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Author | Antonio Esteban Lansaque | ||||
Title | 3D reconstruction and recognition using structured ligth | Type | Report | ||
Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 179 | Issue | Pages | ||
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Abstract | This work covers the problem of 3D reconstruction, recognition and 6DOF pose estimation. The goal of this project is to reconstruct a 3D scene and to align an object model of the industrial pieces onto the reconstructed scene. The reconstruction algorithm is based on stereo techniques and the recognition algorithm is based on SHOT descriptors computed on a set of uniform keypoints. Correspondences are used to estimate a first 6DOF transformation that maps the model onto the scene and then ICP algorithm is used to refine the transformation. In order to check the effectiveness of the proposed algorithm, several experiments were performed. These experiments were conducted on a lab environment in order to get results under the same conditions in all of them. Although obtained results are not real time results, the proposed algorithm ends up with high rates of object recognition. | ||||
Address | UAB; September 2014 | ||||
Corporate Author | Thesis | Master's thesis | |||
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Notes | IAM; 600.075 | Approved | no | ||
Call Number | Admin @ si @ Est2014 | Serial | 2578 | ||
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Author | Ricard Balague | ||||
Title | Exploring the combination of color cues for intrinsic image decomposition | Type | Report | ||
Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 178 | Issue | Pages | ||
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Abstract | Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. | ||||
Address | UAB; September 2014 | ||||
Corporate Author | Thesis | Master's thesis | |||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Notes | CIC; 600.074 | Approved | no | ||
Call Number | Admin @ si @ Bal2014 | Serial | 2579 | ||
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Author | Sebastian Ramos | ||||
Title | Vision-based Detection of Road Hazards for Autonomous Driving | Type | Report | ||
Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
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Address | UAB; September 2014 | ||||
Corporate Author | Thesis | Master's thesis | |||
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Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Ram2014 | Serial | 2580 | ||
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Author | Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio | ||||
Title | A computational framework for cancer response assessment based on oncological PET-CT scans | Type | Journal Article | ||
Year | 2014 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 55 | Issue | Pages | 92–99 | |
Keywords | Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis | ||||
Abstract | In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SED2014 | Serial | 2606 | ||
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Author | R. Clariso; David Masip; A. Rius | ||||
Title | Student projects empowering mobile learning in higher education | Type | Journal | ||
Year | 2014 | Publication | Revista de Universidad y Sociedad del Conocimiento | Abbreviated Journal | RUSC |
Volume | 11 | Issue | Pages | 192-207 | |
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Series Volume | Series Issue | Edition | |||
ISSN | 1698-580X | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ CMR2014 | Serial | 2619 | ||
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Author | Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados | ||||
Title | Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents | Type | Book Chapter | ||
Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 8746 | Issue | Pages | 25-37 | |
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Abstract | Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Bart Lamiroy; Jean-Marc Ogier | |
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 | |
Area | Expedition | Conference | |||
Notes | DAG; 600.045; 600.056; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ BDJ2014 | Serial | 2699 | ||
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Author | Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
Title | Spotting Graphical Symbols in Camera-Acquired Documents in Real Time | Type | Book Chapter | ||
Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 8746 | Issue | Pages | 3-10 | |
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Abstract | In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Bart Lamiroy; Jean-Marc Ogier | |
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 | |
Area | Expedition | Conference | |||
Notes | DAG; 600.045; 600.055; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RKL2014 | Serial | 2700 | ||
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Author | Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados | ||||
Title | Classification of Administrative Document Images by Logo Identification | Type | Book Chapter | ||
Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 8746 | Issue | Pages | 49-58 | |
Keywords | Administrative Document Classification; Logo Recognition; Logo Spotting | ||||
Abstract | This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Bart Lamiroy; Jean-Marc Ogier | |
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
Area | Expedition | Conference | |||
Notes | DAG; 600.056; 600.045; 605.203; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RPK2014 | Serial | 2701 | ||
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Author | Ariel Amato | ||||
Title | Moving cast shadow detection | Type | Journal Article | ||
Year | 2014 | Publication | Electronic letters on computer vision and image analysis | Abbreviated Journal | ELCVIA |
Volume | 13 | Issue | 2 | Pages | 70-71 |
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Abstract | Motion perception is an amazing innate ability of the creatures on the planet. This adroitness entails a functional advantage that enables species to compete better in the wild. The motion perception ability is usually employed at different levels, allowing from the simplest interaction with the ’physis’ up to the most transcendental survival tasks. Among the five classical perception system , vision is the most widely used in the motion perception field. Millions years of evolution have led to a highly specialized visual system in humans, which is characterized by a tremendous accuracy as well as an extraordinary robustness. Although humans and an immense diversity of species can distinguish moving object with a seeming simplicity, it has proven to be a difficult and non trivial problem from a computational perspective. In the field of Computer Vision, the detection of moving objects is a challenging and fundamental research area. This can be referred to as the ’origin’ of vast and numerous vision-based research sub-areas. Nevertheless, from the bottom to the top of this hierarchical analysis, the foundations still relies on when and where motion has occurred in an image. Pixels corresponding to moving objects in image sequences can be identified by measuring changes in their values. However, a pixel’s value (representing a combination of color and brightness) could also vary due to other factors such as: variation in scene illumination, camera noise and nonlinear sensor responses among others. The challenge lies in detecting if the changes in pixels’ value are caused by a genuine object movement or not. An additional challenging aspect in motion detection is represented by moving cast shadows. The paradox arises because a moving object and its cast shadow share similar motion patterns. However, a moving cast shadow is not a moving object. In fact, a shadow represents a photometric illumination effect caused by the relative position of the object with respect to the light sources. Shadow detection methods are mainly divided in two domains depending on the application field. One normally consists of static images where shadows are casted by static objects, whereas the second one is referred to image sequences where shadows are casted by moving objects. For the first case, shadows can provide additional geometric and semantic cues about shape and position of its casting object as well as the localization of the light source. Although the previous information can be extracted from static images as well as video sequences, the main focus in the second area is usually change detection, scene matching or surveillance. In this context, a shadow can severely affect with the analysis and interpretation of the scene. The work done in the thesis is focused on the second case, thus it addresses the problem of detection and removal of moving cast shadows in video sequences in order to enhance the detection of moving object. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Ama2014 | Serial | 2870 | ||
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Author | L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink | ||||
Title | A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting | Type | Journal | ||
Year | 2014 | Publication | Manuscript Cultures | Abbreviated Journal | |
Volume | 7 | Issue | Pages | 47-58 | |
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Abstract | With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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Notes | DAG; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3190 | ||
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Author | Mohammad Rouhani; E. Boyer; Angel Sappa | ||||
Title | Non-Rigid Registration meets Surface Reconstruction | Type | Conference Article | ||
Year | 2014 | Publication | International Conference on 3D Vision | Abbreviated Journal | |
Volume | Issue | Pages | 617-624 | ||
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Abstract | Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers. | ||||
Address | Tokyo; Japan; December 2014 | ||||
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Area | Expedition | Conference | 3DV | ||
Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ RBS2014 | Serial | 2534 | ||
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Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
Title | Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics | Type | Conference Article | ||
Year | 2014 | Publication | 1st Workshop on Computer Vision for Affective Computing | Abbreviated Journal | |
Volume | Issue | Pages | 1-8 | ||
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Abstract | Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression. |
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Address | Singapore; November 2014 | ||||
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Area | Expedition | Conference | ACCV | ||
Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ RBD2014 | Serial | 2599 | ||
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Author | Oualid M. Benkarim; Petia Radeva; Laura Igual | ||||
Title | Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation | Type | Conference Article | ||
Year | 2014 | Publication | 8th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
Volume | 8563 | Issue | Pages | 138-147 | |
Keywords | MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classication | ||||
Abstract | The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset. The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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Address | Palma de Mallorca; July 2014 | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-319-08848-8 | Medium | |
Area | Expedition | Conference | AMDO | ||
Notes | MILAB; OR | Approved | no | ||
Call Number | Admin @ si @ BRI2014 | Serial | 2494 | ||
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Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
Title | Video Segmentation of Life-Logging Videos | Type | Conference Article | ||
Year | 2014 | Publication | 8th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
Volume | 8563 | Issue | Pages | 1-9 | |
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Area | Expedition | Conference | AMDO | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BGR2014 | Serial | 2558 | ||
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Author | E. Bondi ; L. Sidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Real-time people counting from depth imagery of crowded environments | Type | Conference Article | ||
Year | 2014 | Publication | 11th IEEE International Conference on Advanced Video and Signal based Surveillance | Abbreviated Journal | |
Volume | Issue | Pages | 337 - 342 | ||
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Abstract | In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications. | ||||
Address | Seoul; Korea; August 2014 | ||||
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Area | Expedition | Conference | AVSS | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BSB2014 | Serial | 2540 | ||
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