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| Author | Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone | ||||
| Title | Towards Modelling an Attention-Based Text Localization Process | Type | Conference Article | ||
| Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 7887 | Issue | Pages | 296-303 | |
Keywords  |
text localization; visual attention; eye guidance | ||||
| Abstract | This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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| Address | Madeira; Portugal; June 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-38627-5 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ CKL2013 | Serial | 2291 | ||
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| Author | David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich | ||||
| Title | Traffic sign recognition for computer vision project-based learning | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Education | Abbreviated Journal | T-EDUC |
| Volume | 56 | Issue | 3 | Pages | 364-371 |
| Keywords |
traffic signs | ||||
| Abstract | This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0018-9359 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; CIC | Approved | no | ||
| Call Number | Admin @ si @ GSL2013; ADAS @ adas @ | Serial | 2160 | ||
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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 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IMAV | ||
| Notes | ADAS; 600.054; 600.057;IAM | Approved | no | ||
| Call Number | Admin @ si @ NSH2013 | Serial | 2371 | ||
| Permanent link to this record | |||||
| Author | Ferran Diego; Joan Serrat; Antonio Lopez | ||||
| Title | Joint spatio-temporal alignment of sequences | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 15 | Issue | 6 | Pages | 1377-1387 |
| Keywords |
video alignment | ||||
| Abstract | Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1520-9210 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ DSL2013; ADAS @ adas @ | Serial | 2228 | ||
| Permanent link to this record | |||||
| Author | Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez | ||||
| Title | Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance | Type | Conference Article | ||
| Year | 2013 | Publication | Proceedings of the International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 153--161 | |
| Keywords |
Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model | ||||
| Abstract | Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability | ||||
| Address | Barcelona; February 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | SciTePress | Place of Publication | Portugal | Editor | Sebastiano Battiato and José Braz |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-989-8565-47-1 | Medium | ||
| Area | 800 | Expedition | Conference | VISAPP | |
| Notes | IAM;MV; 600.044; 600.047; 600.060; 605.203 | Approved | no | ||
| Call Number | IAM @ iam @ SGR2013 | Serial | 2123 | ||
| Permanent link to this record | |||||
| Author | Albert Gordo; Florent Perronnin; Ernest Valveny | ||||
| Title | Large-scale document image retrieval and classification with runlength histograms and binary embeddings | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 46 | Issue | 7 | Pages | 1898-1905 |
| Keywords |
visual document descriptor; compression; large-scale; retrieval; classification | ||||
| Abstract | We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be computed efficiently. We show how this descriptor can achieve state-of-theart results on two very different public datasets in classification and retrieval tasks. Moreover, we show how we can compress and binarize these descriptors to make them suitable for large-scale applications. We can achieve state-ofthe- art results in classification using binary descriptors of as few as 16 to 64 bits. |
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| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.042; 600.045; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ GPV2013 | Serial | 2306 | ||
| Permanent link to this record | |||||
| Author | Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu | ||||
| Title | Facial expression recognition using tracked facial actions: Classifier performance analysis | Type | Journal Article | ||
| Year | 2013 | Publication | Engineering Applications of Artificial Intelligence | Abbreviated Journal | EAAI |
| Volume | 26 | Issue | 1 | Pages | 467-477 |
| Keywords |
Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction | ||||
| Abstract | In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%. | ||||
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| Corporate Author | Thesis | ||||
| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | OR; 600.046;MV | Approved | no | ||
| Call Number | Admin @ si @ DMR2013 | Serial | 2185 | ||
| Permanent link to this record | |||||