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Author  |
Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin | ||||
| Title | Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes | Type | Book Chapter | ||
| Year | 2011 | Publication | Innovations in Intelligent Image Analysis | Abbreviated Journal | |
| Volume | 339 | Issue | Pages | 7-29 | |
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| Abstract | A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Berlin | Editor | H. Kawasnicka; L.Jain |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-17933-4 | Medium | |
| Area | Expedition | Conference | |||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ETP2011 | Serial | 1746 | ||
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| Author |
Sergio Escalera | ||||
| Title | Fast traffic model matching and recognition on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #84 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2005 | Serial | 572 | ||
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| Author |
Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-Class Pattern and Object Recognition | Type | Miscellaneous | ||
| Year | 2008 | Publication | Abbreviated Journal | ||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2008a | Serial | 1106 | ||
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| Author |
Sergio Escalera | ||||
| Title | Multi-Modal Human Behaviour Analysis from Visual Data Sources | Type | Journal | ||
| Year | 2013 | Publication | ERCIM News journal | Abbreviated Journal | ERCIM |
| Volume | 95 | Issue | Pages | 21-22 | |
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| Abstract | The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0926-4981 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ Esc2013 | Serial | 2361 | ||
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| Author |
Sergio Escalera | ||||
| Title | Human Behavior Analysis From Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 7th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 7378 | Issue | Pages | 282-292 | |
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| Abstract | Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. | ||||
| Address | Mallorca | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Heidelberg | Place of Publication | Editor | F.J. Perales; R.B. Fisher; T.B. Moeslund | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-31566-4 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2012 | Serial | 2040 | ||
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| Author |
Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A | Type | Book Whole | ||
| Year | 2008 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2008b | Serial | 2217 | ||
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| Author |
Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
| Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
| Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
| Volume | 1957 | Issue | Pages | 13-28 | |
| Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
| Abstract | XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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| Address | Barcelona; June 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AED2017 | Serial | 3065 | ||
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| Author |
Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas | ||||
| Title | Read While You Drive-Multilingual Text Tracking on the Road | Type | Conference Article | ||
| Year | 2022 | Publication | 15th IAPR International workshop on document analysis systems | Abbreviated Journal | |
| Volume | 13237 | Issue | Pages | 756–770 | |
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| Abstract | Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. | ||||
| Address | La Rochelle; France; May 2022 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-031-06554-5 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.155; 611.022; 611.004 | Approved | no | ||
| Call Number | Admin @ si @ GTR2022 | Serial | 3783 | ||
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| Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
| Title | Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14192 | Issue | Pages | 106-121 | |
| Keywords | Scene Text Detection; Scene Text Recognition; Transformer Acceleration | ||||
| Abstract | Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GKR2023a | Serial | 3907 | ||
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| Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
| Title | STEP – Towards Structured Scene-Text Spotting | Type | Conference Article | ||
| Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 883-892 | ||
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| Abstract | We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios. | ||||
| Address | Waikoloa; Hawai; USA; January 2024 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GKR2024 | Serial | 3992 | ||
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| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas | ||||
| Title | Out-of-Vocabulary Challenge Report | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 13804 | Issue | Pages | 359–375 | |
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| Abstract | This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. | ||||
| Address | Tel-Aviv; Israel; October 2022 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 600.155; 302.105; 611.002 | Approved | no | ||
| Call Number | Admin @ si @ GMB2022 | Serial | 3771 | ||
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| Author |
Senmao Li; Joost Van de Weijer; Yaxing Wang; Fahad Shahbaz Khan; Meiqin Liu; Jian Yang | ||||
| Title | 3D-Aware Multi-Class Image-to-Image Translation with NeRFs | Type | Conference Article | ||
| Year | 2023 | Publication | 36th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 12652-12662 | ||
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| Abstract | Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware 121 translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In exten-sive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware 121 translation with multi-view consistency. Code is available in 3DI2I. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LWW2023b | Serial | 3920 | ||
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| Author |
Senmao Li; Joost van de Weijer; Taihang Hu; Fahad Shahbaz Khan; Qibin Hou; Yaxing Wang; Jian Yang | ||||
| Title | StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions, and unexpected changes in nonselected regions. (2) They require careful text prompt editing where the prompt should include all visual objects in the input image. To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers, is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique which is used for the unconditional branch of classifier-free guidance, as well as the conditional one as used by P2P. Extensive experimental prompt-editing results on a variety of images, demonstrate qualitatively and quantitatively that our method has superior editing capabilities than existing and concurrent works. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LWH2023 | Serial | 3870 | ||
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| Author |
Sebastien Mace; Herve Locteau; Ernest Valveny; Salvatore Tabbone | ||||
| Title | A system to detect rooms in architectural floor plan images | Type | Conference Article | ||
| Year | 2010 | Publication | 9th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 167–174 | ||
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| Abstract | In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results. | ||||
| Address | Boston; USA | ||||
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| ISSN | ISBN | 978-1-60558-773-8 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MLV2010 | Serial | 1437 | ||
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| Author |
Sebastian Ramos | ||||
| Title | Vision-based Detection of Road Hazards for Autonomous Driving | Type | Report | ||
| Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
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| Address | UAB; September 2014 | ||||
| Corporate Author | Thesis | Master's thesis | |||
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| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ Ram2014 | Serial | 2580 | ||
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