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J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
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Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
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2013 |
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Expert Systems with Applications |
Abbreviated Journal |
EXWA |
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40 |
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17 |
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6707-6712 |
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Neural gas; Expert vision; Eye-tracking; Fixations |
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Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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0957-4174 |
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Admin @ si @ CRM2013 |
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2438 |
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Author |
J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat |
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Title |
Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos. |
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1993 |
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Optica Pura y Aplicada. |
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26 |
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3 |
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685–691 |
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ADAS @ adas @ PYC1993 |
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149 |
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J. Pladellorens; Joan Serrat; A. Castell; M.J. Yzuel |
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Title |
Using mathematical morphology to determine left ventricular contours. |
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1993 |
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Physics in Medicine and Biology. |
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37 |
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1877––1894 |
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ADAS @ adas @ PSC1993 |
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146 |
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Idoia Ruiz; Joan Serrat |
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Title |
Hierarchical Novelty Detection for Traffic Sign Recognition |
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2022 |
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Sensors |
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SENS |
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22 |
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12 |
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4389 |
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Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision |
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Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. |
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ADAS; 600.154 |
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Admin @ si @ RuS2022 |
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3684 |
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Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
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Title |
Improved Dynamic Window Approach by Using Lyapunov Stability Criteria |
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2008 |
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Latin American Applied Research |
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38 |
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4 |
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289–298 |
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ADAS |
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ADAS @ adas @ BSA2008 |
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1056 |
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Author |
Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Title |
Monitoring war destruction from space using machine learning |
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Journal Article |
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Year |
2021 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
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PNAS |
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118 |
Issue |
23 |
Pages |
e2025400118 |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. |
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ADAS; 600.118 |
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Admin @ si @ MGH2021 |
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3584 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Detailed 3D face reconstruction from a single RGB image |
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2019 |
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Journal of WSCG |
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JWSCG |
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27 |
Issue |
2 |
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103-112 |
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3D Wrinkle Reconstruction; Face Analysis, Optimization. |
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This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
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2019/11 |
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ADAS; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3708 |
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Author |
Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez |
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Title |
Recognizing new classes with synthetic data in the loop: application to traffic sign recognition |
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Journal Article |
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2020 |
Publication |
Sensors |
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SENS |
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20 |
Issue |
3 |
Pages |
583 |
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On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. |
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LAMP; ADAS; 600.118; 600.120 |
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Admin @ si @ VWL2020 |
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3405 |
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Author |
Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for On-Board Deep Object Detection |
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Journal Article |
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2020 |
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IEEE Access |
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ACCESS |
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194441 - 194456 |
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Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. |
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ADAS; 600.118 |
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Admin @ si @ ViL2020 |
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3488 |
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Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente |
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Title |
Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy |
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2014 |
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Journal of Biomedical Optics |
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JBiO |
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19 |
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12 |
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126004-1 - 126004-9 |
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The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. |
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ADAS; 600.076 |
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Admin @ si @ BLS2014 |
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2563 |
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