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Author  |
Nuria Cirera; Alicia Fornes; Josep Llados | ||||
| Title | Hidden Markov model topology optimization for handwriting recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 626-630 | ||
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| Abstract | In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
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| Author |
Nuria Cirera | ||||
| Title | Recognition of Handwritten Historical Documents | Type | Report | ||
| Year | 2012 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 174 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Cir2012 | Serial | 2416 | ||
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| Author |
Noha Elfiky; Theo Gevers; Arjan Gijsenij; Jordi Gonzalez | ||||
| Title | Color Constancy using 3D Scene Geometry derived from a Single Image | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 23 | Issue | 9 | Pages | 3855-3868 |
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| Abstract | The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g. grey-world and white patch assumption).
In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models; images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods is selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared to the best-performing single color constancy algorithm. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ EGG2014 | Serial | 2528 | ||
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| Author |
Noha Elfiky; Jordi Gonzalez; Xavier Roca | ||||
| Title | Compact and Adaptive Spatial Pyramids for Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 30 | Issue | 8 | Pages | 492–500 |
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| Abstract | Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ EGR2012 | Serial | 2004 | ||
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| Author |
Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
| Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 4 | Pages | 1627-1636 |
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| Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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| Author |
Noha Elfiky | ||||
| Title | Enhancing Local Binary Patterns with Spatial Pyramid Kernel: Application to Scene Classification | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 129 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Elf2009 | Serial | 2388 | ||
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| Author |
Noha Elfiky | ||||
| Title | Compact, Adaptive and Discriminative Spatial Pyramids for Improved Object and Scene Classification | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these large-scale datasets efficiently. Nowadays the Bag-of-Words (BoW) is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation, which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation, still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Elf2012 | Serial | 2202 | ||
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| Author |
Nil Ballus; Bhalaji Nagarajan; Petia Radeva | ||||
| Title | Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition | Type | Conference Article | ||
| Year | 2022 | Publication | 10th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 13256 | Issue | Pages | ||
| Keywords | Self-supervised; Contrastive learning; Food recognition | ||||
| Abstract | Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. | ||||
| Address | Aveiro; Portugal; May 2022 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BNR2022 | Serial | 3782 | ||
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| Author |
Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis | ||||
| Title | Human Motion Tracking and Recognition | Type | Book Chapter | ||
| Year | 2005 | Publication | Encyclopedia of Information Science and Technology, 1(5):1355–1360 | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | ADAS @ adas @ ASG2005 | Serial | 496 | ||
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| Author |
Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis | ||||
| Title | Advances in Tracking and Recognition of Human Motion | Type | Book Chapter | ||
| Year | 2009 | Publication | Encyclopedia of Information Science and Technology | Abbreviated Journal | |
| Volume | I | Issue | 2nd edition | Pages | 65–71 |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ ASG2009 | Serial | 1143 | ||
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| Author |
Nicola Bellotto; Eric Sommerlade; Ben Benfold; Charles Bibby; I. Reid; Daniel Roth; Luc Van Gool; Carles Fernandez; Jordi Gonzalez | ||||
| Title | A Distributed Camera System for Multi-Resolution Surveillance | Type | Conference Article | ||
| Year | 2009 | Publication | 3rd ACM/IEEE International Conference on Distributed Smart Cameras | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | 10.1109/ICDSC.2009.5289413 | ||||
| Abstract | We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance. | ||||
| Address | Como, Italy | ||||
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| Area | Expedition | Conference | ICDSC | ||
| Notes | Approved | no | |||
| Call Number | ISE @ ise @ BSB2009 | Serial | 1205 | ||
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| Author |
Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier | ||||
| Title | ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1582-1587 | ||
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| Abstract | With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. | ||||
| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ NPB2019 | Serial | 3341 | ||
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| Author |
Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Xavier Carrillo; R. Hemetsberger; Petia Radeva; J. Mauri; A. Bayes | ||||
| Title | Canvis de volum a la arteria radial despres de la administracio de dos tractaments vasodilatadors. Avaluacio mitjançant ecografia intravascular | Type | Conference Article | ||
| Year | 2010 | Publication | 22nd Congres Societat Catalana de Cardiologia, | Abbreviated Journal | |
| Volume | Issue | Pages | 179 | ||
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| Address | Barcelona (Spain) | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SFC2010a | Serial | 1367 | ||
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| Author |
Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; H. Tizon; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | Radial Artery Volume Changes After Administration Of Two Different Intra-arterial Drug Regimens. Assessment by Intravascular Ultrasound | Type | Journal Article | ||
| Year | 2010 | Publication | Journal of the American College of Cardiology | Abbreviated Journal | JACC |
| Volume | 56 | Issue | 13s1 | Pages | B119 |
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SFC2010b | Serial | 1364 | ||
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| Author |
Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2150-2158 | ||
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| Abstract | State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. | ||||
| Address | Virtual; October 2021 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MFN2021 | Serial | 3649 | ||
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