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Author (up) Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund
Title Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Type Miscellaneous
Year 2013 Publication ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; October 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ DDB2013 Serial 2372
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Author (up) Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund
Title Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams Type Journal Article
Year 2016 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 75 Issue 22 Pages 14985-14990
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE; HUPBA Approved no
Call Number Admin @ si @ DDB2016 Serial 2934
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Author (up) Anders Hast; Alicia Fornes
Title A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 150-155
Keywords
Abstract The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.
Address Santorini; Greece; April 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG; 602.006; 600.061; 600.077; 600.097 Approved no
Call Number HaF2016 Serial 2753
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Author (up) Anders Skaarup Johansen; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund
Title Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images Type Journal Article
Year 2023 Publication Applied Sciences Abbreviated Journal AS
Volume 13 Issue 18 Pages
Keywords thermal; object detection; concept drift; conditioning; weather recognition
Abstract Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ SNE2023 Serial 3983
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Author (up) Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari
Title A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition Type Journal Article
Year 2019 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 78 Issue 18 Pages 25259–25271
Keywords Fully convolutional networks; FusionNet; Thermal imaging; Face recognition
Abstract This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ LNE2019 Serial 3318
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Author (up) Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai
Title Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks Type Conference Article
Year 2022 Publication 17th European Conference on Computer Vision Workshops Abbreviated Journal
Volume 13804 Issue Pages 329–344
Keywords
Abstract Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-25068-2 Medium
Area Expedition Conference ECCV-TiE
Notes DAG; 600.162; 600.140; 110.312 Approved no
Call Number Admin @ si @ GBC2022 Serial 3795
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Author (up) Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke
Title A Fast Matching Algorithm for Graph-Based Handwriting Recognition Type Conference Article
Year 2013 Publication 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition Abbreviated Journal
Volume 7877 Issue Pages 194-203
Keywords
Abstract The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
Address Vienna; Austria; May 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-38220-8 Medium
Area Expedition Conference GBR
Notes DAG; 600.045; 605.203 Approved no
Call Number Admin @ si @ FSF2013 Serial 2294
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Author (up) Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke
Title Transcription Alignment of Latin Manuscripts Using Hidden Markov Models Type Conference Article
Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal
Volume Issue Pages 29-36
Keywords
Abstract Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
Address
Corporate Author Thesis
Publisher ACM Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference HIP
Notes DAG Approved no
Call Number Admin @ si @ FFF2011b Serial 1824
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Author (up) Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen
Title Improving HMM-Based Keyword Spotting with Character Language Models Type Conference Article
Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 506-510
Keywords
Abstract Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance.
Address Washington; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-5363 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.045; 605.203 Approved no
Call Number Admin @ si @ FFB2013 Serial 2295
Permanent link to this record
 
 
Author (up) Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera
Title Tri-modal Person Re-identification with RGB, Depth and Thermal Features Type Conference Article
Year 2013 Publication 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 301-307
Keywords
Abstract Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios.
Address Portland; oregon; June 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-7695-4990-3 Medium
Area Expedition Conference CVPRW
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ MBM2013 Serial 2253
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Author (up) Andreea Glavan; Alina Matei; Petia Radeva; Estefania Talavera
Title Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams Type Journal Article
Year 2021 Publication Expert Systems with Applications Abbreviated Journal ESWA
Volume 171 Issue Pages 114506
Keywords
Abstract Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ GMR2021 Serial 3634
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Author (up) Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai
Title WordFences: Text Localization and Recognition Type Conference Article
Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Beijing; China; September 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICIP
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ PAE2017 Serial 3007
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Author (up) Andres Mafla
Title Leveraging Scene Text Information for Image Interpretation Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Until recently, most computer vision models remained illiterate, largely ignoring the semantically rich and explicit information contained in scene text. Recent progress in scene text detection and recognition has recently allowed exploring its role in a diverse set of open computer vision problems, e.g. image classification, image-text retrieval, image captioning, and visual question answering to name a few. The explicit semantics of scene text closely requires specific modeling similar to language. However, scene text is a particular signal that has to be interpreted according to a comprehensive perspective that encapsulates all the visual cues in an image. Incorporating this information is a straightforward task for humans, but if we are unfamiliar with a language or scripture, achieving a complete world understanding is impossible (e.a. visiting a foreign country with a different alphabet). Despite the importance of scene text, modeling it requires considering the several ways in which scene text interacts with an image, processing and fusing an additional modality. In this thesis, we mainly focus
on two tasks, scene text-based fine-grained image classification, and cross-modal retrieval. In both studied tasks we identify existing limitations in current approaches and propose plausible solutions. Concretely, in each chapter: i) We define a compact way to embed scene text that generalizes to unseen words at training time while performing in real-time. ii) We incorporate the previously learned scene text embedding to create an image-level descriptor that overcomes optical character recognition (OCR) errors which is well-suited to the fine-grained image classification task. iii) We design a region-level reasoning network that learns the interaction through semantics among salient visual regions and scene text instances. iv) We employ scene text information in image-text matching and introduce the Scene Text Aware Cross-Modal retrieval StacMR task. We gather a dataset that incorporates scene text and design a model suited for the newly studied modality. v) We identify the drawbacks of current retrieval metrics in cross-modal retrieval. An image captioning metric is proposed as a way of better evaluating semantics in retrieved results. Ample experimentation shows that incorporating such semantics into a model yields better semantic results while
requiring significantly less data to converge.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Dimosthenis Karatzas;Lluis Gomez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-6-2 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ Maf2022 Serial 3756
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Author (up) Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas
Title StacMR: Scene-Text Aware Cross-Modal Retrieval Type Conference Article
Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 2219-2229
Keywords
Abstract
Address Virtual; January 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference WACV
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ MRG2021a Serial 3492
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Author (up) Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
Title Real-time Lexicon-free Scene Text Retrieval Type Journal Article
Year 2021 Publication Pattern Recognition Abbreviated Journal PR
Volume 110 Issue Pages 107656
Keywords
Abstract In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.129; 601.338 Approved no
Call Number Admin @ si @ MTD2021 Serial 3493
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