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| Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Pay attention to the activations: a modular attention mechanism for fine-grained image recognition | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
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22 | Issue | 2 | Pages | 502-514 |
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| Abstract | Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%. | ||||
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| Notes | ISE; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ RVC2020a | Serial | 3417 | ||
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| Author | Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal | ||||
| Title | Evaluation of the Effect of Improper Segmentation on Word Spotting | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
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22 | Issue | Pages | 361-374 | |
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| Abstract | Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods. | ||||
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| Notes | DAG; 600.097; 600.084; 600.121; 600.140; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DNL2019 | Serial | 3455 | ||
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| Author | Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Joaquim Punti Vidal; Pilar Medina Bravo; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez | ||||
| Title | Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis | Type | Journal Article | ||
| Year | 2020 | Publication | Journal of Medical Internet Research | Abbreviated Journal | JMIR |
| Volume |
22 | Issue | 7 | Pages | e17758 |
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| Abstract | Background:
Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective: This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods: We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results: We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions: The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders. |
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| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RFB2020 | Serial | 3552 | ||
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| Author | Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard | ||||
| Title | DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification | Type | Journal Article | ||
| Year | 2021 | Publication | BMC Bioinformatics | Abbreviated Journal | |
| Volume |
22 | Issue | Pages | 473 | |
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| Abstract | Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ DAP2021 | Serial | 3650 | ||
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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 |
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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 | Idoia Ruiz; Joan Serrat | ||||
| Title | Hierarchical Novelty Detection for Traffic Sign Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume |
22 | Issue | 12 | Pages | 4389 |
| Keywords | Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision | ||||
| 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. | ||||
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| Notes | ADAS; 600.154 | Approved | no | ||
| Call Number | Admin @ si @ RuS2022 | Serial | 3684 | ||
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| Author | Xavier Otazu; Xim Cerda-Company | ||||
| Title | The contribution of luminance and chromatic channels to color assimilation | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
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22(6) | Issue | 10 | Pages | 1-15 |
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| Abstract | Color induction is the phenomenon where the physical and the perceived colors of an object differ owing to the color distribution and the spatial configuration of the surrounding objects. Previous works studying this phenomenon on the lsY MacLeod–Boynton color space, show that color assimilation is present only when the magnocellular pathway (i.e., the Y axis) is activated (i.e., when there are luminance differences). Concretely, the authors showed that the effect is mainly induced by the koniocellular pathway (s axis), but not by the parvocellular pathway (l axis), suggesting that when magnocellular pathway is activated it inhibits the koniocellular pathway. In the present work, we study whether parvo-, konio-, and magnocellular pathways may influence on each other through the color induction effect. Our results show that color assimilation does not depend on a chromatic–chromatic interaction, and that chromatic assimilation is driven by the interaction between luminance and chromatic channels (mainly the magno- and the koniocellular pathways). Our results also show that chromatic induction is greatly decreased when all three visual pathways are simultaneously activated, and that chromatic pathways could influence each other through the magnocellular (luminance) pathway. In addition, we observe that chromatic channels can influence the luminance channel, hence inducing a small brightness induction. All these results show that color induction is a highly complex process where interactions between the several visual pathways are yet unknown and should be studied in greater detail. | ||||
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| Notes | Neurobit; 600.128; 600.120; 600.158 | Approved | no | ||
| Call Number | Admin @ si @ OtC2022 | Serial | 3685 | ||
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| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume |
22 | Issue | 6 | Pages | 2254 |
| Keywords | Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images | ||||
| 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. | ||||
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| Notes | MSIAU; | Approved | no | ||
| Call Number | Admin @ si @ RSV2022b | Serial | 3688 | ||
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| Author | Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate | ||||
| Title | Weather Classification by Utilizing Synthetic Data | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume |
22 | Issue | 9 | Pages | 3193 |
| Keywords | Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems | ||||
| 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. | ||||
| Address | 21 April 2022 | ||||
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| Publisher | MDPI | Place of Publication | Editor | ||
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| Notes | IAM; 600.139; 600.159; 600.166; 600.145; | Approved | no | ||
| Call Number | Admin @ si @ MKE2022 | Serial | 3761 | ||
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| Author | A. Martinez; Jordi Vitria | ||||
| Title | Learning mixture models using a genetic version of the EM algorithm. | Type | Journal Article | ||
| Year | 2000 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
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21 | Issue | 8 | Pages | 759–769 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MVi2000 | Serial | 335 | ||
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| Author | Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva | ||||
| Title | On Tracking Inside Groups | Type | Journal Article | ||
| Year | 2010 | Publication | Machine Vision and Applications | Abbreviated Journal | MVA |
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21 | Issue | 2 | Pages | 113–127 |
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| Abstract | This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
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| ISSN | 0932-8092 | ISBN | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ RGP2010 | Serial | 1158 | ||
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| Author | Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada | ||||
| Title | Intestinal motor activity, endoluminal motion and transit | Type | Journal Article | ||
| Year | 2009 | Publication | Neurogastroenterology & Motility | Abbreviated Journal | NEUMOT |
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21 | Issue | 12 | Pages | 1264–e119 |
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| Abstract | A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. | ||||
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| Notes | OR;MILAB;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ DMA2009 | Serial | 1251 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Traffic sign recognition system with β -correction | Type | Journal Article | ||
| Year | 2010 | Publication | Machine Vision and Applications | Abbreviated Journal | MVA |
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21 | Issue | 2 | Pages | 99–111 |
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| Abstract | Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
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| ISSN | 0932-8092 | ISBN | Medium | ||
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| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010a | Serial | 1276 | ||
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| Author | Debora Gil; Petia Radeva | ||||
| Title | Shape Restoration via a Regularized Curvature Flow | Type | Journal Article | ||
| Year | 2004 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | |
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21 | Issue | 3 | Pages | 205-223 |
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| Abstract | Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes.
In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications. |
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| Notes | IAM;MILAB | Approved | no | ||
| Call Number | IAM @ iam @ GiR2004c | Serial | 1532 | ||
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| Author | Carme Julia; Felipe Lumbreras; Angel Sappa | ||||
| Title | A Factorization-based Approach to Photometric Stereo | Type | Journal Article | ||
| Year | 2011 | Publication | International Journal of Imaging Systems and Technology | Abbreviated Journal | IJIST |
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21 | Issue | 1 | Pages | 115-119 |
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| Abstract | This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ JLS2011; ADAS @ adas @ | Serial | 1711 | ||
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