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Author Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca edit   pdf
url  openurl
  Title Camera pose estimation in multi-view environments: From virtual scenarios to the real world Type Journal Article
  Year 2021 Publication Image and Vision Computing Abbreviated Journal IVC  
  Volume (down) 110 Issue Pages 104182  
  Keywords  
  Abstract This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios.  
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  Notes MSIAU; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ CSV2021 Serial 3577  
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Author Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas edit   pdf
url  openurl
  Title myStone: A system for automatic kidney stone classification Type Journal Article
  Year 2017 Publication Expert Systems with Applications Abbreviated Journal ESA  
  Volume (down) 89 Issue Pages 41-51  
  Keywords Kidney stone; Optical device; Computer vision; Image classification  
  Abstract Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.  
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  Notes ADAS; MSIAU; 603.046; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SLB2017 Serial 3026  
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Author Daniel Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa edit  doi
openurl 
  Title A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells Type Journal Article
  Year 2022 Publication Journal of Manufacturing Systems Abbreviated Journal JMANUFSYST  
  Volume (down) 64 Issue Pages 497-507  
  Keywords Calibration; Collaborative cell; Multi-modal; Multi-sensor  
  Abstract Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs.  
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  Publisher Science Direct Place of Publication Editor  
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  Notes MSIAU; MACO Approved no  
  Call Number Admin @ si @ ROS2022 Serial 3750  
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud edit   pdf
doi  openurl
  Title A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution Type Journal Article
  Year 2022 Publication Sensors Abbreviated Journal SENS  
  Volume (down) 22 Issue 6 Pages 2254  
  Keywords Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images  
  Abstract This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.  
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  Notes MSIAU; Approved no  
  Call Number Admin @ si @ RSV2022b Serial 3688  
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Author Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa edit   pdf
url  doi
openurl 
  Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
  Year 2020 Publication Sensors Abbreviated Journal SENS  
  Volume (down) 20 Issue 11 Pages 3249  
  Keywords stereo matching; deep learning; embedded GPU  
  Abstract Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices.  
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  Notes MSIAU; 600.122 Approved no  
  Call Number Admin @ si @ AAN2020 Serial 3428  
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