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Author  |
Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 246-256 | ||
| Keywords | Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality | ||||
| Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. | ||||
| Address | Aquila; Italia; June 2019 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSS2019 | Serial | 3270 | ||
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| Author |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez | ||||
| Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 16 | Pages | 4587 |
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| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. | ||||
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| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSM2020 | Serial | 3452 | ||
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| Author |
Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim | ||||
| Title | Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 390-398 | ||
| Keywords | Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers | ||||
| Abstract | This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. | ||||
| Address | New Orleans; USA; June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RMV2022 | Serial | 3774 | ||
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| Author |
Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez | ||||
| Title | Obstacle mapping module for quadrotors on outdoor Search and Rescue operations | Type | Conference Article | ||
| Year | 2013 | Publication | International Micro Air Vehicle Conference and Flight Competition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | UAV | ||||
| Abstract | Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in unknown and unstructured environments. |
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| Address | Toulouse; France; September 2013 | ||||
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| Area | Expedition | Conference | IMAV | ||
| Notes | ADAS; 600.054; 600.057;IAM | Approved | no | ||
| Call Number | Admin @ si @ NSH2013 | Serial | 2371 | ||
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| Author |
Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 75-79 | ||
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| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TEA2015 | Serial | 2653 | ||
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| Author |
Andres Traumann; Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | Accurate 3D Measurement Using Optical Depth Information | Type | Journal Article | ||
| Year | 2015 | Publication | Electronic Letters | Abbreviated Journal | EL |
| Volume | 51 | Issue | 18 | Pages | 1420-1422 |
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| Abstract | A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TAE2015 | Serial | 2647 | ||
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| Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. | ||||
| Address | Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ MDB2020 | Serial | 3334 | ||
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| Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 4022-4032 | ||
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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MDB2021 | Serial | 3491 | ||
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| Author |
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 | |
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| 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. | ||||
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| Notes | DAG; 600.121; 600.129; 601.338 | Approved | no | ||
| Call Number | Admin @ si @ MTD2021 | Serial | 3493 | ||
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| Author |
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 | ||
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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MRG2021a | Serial | 3492 | ||
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| Author |
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 | |
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| 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. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez | |
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| ISSN | ISBN | 978-84-124793-6-2 | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Maf2022 | Serial | 3756 | ||
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| Author |
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 | |
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| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ PAE2017 | Serial | 3007 | ||
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| Author |
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 | |
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| 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. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ GMR2021 | Serial | 3634 | ||
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| Author |
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 | ||
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| 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 | ||||
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| 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 |
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 | ||
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| 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 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.045; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ FFB2013 | Serial | 2295 | ||
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