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Author | Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | Issue | Pages | 194441 - 194456 | ||
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Abstract | 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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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ ViL2020 | Serial | 3488 | ||
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Author | Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez | ||||
Title | Recognizing new classes with synthetic data in the loop: application to traffic sign recognition | Type | Journal Article | ||
Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 20 | Issue | 3 | Pages | 583 |
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Abstract | 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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Notes | LAMP; ADAS; 600.118; 600.120 | Approved | no | ||
Call Number | Admin @ si @ VWL2020 | Serial | 3405 | ||
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Author | Gemma Sanchez; Josep Llados; K. Tombre | ||||
Title | A mean string algorithm to compute the average among a set of 2D shapes | Type | Journal Article | ||
Year | 2002 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 23 | Issue | 1-3 | Pages | 203–214 |
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Notes | DAG; IF: 0.409 | Approved | no | ||
Call Number | DAG @ dag @ SLT2002 | Serial | 275 | ||
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Author | Gerard Canal; Sergio Escalera; Cecilio Angulo | ||||
Title | A Real-time Human-Robot Interaction system based on gestures for assistive scenarios | Type | Journal Article | ||
Year | 2016 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 65-77 | |
Keywords | Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation | ||||
Abstract | Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEA2016 | Serial | 2768 | ||
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Author | Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 136 | Issue | Pages | 104689 | |
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Abstract | The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. | ||||
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Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ PDR2021 | Serial | 3635 | ||
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Author | Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 198 | Issue | Pages | 105792 | |
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Abstract | Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ PRR2021 | Serial | 3530 | ||
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Author | Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez | ||||
Title | Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps | Type | Journal Article | ||
Year | 2016 | Publication | Endoscopy | Abbreviated Journal | END |
Volume | 48 | Issue | 9 | Pages | 837-842 |
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Abstract | Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence. Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively. Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice. |
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Notes | MV; | Approved | no | ||
Call Number | Admin @ si @FBL2016 | Serial | 2778 | ||
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Author | Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell | ||||
Title | Spectral sharpening by spherical sampling | Type | Journal Article | ||
Year | 2012 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
Volume | 29 | Issue | 7 | Pages | 1199-1210 |
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Abstract | There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art. | ||||
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ISSN | 1084-7529 | ISBN | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ FVS2012 | Serial | 2000 | ||
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Author | Guillermo Torres; Debora Gil | ||||
Title | A multi-shape loss function with adaptive class balancing for the segmentation of lung structures | Type | Journal Article | ||
Year | 2020 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
Volume | 15 | Issue | 1 | Pages | S154-55 |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ ToG2020 | Serial | 3590 | ||
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Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project | Type | Journal Article | ||
Year | 2023 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCARS |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ TGM2023 | Serial | 3830 | ||
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Author | Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil | ||||
Title | An Intelligent Radiomic Approach for Lung Cancer Screening | Type | Journal Article | ||
Year | 2022 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
Volume | 12 | Issue | 3 | Pages | 1568 |
Keywords | Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization | ||||
Abstract | The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection. |
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Address | Jan 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ TBS2022 | Serial | 3699 | ||
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Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena | ||||
Title | Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior | Type | Journal Article | ||
Year | 2016 | Publication | Patter Recognition Letters | Abbreviated Journal | PRL |
Volume | 76 | Issue | 1 | Pages | 76-82 |
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Notes | IAM; 600.060 | Approved | no | ||
Call Number | Admin @ si @ MFV2017b | Serial | 2941 | ||
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Author | Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers | ||||
Title | A Statistical Method for 2D Facial Landmarking | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 21 | Issue | 2 | Pages | 844-858 |
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Abstract | IF = 3.32
Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE). |
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ DSG 2012 | Serial | 1853 | ||
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Author | Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
Title | Monitoring war destruction from space using machine learning | Type | Journal Article | ||
Year | 2021 | Publication | Proceedings of the National Academy of Sciences of the United States of America | Abbreviated Journal | PNAS |
Volume | 118 | Issue | 23 | Pages | e2025400118 |
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Abstract | 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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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ MGH2021 | Serial | 3584 | ||
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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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