
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Rafael E. Rivadeneira, A. D. S. and B. X. V. (2022). Multi-Image Super-Resolution for Thermal Images. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 (Vol. 4, pp. 635–642).
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N. Onkarappa, Cristhian A. Aguilera, B. X. Vintimilla, & Angel D. Sappa. (2014). Cross-spectral Stereo Correspondence using Dense Flow Fields. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 613–617). IEEE.
Abstract: This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.
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P. Ricaurte, C. Chilán, C. A. Aguilera-Carrasco, B. X. Vintimilla, & Angel D. Sappa. (2014). Performance Evaluation of Feature Point Descriptors in the Infrared Domain. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 (Vol. 1, pp. 545–550). IEEE.
Abstract: This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
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Julien Poujol, Cristhian A. Aguilera, Etienne Danos, Boris X. Vintimilla, Ricardo Toledo, & Angel D. Sappa. (2015). A visible-Thermal Fusion based Monocular Visual Odometry. In Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 (Vol. 417, pp. 517–528).
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
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Spencer Low, O. N., Angel D. Sappa, Erik Blasch, Nathan Inkawhich. (2023). Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 (Vol. 2023-June, pp. 515–523).
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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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Miguel Oliveira, Vítor Santos, Angel D. Sappa, & Paulo Dias. (2015). Scene representations for autonomous driving: an approach based on polygonal primitives. In Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 (Vol. 417, pp. 503–515). Springer International Publishing Switzerland 2016.
Abstract: In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
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Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, & Henry O. Velesaca. (2020). Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem. In The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 (Vol. 4, pp. 498–505).
Abstract: This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets.
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye. (2023). Thermal Image Super-Resolution Challenge Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 (Vol. 2023-June, pp. 470–478).
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