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| Author | Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen | ||||
| Title | Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset | Type | Journal Article | ||
| Year | 2023 | Publication | Frontiers in Cardiovascular Medicine | Abbreviated Journal  |
FCM |
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RMN2023 | Serial | 3937 | ||
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| Author | Olivier Penacchio; Xavier Otazu; Arnold J Wilkings; Sara M. Haigh | ||||
| Title | A mechanistic account of visual discomfort | Type | Journal Article | ||
| Year | 2023 | Publication | Frontiers in Neuroscience | Abbreviated Journal |
FN |
| Volume | 17 | Issue | Pages | ||
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| Abstract | Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable in a biologically based neurodynamic model of the early visual cortex that included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted toward values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress. | ||||
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| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ POW2023 | Serial | 3886 | ||
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| Author | Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach | ||||
| Title | Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia | Type | Journal Article | ||
| Year | 2020 | Publication | Gastroenterología y Hepatología | Abbreviated Journal |
GH |
| Volume | 43 | Issue | 4 | Pages | 222-232 |
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| Abstract | Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented. | ||||
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| Notes | MV; | Approved | no | ||
| Call Number | Admin @ si @ SBG2020 | Serial | 3404 | ||
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| Author | Fernando Vilariño; Stephan Ameling; Gerard Lacey; Stephen Patchett; Hugh Mulcahy | ||||
| Title | Eye Tracking Search Patterns in Expert and Trainee Colonoscopists: A Novel Method of Assessing Endoscopic Competency? | Type | Journal Article | ||
| Year | 2009 | Publication | Gastrointestinal Endoscopy | Abbreviated Journal |
GI |
| Volume | 69 | Issue | 5 | Pages | 370 |
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | fernando @ fernando @ | Serial | 2420 | ||
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| Author | G.Blasco; Simone Balocco; J.Puig; J.Sanchez-Gonzalez; W.Ricart; J.Daunis-I-Estadella; X.Molina; S.Pedraza; J.M.Fernandez-Real | ||||
| Title | Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Cardiovascular Imaging | Abbreviated Journal |
ICJI |
| Volume | 31 | Issue | 3 | Pages | 603-612 |
| Keywords | Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance | ||||
| Abstract | Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1569-5794 | ISBN | Medium | ||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BBP2015 | Serial | 2670 | ||
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| Author | Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov | ||||
| Title | An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Sensors Journal | Abbreviated Journal |
IEEE-SENS |
| Volume | 23 | Issue | 24 | Pages | |
| Keywords | Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov | ||||
| Abstract | Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ CQL2023 | Serial | 3884 | ||
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| Author | Frederic Sampedro; Sergio Escalera | ||||
| Title | Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | IET Computer Vision | Abbreviated Journal |
IETCV |
| Volume | 9 | Issue | 3 | Pages | 439 - 446 |
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| Abstract | In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. | ||||
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| ISSN | 1751-9632 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SaE2015 | Serial | 2551 | ||
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| Author | Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund | ||||
| Title | Back-dropout Transfer Learning for Action Recognition | Type | Journal Article | ||
| Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal |
IETCV |
| Volume | 12 | Issue | 4 | Pages | 484-491 |
| Keywords | Learning (artificial intelligence); Pattern Recognition | ||||
| Abstract | Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKM2018 | Serial | 3071 | ||
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| Author | Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli | ||||
| Title | Recurrent Neural Networks for Remote Sensing Image Classification | Type | Journal Article | ||
| Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal |
IETCV |
| Volume | 12 | Issue | 7 | Pages | 1040 - 1045 |
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| Abstract | Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LÇE2018 | Serial | 3119 | ||
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| Author | Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez | ||||
| Title | End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data | Type | Journal Article | ||
| Year | 2022 | Publication | IET Computer Vision | Abbreviated Journal |
IETCV |
| Volume | 16 | Issue | 1 | Pages | 50-66 |
| Keywords | Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation | ||||
| Abstract | Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. | ||||
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| Notes | HUPBA; ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ MEB2022 | Serial | 3652 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa | ||||
| Title | Multimodal Inverse Perspective Mapping | Type | Journal Article | ||
| Year | 2015 | Publication | Information Fusion | Abbreviated Journal |
IF |
| Volume | 24 | Issue | Pages | 108–121 | |
| Keywords | Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles | ||||
| Abstract | Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. | ||||
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| Notes | ADAS; 600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OSS2015c | Serial | 2532 | ||
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| Author | Jose Elias Yauri; M. Lagos; H. Vega-Huerta; P. de-la-Cruz; G.L.E Maquen-Niño; E. Condor-Tinoco | ||||
| Title | Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings | Type | Journal Article | ||
| Year | 2023 | Publication | International Journal of Advanced Computer Science and Applications | Abbreviated Journal |
IJACSA |
| Volume | 14 | Issue | 5 | Pages | 1067-1074 |
| Keywords | Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention | ||||
| Abstract | According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches. | ||||
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3856 | ||
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| Author | Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf | ||||
| Title | Robust lane markings detection and road geometry computation | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal of Automotive Technology | Abbreviated Journal |
IJAT |
| Volume | 11 | Issue | 3 | Pages | 395–407 |
| Keywords | lane markings | ||||
| Abstract | Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. | ||||
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| Publisher | The Korean Society of Automotive Engineers | Place of Publication | Editor | ||
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| ISSN | 1229-9138 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LSC2010 | Serial | 1300 | ||
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| Author | Hans Stadthagen-Gonzalez; M. Carmen Parafita; C. Alejandro Parraga; Markus F. Damian | ||||
| Title | Testing alternative theoretical accounts of code-switching: Insights from comparative judgments of adjective noun order | Type | Journal Article | ||
| Year | 2019 | Publication | International journal of bilingualism: interdisciplinary studies of multilingual behaviour | Abbreviated Journal |
IJB |
| Volume | 23 | Issue | 1 | Pages | 200-220 |
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| Abstract | Objectives:
Spanish and English contrast in adjective–noun word order: for example, brown dress (English) vs. vestido marrón (‘dress brown’, Spanish). According to the Matrix Language model (MLF) word order in code-switched sentences must be compatible with the word order of the matrix language, but working within the minimalist program (MP), Cantone and MacSwan arrived at the descriptive generalization that the position of the noun phrase relative to the adjective is determined by the adjective’s language. Our aim is to evaluate the predictions derived from these two models regarding adjective–noun order in Spanish–English code-switched sentences. Methodology: We contrasted the predictions from both models regarding the acceptability of code-switched sentences with different adjective–noun orders that were compatible with the MP, the MLF, both, or none. Acceptability was assessed in Experiment 1 with a 5-point Likert and in Experiment 2 with a 2-Alternative Forced Choice (2AFC) task. |
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SPP2019 | Serial | 3242 | ||
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| Author | Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil | ||||
| Title | Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal |
IJCAR |
| Volume | 10 | Issue | 6 | Pages | 935-945 |
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| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SBS2015a | Serial | 2611 | ||
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