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Patricia Suarez, Dario Carpio, & Angel Sappa. (2024). Enhancement of guided thermal image super-resolution approaches. NEUCOM - Neurocomputing, 573(127197), 1–17.
Abstract: Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation.
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Jorge Charco, Angel Sappa, Boris X. Vintimilla, & Henry Velesaca. (2021). Camera pose estimation in multi-view environments: From virtual scenarios to the real world. IVC - Image and Vision Computing, 110, 104182.
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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Joan Serrat, Felipe Lumbreras, & Idoia Ruiz. (2018). Learning to measure for preshipment garment sizing. MEASURE - Measurement, 130, 327–339.
Abstract: Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.
Keywords: Apparel; Computer vision; Structured prediction; Regression
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Joan Serrat, Felipe Lumbreras, Francisco Blanco, Manuel Valiente, & Montserrat Lopez-Mesas. (2017). myStone: A system for automatic kidney stone classification. ESA - Expert Systems with Applications, 89, 41–51.
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.
Keywords: Kidney stone; Optical device; Computer vision; Image classification
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Iban Berganzo-Besga, Hector A. Orengo, Felipe Lumbreras, Paloma Aliende, & Monica N. Ramsey. (2022). Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms. JArchSci - Journal of Archaeological Science, 148, 105654.
Abstract: This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability.
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