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| Author | Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers | ||||
| Title | A Statistical Method for 2D Facial Landmarking | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 2 | Pages | 844-858 |
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| Abstract | IF = 3.32
Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE). |
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ DSG 2012 | Serial | 1853 | ||
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| Author | Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo | ||||
| Title | Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 50 | Issue | 1 | Pages | 112-121 |
| Keywords | RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition | ||||
| Abstract | PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach. |
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| Notes | HuPBA;MV; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ HBP2014 | Serial | 2353 | ||
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| Author | Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa | ||||
| Title | Multiple target tracking for intelligent headlights control | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 13 | Issue | 2 | Pages | 594-605 |
| Keywords | Intelligent Headlights | ||||
| Abstract | Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. | ||||
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| ISSN | 1524-9050 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g | Serial | 1877 | ||
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| Author | Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo | ||||
| Title | Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2011 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
| Volume | 30 | Issue | 7 | Pages | 2107-2115 |
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| Abstract | IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EPA2011 | Serial | 1881 | ||
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| Author | Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva | ||||
| Title | A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder | Type | Journal Article | ||
| Year | 2011 | Publication | BioMedical Engineering Online | Abbreviated Journal | BEO |
| Volume | 10 | Issue | 105 | Pages | 1-23 |
| Keywords | Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework | ||||
| Abstract | Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD. |
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| ISSN | 1475-925X | ISBN | Medium | ||
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ISH2011 | Serial | 1882 | ||
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| Author | David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo | ||||
| Title | Virtual and Real World Adaptation for Pedestrian Detection | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 4 | Pages | 797-809 |
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
| Abstract | Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | ADAS; 600.057; 600.054; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ VML2014 | Serial | 2275 | ||
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| Author | Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre | ||||
| Title | Continuous Generalized Procrustes Analysis | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 2 | Pages | 659–671 |
| Keywords | Procrustes analysis; 2D shape model; Continuous approach | ||||
| Abstract | PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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| Notes | OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
| Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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| Author | Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca | ||||
| Title | Toward Real-Time Pedestrian Detection Based on a Deformable Template Model | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 15 | Issue | 1 | Pages | 355-364 |
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| Abstract | Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. | ||||
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| ISSN | 1524-9050 | ISBN | Medium | ||
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| Notes | ISE; 601.213; 600.078 | Approved | no | ||
| Call Number | PGH2014 | Serial | 2350 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title | Boosting the Handwritten Word Spotting Experience by Including the User in the Loop | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 3 | Pages | 1063–1072 |
| Keywords | Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling | ||||
| Abstract | In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.045; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RuL2013 | Serial | 2343 | ||
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| Author | Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades | ||||
| Title | Flowchart Recognition for Non-Textual Information Retrieval in Patent Search | Type | Journal Article | ||
| Year | 2014 | Publication | Information Retrieval | Abbreviated Journal | IR |
| Volume | 17 | Issue | 5-6 | Pages | 545-562 |
| Keywords | Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition | ||||
| Abstract | Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. | ||||
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| ISSN | 1386-4564 | ISBN | Medium | ||
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| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RHR2013 | Serial | 2342 | ||
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| Author | Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu | ||||
| Title | New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired | Type | Journal Article | ||
| Year | 2014 | Publication | Computer | Abbreviated Journal | COMP |
| Volume | 47 | Issue | 4 | Pages | 52-58 |
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| Abstract | Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices. | ||||
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| ISSN | 0018-9162 | ISBN | Medium | ||
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ TSR2014a | Serial | 2317 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 1 | Pages | 480-492 |
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| Abstract | Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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| Author | Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Writer identification in handwritten musical scores with bags of notes | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 46 | Issue | 5 | Pages | 1337-1345 |
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| Abstract | Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GFV2013 | Serial | 2307 | ||
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| Author | Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke | ||||
| Title | Keyword spotting for self-training of BLSTM NN based handwriting recognition systems | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 3 | Pages | 1073-1082 |
| Keywords | Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning | ||||
| Abstract | The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. | ||||
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| Notes | DAG; 600.077; 602.101 | Approved | no | ||
| Call Number | Admin @ si @ FFB2014 | Serial | 2297 | ||
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| Author | Jordi Roca; C. Alejandro Parraga; Maria Vanrell | ||||
| Title | Chromatic settings and the structural color constancy index | Type | Journal Article | ||
| Year | 2013 | Publication | Journal of Vision | Abbreviated Journal | JV |
| Volume | 13 | Issue | 4-3 | Pages | 1-26 |
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| Abstract | Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects. | ||||
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| Notes | CIC; 600.052; 600.051; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ RPV2013 | Serial | 2288 | ||
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