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| Author | Agata Lapedriza; David Masip; Jordi Vitria | ||||
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On the Use of External Face Features for Identity Verification | Type | Journal | ||
| Year | 2006 | Publication | Journal of Multimedia, 1(4): 11–20 | Abbreviated Journal | |
| Volume | 1 | Issue | 4 | Pages | 11-20 |
| Keywords | Face Verification, Computer Vision, Machine Learning | ||||
| Abstract | In general automatic face classification applications images are captured in natural environments. In these cases, the performance is affected by variations in facial images related to illumination, pose, occlusion or expressions. Most of the existing face classification systems use only the internal features information, composed by eyes, nose and mouth, since they are more difficult to imitate. Nevertheless, nowadays a lot of applications not related to security are developed, and in these cases the information located at head, chin or ears zones (external features) can be useful to improve the current accuracies. However, the lack of a natural alignment in these areas makes difficult to extract these features applying classic Bottom-Up methods. In this paper, we propose a complete scheme based on a Top-Down reconstruction algorithm to extract external features of face images. To test our system we have performed face verification experiments using public databases, given that identity verification is a general task that has many real life applications. We have considered images uniformly illuminated, images with occlusions and images with high local changes in the illumination, and the obtained results show that the information contributed by the external features can be useful for verification purposes, specially significant when faces are partially occluded. | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ LMV2006b | Serial | 708 | ||
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| Author | Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo | ||||
| Title |
On the synthesis of visual illusions using deep generative models | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
| Volume | 22(8) | Issue | 2 | Pages | 1-18 |
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| Abstract | Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference. | ||||
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| Notes | LAMP; 600.161; 611.007 | Approved | no | ||
| Call Number | Admin @ si @ GMV2022 | Serial | 3682 | ||
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| Author | A. Pujol; Alex Caralps; Juan J. Villanueva | ||||
| Title |
On the suitability of pixel-outlier removal in face recognition. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:241–247 | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ PCV2001 | Serial | 152 | ||
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| Author | Petia Radeva | ||||
| Title |
On the Role of Intravascular Ultrasound Image Analysis | Type | Miscellaneous | ||
| Year | 2003 | Publication | Angiography and Plaque Imaging: Advanced Segmentation Techniques, CRC, pp.397–450, ISBN: 0849317401 | Abbreviated Journal | |
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Rad2003 | Serial | 402 | ||
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| Author | Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli | ||||
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On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CTR2018 | Serial | 3184 | ||
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| Author | Miquel Ferrer; F. Serratosa; Ernest Valveny | ||||
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On the Relation Between the Median Graph and the Maximum Common Subgraph of a Set of Graphs | Type | Book Chapter | ||
| Year | 2007 | Publication | Abbreviated Journal | ||
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| Address | Alicante (Spain) | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FSV2007 | Serial | 790 | ||
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| Author | David Masip; Jordi Vitria | ||||
| Title |
On the Nearest Neighbor Approach for Gender Recognition | Type | Miscellaneous | ||
| Year | 2003 | Publication | In I. Aguilo, Ll. Valverde M.T. Escrig, editors. Artificial Intelligence Research and Development. IOS PRESS pp.178–188 | Abbreviated Journal | |
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| Address | Amsterdam | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MaV2003b | Serial | 387 | ||
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| Author | David Masip; Jordi Vitria | ||||
| Title |
On the Nearest Neighbor Approach for Gender Recognition | Type | Miscellaneous | ||
| Year | 2003 | Publication | 6th Catalonian Conference on Artificial Intelligence | Abbreviated Journal | |
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| Address | Palma de Mallorca | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MaV2003c | Serial | 392 | ||
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| Author | Debora Gil; Jose Maria-Carazo; Roberto Marabini | ||||
| Title |
On the nature of 2D crystal unbending | Type | Journal Article | ||
| Year | 2006 | Publication | Journal of Structural Biology | Abbreviated Journal | |
| Volume | 156 | Issue | 3 | Pages | 546-555 |
| Keywords | Electron microscopy | ||||
| Abstract | Crystal unbending, the process that aims to recover a perfect crystal from experimental data, is one of the more important steps in electron crystallography image processing. The unbending process involves three steps: estimation of the unit cell displacements from their ideal positions, extension of the deformation field to the whole image and transformation of the image in order to recover an ideal crystal. In this work, we present a systematic analysis of the second step oriented to address two issues. First, whether the unit cells remain undistorted and only the distance between them should be changed (rigid case) or should be modified with the same deformation suffered by the whole crystal (elastic case). Second, the performance of different extension algorithms (interpolation versus approximation) is explored. Our experiments show that there is no difference between elastic and rigid cases or among the extension algorithms. This implies that the deformation fields are constant over large areas. Furthermore, our results indicate that the main source of error is the transformation of the crystal image. | ||||
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| ISSN | 1047-8477 | ISBN | Medium | ||
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| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ GCM2006 | Serial | 1519 | ||
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| Author | Akhil Gurram; Antonio Lopez | ||||
| Title |
On the Metrics for Evaluating Monocular Depth Estimation | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ GuL2023 | Serial | 3867 | ||
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| Author | Mohammed Al Rawi; Dimosthenis Karatzas | ||||
| Title |
On the Labeling Correctness in Computer Vision Datasets | Type | Conference Article | ||
| Year | 2018 | Publication | Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
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| Abstract | Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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| Area | Expedition | Conference | ECML-PKDDW | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RaK2018 | Serial | 3144 | ||
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| Author | Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes | ||||
| Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2021 | Publication | International Society for Music Information Retrieval Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 690-696 | ||
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| Abstract | Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. | ||||
| Address | Virtual; November 2021 | ||||
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| Area | Expedition | Conference | ISMIR | ||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ TBK2021 | Serial | 3616 | ||
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| Author | Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta | ||||
| Title |
On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
| Volume | 26 | Issue | 5 | Pages | 1263002-126027 |
| Keywords | Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 | ||||
| Abstract | 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ LRF2012 | Serial | 2128 | ||
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| Author | David Fernandez; Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title |
On the Influence of Key Point Encoding for Handwritten Word Spotting | Type | Conference Article | ||
| Year | 2014 | Publication | 14th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 476 - 481 | ||
| Keywords | Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis | ||||
| Abstract | In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries. | ||||
| Address | Creete Island; Grecia; September 2014 | ||||
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| ISSN | 2167-6445 | ISBN | 978-1-4799-4335-7 | Medium | |
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ FRF2014 | Serial | 2460 | ||
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| Author | Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski | ||||
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On the importance of cross-task features for class-incremental learning | Type | Conference Article | ||
| Year | 2021 | Publication | Theory and Foundation of continual learning workshop of ICML | Abbreviated Journal | |
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| Abstract | In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. | ||||
| Address | Virtual; July 2021 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SMW2021 | Serial | 3588 | ||
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