| Records |
| 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 |
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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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HuPBA;MILAB |
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no |
| Call Number |
Admin @ si @ SEP2014 |
Serial  |
2550 |
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| Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
| Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
Type |
Journal Article |
| Year |
2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
| Volume |
28 |
Issue |
1 |
Pages |
87-101 |
| Keywords |
Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
| Abstract |
The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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| Notes |
HUPBA;MILAB |
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no |
| Call Number |
Admin @ si @ CCE2015 |
Serial |
2549 |
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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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HuPBA;MILAB |
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no |
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Admin @ si @ SDE2014b |
Serial |
2548 |
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| Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
| Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
| Volume |
26 |
Issue |
8 |
Pages |
1761 - 1775 |
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| Abstract |
Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
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| ISSN |
2162-237X |
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| Notes |
ADAS; 600.076 |
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no |
| Call Number |
Admin @ si @ DFD2015 |
Serial |
2547 |
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| Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
| Title |
Normalisation et validation d'images de documents capturées en mobilité |
Type |
Conference Article |
| Year |
2014 |
Publication |
Colloque International Francophone sur l'Écrit et le Document |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
109-124 |
| Keywords |
mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction |
| Abstract |
Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
| Address |
Nancy; France; March 2014 |
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CIFED |
| Notes |
DAG; 601.223; 600.077 |
Approved |
no |
| Call Number |
Admin @ si @ RCO2014b |
Serial |
2546 |
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| Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
| Title |
Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images |
Type |
Conference Article |
| Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
181 - 185 |
| Keywords |
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| Abstract |
Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. |
| Address |
Tours; France; April 2014 |
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ISBN |
978-1-4799-3243-6 |
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DAS |
| Notes |
DAG; 601.223; 600.077 |
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no |
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Admin @ si @ RCO2014a |
Serial |
2545 |
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| Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
| Title |
Efficient segmentation-free keyword spotting in historical document collections |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
48 |
Issue |
2 |
Pages |
545–555 |
| Keywords |
Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization |
| Abstract |
In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. |
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DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 |
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no |
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Admin @ si @ RAT2015a |
Serial |
2544 |
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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 |
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Issue |
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Pages |
156-160 |
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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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978-1-4799-3243-6 |
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DAS |
| Notes |
DAG; 600.077 |
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no |
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Admin @ si @ DTR2014 |
Serial |
2543 |
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| Author |
Mohammad Rouhani; Angel Sappa; E. Boyer |
| Title |
Implicit B-Spline Surface Reconstruction |
Type |
Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
24 |
Issue |
1 |
Pages |
22 - 32 |
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This paper presents a fast and flexible curve, and surface reconstruction technique based on implicit B-spline. This representation does not require any parameterization and it is locally supported. This fact has been exploited in this paper to propose a reconstruction technique through solving a sparse system of equations. This method is further accelerated to reduce the dimension to the active control lattice. Moreover, the surface smoothness and user interaction are allowed for controlling the surface. Finally, a novel weighting technique has been introduced in order to blend small patches and smooth them in the overlapping regions. The whole framework is very fast and efficient and can handle large cloud of points with very low computational cost. The experimental results show the flexibility and accuracy of the proposed algorithm to describe objects with complex topologies. Comparisons with other fitting methods highlight the superiority of the proposed approach in the presence of noise and missing data. |
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1057-7149 |
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ADAS; 600.076 |
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no |
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Admin @ si @ RSB2015 |
Serial |
2541 |
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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 |
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Issue |
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Pages |
337 - 342 |
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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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AVSS |
| Notes |
LAMP; 600.079 |
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no |
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Admin @ si @ BSB2014 |
Serial |
2540 |
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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 |
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| Volume |
8925 |
Issue |
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Pages |
654-668 |
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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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LNCS |
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ECCVW |
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OR; HuPBA;MILAB |
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no |
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Admin @ si @ PTI2014 |
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2539 |
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| Author |
Lluis Gomez; Dimosthenis Karatzas |
| Title |
Scene Text Recognition: No Country for Old Men? |
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Conference Article |
| Year |
2014 |
Publication |
1st International Workshop on Robust Reading |
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IWRR |
| Notes |
DAG; 600.077 |
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no |
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Admin @ si @ GoK2014c |
Serial |
2538 |
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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 |
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8746 |
Issue |
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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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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-662-44853-3 |
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| Notes |
DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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no |
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Admin @ si @ HFF2014 |
Serial |
2536 |
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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 |
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Book Chapter |
| Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
Issue |
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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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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.076; 600.077 |
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no |
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Admin @ si @ HVS2014 |
Serial |
2535 |
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| Author |
Mohammad Rouhani; E. Boyer; Angel Sappa |
| Title |
Non-Rigid Registration meets Surface Reconstruction |
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Conference Article |
| Year |
2014 |
Publication |
International Conference on 3D Vision |
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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. |
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Tokyo; Japan; December 2014 |
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3DV |
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ADAS; 600.055; 600.076 |
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Admin @ si @ RBS2014 |
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2534 |
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