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| Author | Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas | ||||
| Title | Sparse Radial Sampling LBP for Writer Identification | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 716-720 | ||
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| Abstract | In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ NBL2015 | Serial | 2692 | ||
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| Author | M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
| Title | Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 4169 - 4172 | ||
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| Abstract | This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. | ||||
| Address | Milan; Italy; July 2015 | ||||
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| Area | Expedition | Conference | IGARSS | ||
| Notes | LAMP; 600.079;MILAB | Approved | no | ||
| Call Number | Admin @ si @ CRG2015 | Serial | 2724 | ||
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| Author | Eduardo Tusa; Arash Akbarinia; Raquel Gil Rodriguez; Corina Barbalata | ||||
| Title | Real-Time Face Detection and Tracking Utilising OpenMP and ROS | Type | Conference Article | ||
| Year | 2015 | Publication | 3rd Asia-Pacific Conference on Computer Aided System Engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 179 - 184 | ||
| Keywords | RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP | ||||
| Abstract | The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction. |
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| Address | Quito; Ecuador; July 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | APCASE | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ TAG2015 | Serial | 2659 | ||
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| Author | Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1-8 | ||
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| Abstract | Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA; ISE; 600.063; 600.078;MV | Approved | no | ||
| Call Number | Admin @ si @ EGB2015 | Serial | 2591 | ||
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| Author | Gerard Canal; Cecilio Angulo; Sergio Escalera | ||||
| Title | Gesture based Human Multi-Robot interaction | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Abstract | The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case. A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | CAE2015a | Serial | 2651 | ||
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| Author | Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro | ||||
| Title | Improving Bag of Visual Words Representations with Genetic Programming | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Abstract | The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EME2015 | Serial | 2603 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Alexander Statnikov; Evelyne Viegas | ||||
| Title | Design of the 2015 ChaLearn AutoML Challenge | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Abstract | ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code
submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results. There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is “blind tested” on datasets the participants have never seen before, and Tweakathon phases giving time (' 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real world applications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015a | Serial | 2604 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera | ||||
| Title | The AutoML challenge on codalab | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015b | Serial | 2650 | ||
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| Author | Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez | ||||
| Title | Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Intelligent Vehicles Symposium IV2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 356-361 | ||
| Keywords | Pedestrian Detection | ||||
| Abstract | Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. | ||||
| Address | Seoul; Corea; June 2015 | ||||
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| Area | ACDC | Expedition | Conference | IV | |
| Notes | ADAS; 600.076; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ GVX2015 | Serial | 2625 | ||
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| Author | German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez | ||||
| Title | Vision-based Offline-Online Perception Paradigm for Autonomous Driving | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 231 - 238 | ||
| Keywords | Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation | ||||
| Abstract | Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. | ||||
| Address | Hawaii; January 2015 | ||||
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| Area | ACDC | Expedition | Conference | WACV | |
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ RRG2015 | Serial | 2499 | ||
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| Author | Frederic Sampedro; Sergio Escalera | ||||
| Title | Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
| Volume | 9 | Issue | 3 | Pages | 439 - 446 |
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| Abstract | In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1751-9632 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SaE2015 | Serial | 2551 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund | ||||
| Title | Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 22-29 | ||
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| Abstract | The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology. |
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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 @ BGE2015 | Serial | 2655 | ||
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| Author | Aniol Lidon; Xavier Giro; Marc Bolaños; Petia Radeva; Markus Seidl; Matthias Zeppelzauer | ||||
| Title | UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 MediaEval Retrieving Diverse Images Task | Abbreviated Journal | |
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| Abstract | This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity. | ||||
| Address | Wurzen; Germany; September 2015 | ||||
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| Area | Expedition | Conference | MediaEval | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @LGB2016 | Serial | 2793 | ||
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| Author | G.Thorvaldsen; Joana Maria Pujadas-Mora; T.Andersen ; L.Eikvil; Josep Llados; Alicia Fornes; Anna Cabre | ||||
| Title | A Tale of two Transcriptions | Type | Journal | ||
| Year | 2015 | Publication | Historical Life Course Studies | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 1-19 | |
| Keywords | Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting | ||||
| Abstract | non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. |
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| ISSN | 2352-6343 | ISBN | Medium | ||
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| Notes | DAG; 600.077; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ TPA2015 | Serial | 2582 | ||
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| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris | ||||
| Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | ||
| Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
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| Address | Liverpool; uk; August 2015 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; | Approved | no | ||
| Call Number | Admin @ si @ POW2015 | Serial | 2633 | ||
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