|
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).
|
|
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).
|
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.
|
Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Jin Kim, Dogun Kim et al. (2022). Thermal Image Super-Resolution Challenge Results- PBVS 2022. In Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 349–357).
Abstract: This paper presents results from the third Thermal Image
Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.
The challenge uses the same thermal image dataset as the
first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was
kept aside for testing. The evaluation tasks were to measure
the PSNR and SSIM between the SR image and the ground
truth (HR thermal noisy image downsampled by four), and
also to measure the PSNR and SSIM between the SR image
and the semi-registered HR image (acquired with another
camera). The results outperformed those from last year’s
challenge, improving both evaluation metrics. This year,
almost 100 teams participants registered for the challenge,
showing the community’s interest in this hot topic.
|
|
Jorge L. Charco, A. D. S., Boris X. Vintimilla, Henry O. Velesaca. (2022). Human Body Pose Estimation in Multi-view Environments. In ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series (Vol. 224, pp. 79–99).
|
|
Patricia L. Suárez, A. D. S. and B. X. V. (2021). Deep learning-based vegetation index estimation. In Generative Adversarial Networks for Image-to-Image Translation Book. (Vol. Chapter 9, pp. 205–232).
|
Patricia L. Suárez, A. D. S., Boris X. Vintimilla. (2021). Cycle generative adversarial network: towards a low-cost vegetation index estimation. In IEEE International Conference on Image Processing (ICIP 2021) (Vol. 2021-September, pp. 2783–2787).
Abstract: This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
|
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.
|
Rafael E. Rivadeneira, Angel D. Sappa, & Boris X. Vintimilla. (2020). Thermal Image Super-Resolution: a Novel Architecture and Dataset. 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. 111–119).
Abstract: This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large
dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal
cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal
cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty
on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach
is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are
available.
|
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Lin Guo, Jiankun Hou, Armin Mehri, et al. (2020). Thermal Image Super-Resolution Challenge – PBVS 2020. In The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) (Vol. 2020-June, pp. 432–439).
Abstract: This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework.
The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution
results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
|
