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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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| 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 | Sergio Escalera; Xavier Baro; Jordi Gonzalez; Miguel Angel Bautista; Meysam Madadi; Miguel Reyes; Victor Ponce; Hugo Jair Escalante; Jaime Shotton; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People Challenge 2014: Dataset and Results | Type | Conference Article | ||
| Year | 2014 | Publication | ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
| Volume | 8925 | Issue | Pages | 459-473 | |
| Keywords | Human Pose Recovery; Behavior Analysis; Action and in- teractions; Multi-modal gestures; recognition | ||||
| Abstract | This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HuPBA; ISE; 600.063;MV | Approved | no | ||
| Call Number | Admin @ si @ EBG2014 | Serial | 2529 | ||
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| Author | Francisco Cruz; Oriol Ramos Terrades | ||||
| Title | EM-Based Layout Analysis Method for Structured Documents | Type | Conference Article | ||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 315-320 | ||
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| Abstract | In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
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| ISSN | 1051-4651 | ISBN | Medium | ||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 602.006; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CrR2014 | Serial | 2530 | ||
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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 | ||
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| 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; ADAS; 600.076; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ HVS2014 | Serial | 2535 | ||
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| Author | Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados | ||||
| Title | Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 135-146 | |
| Keywords | Graphics recognition; Graphics retrieval; Image classification | ||||
| Abstract | This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. | ||||
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| Publisher | Springer Berlin Heidelberg | 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 | 978-3-662-44853-3 | Medium | |
| Area | Expedition | Conference | |||
| Notes | DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ HFF2014 | Serial | 2536 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Scene Text Recognition: No Country for Old Men? | Type | Conference Article | ||
| Year | 2014 | Publication | 1st International Workshop on Robust Reading | Abbreviated Journal | |
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| Area | Expedition | Conference | IWRR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GoK2014c | Serial | 2538 | ||
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| Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
| Title | Subspace Procrustes Analysis | Type | Conference Article | ||
| Year | 2014 | Publication | ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
| Volume | 8925 | Issue | Pages | 654-668 | |
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| Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling di erent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the bene ts of our approach. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | OR; HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PTI2014 | Serial | 2539 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 156-160 | ||
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| Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
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| Author | Frederic Sampedro; Anna Domenech; Sergio Escalera | ||||
| Title | Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Medical Imaging and Health Informatics | Abbreviated Journal | JMIHI |
| Volume | 4 | Issue | 6 | Pages | 825-831 |
| Keywords | CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION | ||||
| Abstract | In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SDE2014b | Serial | 2548 | ||
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| Author | Frederic Sampedro; Sergio Escalera; Anna Puig | ||||
| Title | Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 46 | Issue | Pages | 1-10 | |
| Keywords | Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation | ||||
| Abstract | In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SEP2014 | Serial | 2550 | ||
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| Author | Eloi Puertas; Miguel Angel Bautista; Daniel Sanchez; Sergio Escalera; Oriol Pujol | ||||
| Title | Learning to Segment Humans by Stacking their Body Parts, | Type | Conference Article | ||
| Year | 2014 | Publication | ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
| Volume | 8925 | Issue | Pages | 685-697 | |
| Keywords | Human body segmentation; Stacked Sequential Learning | ||||
| Abstract | Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body
part likelihood maps. These likelihood maps are obtained in a first stage by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline. |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PBS2014 | Serial | 2553 | ||
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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 | Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente | ||||
| Title | Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Biomedical Optics | Abbreviated Journal | JBiO |
| Volume | 19 | Issue | 12 | Pages | 126004-1 - 126004-9 |
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| Abstract | The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. | ||||
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| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ BLS2014 | Serial | 2563 | ||
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| Author | Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta | ||||
| Title | The ICDAR/GREC 2013 Music Scores Competition: Staff Removal | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 207-220 | |
| Keywords | Competition; Graphics recognition; Music scores; Writer identification; Staff removal | ||||
| Abstract | The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | B.Lamiroy; J.-M. Ogier | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
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| Notes | DAG; 600.077; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ FKV2014 | Serial | 2581 | ||
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| Author | Antonio Clavelli | ||||
| Title | A computational model of eye guidance, searching for text in real scene images | Type | Book Whole | ||
| Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Searching for text objects in real scene images is an open problem and a very active computer vision research area. A large number of methods have been proposed tackling the text search as extension of the ones from the document analysis field or inspired by general purpose object detection methods. However the general problem of object search in real scene images remains an extremely challenging problem due to the huge variability in object appearance. This thesis builds on top of the most recent findings in the visual attention literature presenting a novel computational model of eye guidance aiming to better describe text object search in real scene images.
First are presented the relevant state-of-the-art results from the visual attention literature regarding eye movements and visual search. Relevant models of attention are discussed and integrated with recent observations on the role of top-down constraints and the emerging need for a layered model of attention in which saliency is not the only factor guiding attention. Visual attention is then explained by the interaction of several modulating factors, such as objects, value, plans and saliency. Then we introduce our probabilistic formulation of attention deployment in real scene. The model is based on the rationale that oculomotor control depends on two interacting but distinct processes: an attentional process that assigns value to the sources of information and motor process that flexibly links information with action. In such framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the reward of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. In the experimental section the model is tested in laboratory condition, comparing model simulations with data from eye tracking experiments. The comparison is qualitative in terms of observable scan paths and quantitative in terms of statistical similarity of gaze shift amplitude. Experiments are performed using eye tracking data from both a publicly available dataset of face and text and from newly performed eye-tracking experiments on a dataset of street view pictures containing text. The last part of this thesis is dedicated to study the extent to which the proposed model can account for human eye movements in a low constrained setting. We used a mobile eye tracking device and an ad-hoc developed methodology to compare model simulated eye data with the human eye data from mobile eye tracking recordings. Such setting allow to test the model in an incomplete visual information condition, reproducing a close to real-life search task. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Dimosthenis Karatzas;Giuseppe Boccignone;Josep Llados | |
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| ISSN | ISBN | 978-84-940902-6-4 | Medium | ||
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| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Cla2014 | Serial | 2571 | ||
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