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| Author | Ali Furkan Biten; Ruben Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | ICDAR 2019 Competition on Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 | Abbreviated Journal | |
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| Abstract | This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | CLVL | ||
| Notes | DAG; 600.129; 601.338; 600.135; 600.121 | Approved | no | ||
Call Number  |
Admin @ si @ BTM2019a | Serial | 3284 | ||
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| Author | Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | OCR-IDL: OCR Annotations for Industry Document Library Dataset | Type | Conference Article | ||
| Year | 2022 | Publication | ECCV Workshop on Text in Everything | Abbreviated Journal | |
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| Abstract | Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes | DAG; no proj | Approved | no | ||
| Call Number |
Admin @ si @ BTG2022 | Serial | 3817 | ||
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| Author | Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras | ||||
| Title | Intrinsic Image Evaluation On Synthetic Complex Scenes | Type | Conference Article | ||
| Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 285 - 289 | ||
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| Abstract | Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes. |
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| Address | Melbourne; Australia; September 2013 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | CIC; 600.048; 600.052; 600.051 | Approved | no | ||
| Call Number |
Admin @ si @ BSW2013 | Serial | 2264 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames | Type | Conference Article | ||
| Year | 2013 | Publication | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society | Abbreviated Journal | |
| Volume | Issue | Pages | 7350 - 7354 | ||
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| Abstract | In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results. | ||||
| Address | Osaka; Japan; July 2013 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1557-170X | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | EMBC | |
| Notes | MV; 600.047; 600.060;SIAI | Approved | no | ||
| Call Number |
Admin @ si @ BSV2013 | Serial | 2286 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Towards Automatic Polyp Detection with a Polyp Appearance Model | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 9 | Pages | 3166-3182 |
| Keywords | Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot | ||||
| Abstract | This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes | MV;SIAI | Approved | no | ||
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Admin @ si @ BSV2012; IAM @ iam | Serial | 1997 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions | Type | Conference Article | ||
| Year | 2010 | Publication | 28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica | Abbreviated Journal | |
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| Abstract | One of the first usual steps in pattern recognition schemas is image segmentation, in order to reduce the dimensionality of the problem and manage smaller quantity of data. In our case as we are pursuing real-time colon cancer polyp detection, this step is crucial. In this paper we present a non-informative region estimation algorithm that will let us discard some parts of the image where we will not expect to find colon cancer polyps. The performance of our approach will be measured in terms of both non-informative areas elimination and polyps’ areas preserving. The results obtained show the importance of having correct non- informative region estimation in order to fasten the whole recognition process. | ||||
| Address | Madrid (Spain) | ||||
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| Area | 800 | Expedition | Conference | CASEIB | |
| Notes | MV;SIAI | Approved | no | ||
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Admin @ si @ BSV2010 | Serial | 1469 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists | Type | Book Chapter | ||
| Year | 2015 | Publication | Colonoscopy and Colorectal Cancer | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description | ||||
| Abstract | Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly. | ||||
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| ISSN | ISBN | 978-953-51-2225-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MV | Approved | no | ||
| Call Number |
Admin @ si @ BSR2015 | Serial | 2624 | ||
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| Author | Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados | ||||
| Title | Classification of aesthetic natural scene images using statistical and semantic features | Type | Journal Article | ||
| Year | 2023 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 82 | Issue | 9 | Pages | 13507-13532 |
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| Abstract | Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. | ||||
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| Notes | DAG | Approved | no | ||
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Admin @ si @ BSP2023 | Serial | 3873 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño | ||||
| Title | WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians | Type | Journal Article | ||
| Year | 2015 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 43 | Issue | Pages | 99-111 | |
| Keywords | Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection | ||||
| Abstract | We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0895-6111 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MV; IAM; 600.047; 600.060; 600.075;SIAI | Approved | no | ||
| Call Number |
Admin @ si @ BSF2015 | Serial | 2609 | ||
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| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation | Type | Journal Article | ||
| Year | 2023 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
| Volume | 228 | Issue | Pages | 107241 | |
| Keywords | Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation | ||||
| Abstract | Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | IAM; | Approved | no | ||
| Call Number |
Admin @ si @ BSC2023 | Serial | 3702 | ||
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| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | A benchmark for the evaluation of computational methods for bronchoscopic navigation | Type | Journal Article | ||
| Year | 2022 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCARS |
| Volume | 17 | Issue | 1 | Pages | |
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| Notes | IAM | Approved | no | ||
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Admin @ si @ BSC2022 | Serial | 3832 | ||
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| Author | E. Bondi ; L. Sidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Real-time people counting from depth imagery of crowded environments | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IEEE International Conference on Advanced Video and Signal based Surveillance | Abbreviated Journal | |
| Volume | Issue | Pages | 337 - 342 | ||
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| Abstract | In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications. | ||||
| Address | Seoul; Korea; August 2014 | ||||
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| Area | Expedition | Conference | AVSS | ||
| Notes | LAMP; 600.079 | Approved | no | ||
| Call Number |
Admin @ si @ BSB2014 | Serial | 2540 | ||
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| Author | Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados | ||||
| Title | TWD: A New Deep E2E Model for Text Watermark Detection in Video Images | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection | ||||
| Abstract | Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge | ||||
| Address | Montreal; Quebec; Canada; August 2022 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; | Approved | no | ||
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Admin @ si @ BSA2022 | Serial | 3788 | ||
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| Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou | ||||
| Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 23 | Issue | 1 | Pages | 83-95 |
| Keywords | color constancy; CRF; multi-illuminant | ||||
| Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
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Admin @ si @ BRW2014 | Serial | 2451 | ||
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| Author | Francesco Brughi | ||||
| Title | Artistic Heritage Motive Retrieval: an Explorative Study | Type | Report | ||
| Year | 2013 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 176 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | IAM | Approved | no | ||
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Admin @ si @ Bru2013 | Serial | 2410 | ||
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