Angel Morera, Angel Sanchez, A. Belen Moreno, Angel Sappa, & Jose F. Velez. (2020). SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. SENS - Sensors, 20(16), 4587.
Abstract: This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.
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Jose Luis Gomez, Gabriel Villalonga, & Antonio Lopez. (2021). Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches. SENS - Sensors, 21(9), 3185.
Abstract: Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.
Keywords: co-training; multi-modality; vision-based object detection; ADAS; self-driving
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Idoia Ruiz, & Joan Serrat. (2022). Hierarchical Novelty Detection for Traffic Sign Recognition. SENS - Sensors, 22(12), 4389.
Abstract: 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.
Keywords: Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision
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Rafael E. Rivadeneira, Angel Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2022). A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution. SENS - Sensors, 22(6), 2254.
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.
Keywords: Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images
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Aura Hernandez-Sabate, Jose Elias Yauri, Pau Folch, Daniel Alvarez, & Debora Gil. (2024). EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment. SENS - Sensors, 24(4), 1174.
Abstract: High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
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Saad Minhas, Zeba Khanam, Shoaib Ehsan, Klaus McDonald Maier, & Aura Hernandez-Sabate. (2022). Weather Classification by Utilizing Synthetic Data. SENS - Sensors, 22(9), 3193.
Abstract: Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.
Keywords: Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems
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Victor M. Campello, Carlos Martin-Isla, Cristian Izquierdo, Andrea Guala, Jose F. Rodriguez Palomares, David Vilades, et al. (2022). Minimising multi-centre radiomics variability through image normalisation: a pilot study. ScR - Scientific Reports, 12(1), 12532.
Abstract: Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
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Iban Berganzo-Besga, Hector A. Orengo, Felipe Lumbreras, Aftab Alam, Rosie Campbell, Petrus J Gerrits, et al. (2023). Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan. ScR - Scientific Reports, 13, 11257.
Abstract: This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
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Miguel Oliveira, Victor Santos, Angel Sappa, P. Dias, & A. Moreira. (2016). Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives. RAS - Robotics and Autonomous Systems, 83, 312–325.
Abstract: When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. 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. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
Keywords: Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives
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Angel Sappa, Cristhian A. Aguilera-Carrasco, Juan A. Carvajal Ayala, Miguel Oliveira, Dennis Romero, Boris X. Vintimilla, et al. (2016). Monocular visual odometry: A cross-spectral image fusion based approach. RAS - Robotics and Autonomous Systems, 85, 26–36.
Abstract: This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
Keywords: Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion
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Miguel Oliveira, Victor Santos, Angel Sappa, P. Dias, & A. Moreira. (2016). Incremental texture mapping for autonomous driving. RAS - Robotics and Autonomous Systems, 84, 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
Keywords: Scene reconstruction; Autonomous driving; Texture mapping
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O. Rodriguez, J. Mauri, E Fernandez-Nofrerias, C. Garcia, R. Villuendas, A. Tovar, et al. (2003). Model Empiric de Simulacio d Ecografia Intravascular. Revista Societat Catalana de Cardiologia, 4(4):42, XIVe Congres de la Societat Catalana de Cardiologia, .
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Oriol Rodriguez-Leor, J. Mauri, Eduard Fernandez-Nofrerias, Antonio Tovar, Vicente del Valle, Aura Hernandez-Sabate, et al. (2004). Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria. Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología, 57(2), 100.
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Frederic Sampedro, Anna Domenech, Sergio Escalera, & Ignasi Carrio. (2017). Computing quantitative indicators of structural renal damage in pediatric DMSA scans. REMNIM - Revista Española de Medicina Nuclear e Imagen Molecular, 36(2), 72–77.
Abstract: OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.
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Jaume Garcia, Debora Gil, Sandra Pujades, & Francesc Carreras. (2008). Valoracion de la Funcion del Ventriculo Izquierdo mediante Modelos Regionales Hiperparametricos. Revista Española de Cardiologia, 61(3), 79.
Abstract: La mayoría de la enfermedades cardiovasculares afectan a las propiedades contráctiles de la banda ventricular helicoidal. Esto se refleja en una variación del comportamiento normal de la función ventricular. Parámetros locales tales como los strains, o la deformación experimentada por el tejido, son indicadores capaces de detectar anomalías funcionales en territorios específicos. A menudo, dichos parámetros son considerados de forma separada. En este trabajo presentamos un marco computacional (el Dominio Paramétrico Normalizado, DPN) que permite integrarlos en hiperparámetros funcionales y estudiar sus rangos de normalidad. Dichos rangos permiten valorar de forma objetiva la función regional de cualquier nuevo paciente. Para ello, consideramos secuencias de resonancia magnética etiquetada a nivel basal, medio y apical. Los hiperparámetros se obtienen a partir del movimiento intramural del VI estimado mediante el método Harmonic Phase Flow. El DPN se define a partir de en una parametrización del Ventrículo Izquierdo (VI) en sus coordenadas radiales y circunferencial basada en criterios anatómicos. El paso de los hiperparámetros al DPN hace posible la comparación entre distintos pacientes. Los rangos de normalidad se definen mediante análisis estadístico de valores de voluntarios sanos en 45 regiones del DPN a lo largo de 9 fases sistólicas. Se ha usado un conjunto de 19 (14 H; E: 30.7±7.5) voluntarios sanos para crear los patrones de normalidad y se han validado usando 2 controles sanos y 3 pacientes afectados de contractilidad global reducida. Para los controles los resultados regionales se han ajustado dentro de la normalidad, mientras que para los pacientes se han obtenido valores anormales en las zonas descritas, localizando y cuantificando así el diagnóstico empírico.
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