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| Author | Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez | ||||
| Title | CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 18 | Issue | 1 | Pages | 15-30 |
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| Abstract | Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1433-2833 | ISBN | Medium | ||
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| Notes | DAG; ADAS; 600.061; 600.076; 600.077 | Approved | no | ||
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Admin @ si @ HRR2015 | Serial | 2567 | ||
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| Author | Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure | ||||
| Title | Slanted Stixels: A way to represent steep streets | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 127 | Issue | Pages | 1643–1658 | |
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| Abstract | This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. | ||||
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| Notes | ADAS; 600.118; 600.124 | Approved | no | ||
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Admin @ si @ HSC2019 | Serial | 3304 | ||
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| Author | Mario Hernandez; Joao Sanchez; Jordi Vitria | ||||
| Title | Selected papers from Iberian Conference on Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume | 45 | Issue | 9 | Pages | 3047-3582 |
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | OR;MV | Approved | no | ||
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Admin @ si @ HSV2012 | Serial | 2069 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
| Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal | |
| Volume | 709 | Issue | Pages | ||
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| Area | Expedition | Conference | RTIP2R | ||
| Notes | DAG | Approved | no | ||
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Admin @ si @ HTR2016 | Serial | 2956 | ||
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| Author | Xu Hu | ||||
| Title | Real-Time Part Based Models for Object Detection | Type | Report | ||
| Year | 2012 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 171 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | ADAS;ISE | Approved | no | ||
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Admin @ si @ Hu2012 | Serial | 2415 | ||
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| Author | Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez | ||||
| Title | Combining structural and statistical strategies for unsupervised wall detection in floor plans | Type | Conference Article | ||
| Year | 2013 | Publication | 10th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated data
to learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing the process in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on 4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach. |
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| Address | Bethlehem; PA; USA; August 2013 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.045 | Approved | no | ||
| Call Number |
Admin @ si @ HVS2013a | Serial | 2321 | ||
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| Author | Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez | ||||
| Title | Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies | Type | Conference Article | ||
| Year | 2013 | Publication | 10th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Address | Bethlehem; PA; USA; August 2013 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
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Admin @ si @ HVS2013b | Serial | 2696 | ||
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| Author | Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez | ||||
| Title | Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 109-121 | |
| Keywords | Graphics recognition; Floor plan analysis; Object segmentation | ||||
| Abstract | In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions. | ||||
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| 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 | 978-3-662-44853-3 | Medium | |
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| Notes | DAG; ADAS; 600.076; 600.077 | Approved | no | ||
| Call Number |
Admin @ si @ HVS2014 | Serial | 2535 | ||
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| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil | ||||
| Title | Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals | Type | Journal Article | ||
| Year | 2022 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
| Volume | 12 | Issue | 5 | Pages | 2298 |
| Keywords | Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion | ||||
| Abstract | The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. | ||||
| Address | February 2022 | ||||
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| Notes | IAM; ADAS; 600.139; 600.145; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ HYF2022 | Serial | 3720 | ||
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| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil | ||||
| Title | EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment | Type | Journal Article | ||
| Year | 2024 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 24 | Issue | 4 | Pages | 1174 |
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| Abstract | High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. | ||||
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| Notes | IAM | Approved | no | ||
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Admin @ si @ HYF2024 | Serial | 4019 | ||
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| Author | Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera | ||||
| Title | Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization | Type | Journal Article | ||
| Year | 2012 | Publication | Journal of Ambient Intelligence and Smart Environments | Abbreviated Journal | JAISE |
| Volume | 4 | Issue | 6 | Pages | 535-546 |
| Keywords | Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation | ||||
| Abstract | We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. | ||||
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| ISSN | 1876-1364 | ISBN | Medium | ||
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| Notes | MILAB;HuPBA | Approved | no | ||
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Admin @ si @ HZM2012a | Serial | 2006 | ||
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| Author | Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera | ||||
| Title | Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 726-732 | ||
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| Abstract | We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. | ||||
| Address | Portland; Oregon; June 2013 | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | MILAB;HuPBA | Approved | no | ||
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Admin @ si @ HZM2012b | Serial | 2046 | ||
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| Author | Mohamed Ramzy Ibrahim; Robert Benavente; Felipe Lumbreras; Daniel Ponsa | ||||
| Title | 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition | ||||
| Abstract | The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively. | ||||
| Address | New Orleans, USA; 19 June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130 | Approved | no | ||
| Call Number |
Admin @ si @ IBL2022 | Serial | 3693 | ||
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| Author | Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras | ||||
| Title | Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification | Type | Conference Article | ||
| Year | 2023 | Publication | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications | Abbreviated Journal | |
| Volume | 14469 | Issue | Pages | 214–228 | |
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| Abstract | Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CIARP | ||
| Notes | MSIAU | Approved | no | ||
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Admin @ si @ IBP2023 | Serial | 4008 | ||
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| Author | Emanuel Indermühle; Volkmar Frinken; Horst Bunke | ||||
| Title | Mode Detection in Online Handwritten Documents using BLSTM Neural Networks | Type | Conference Article | ||
| Year | 2012 | Publication | 13th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 302-307 | ||
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| Abstract | Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data. | ||||
| Address | Bari, italy | ||||
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| ISSN | ISBN | 978-1-4673-2262-1 | Medium | ||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ IFB2012 | Serial | 2056 | ||
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