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Author |
Oriol Pujol; Sergio Escalera; Petia Radeva |
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Title |
An Incremental Node Embedding Technique for Error Correcting Output Codes |
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2008 |
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Pattern Recognition |
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PR |
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41 |
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2 |
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713–725 |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PER2008 |
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942 |
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Author |
Ignasi Rius; Jordi Gonzalez; Javier Varona; Xavier Roca |
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Title |
Action-specific motion prior for efficient bayesian 3D human body tracking |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition |
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PR |
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42 |
Issue |
11 |
Pages |
2907–2921 |
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In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane.
Towards this end, an action-specific motion model is trained from a database of real
motion-captured performances. The learnt motion model is used within a particle
filtering framework as a priori knowledge on human motion. First, our dynamic
model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints. |
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0031-3203 |
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ISE |
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no |
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ISE @ ise @ RGV2009 |
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1159 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Title |
Median Graphs: A Genetic Approach based on New Theoretical Properties |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
42 |
Issue |
9 |
Pages |
2003–2012 |
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Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition |
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Abstract |
Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. |
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DAG |
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no |
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DAG @ dag @ FVS2009b |
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1167 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Variance reduction techniques in particle-based visual contour Tracking |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
42 |
Issue |
11 |
Pages |
2372–2391 |
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Keywords |
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
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Abstract |
This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
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ADAS |
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no |
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ADAS @ adas @ PoL2009a |
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1168 |
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Author |
Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta |
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Title |
Structure-preserving smoothing of biomedical images |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
44 |
Issue |
9 |
Pages |
1842-1851 |
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Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography |
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Abstract |
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. |
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0031-3203 |
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IAM; ADAS |
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no |
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Call Number |
IAM @ iam @ GHB2011 |
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1526 |
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Author |
Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal |
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Title |
A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
44 |
Issue |
8 |
Pages |
1671-1683 |
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Abstract |
In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels. |
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DAG |
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no |
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Call Number |
Admin @ si @ SDP2011 |
Serial |
1727 |
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Author |
Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez |
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Title |
Discriminative Compact Pyramids for Object and Scene Recognition |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
Issue |
4 |
Pages |
1627-1636 |
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Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. |
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0031-3203 |
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Notes |
ISE; CAT;CIC |
Approved |
no |
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Call Number |
Admin @ si @ EKW2012 |
Serial |
1807 |
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Author |
Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre |
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Title |
Continuous Generalized Procrustes Analysis |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
2 |
Pages |
659–671 |
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Procrustes analysis; 2D shape model; Continuous approach |
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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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OR; HuPBA; 605.203; 600.046;MILAB |
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no |
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Admin @ si @ IPE2014 |
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2352 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
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Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
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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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0031-3203 |
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DAG; 600.045; 600.061; 600.077 |
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no |
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Admin @ si @ RuL2013 |
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2343 |
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Bogdan Raducanu; Fadi Dornaika |
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Embedding new observations via sparse-coding for non-linear manifold learning |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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1 |
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480-492 |
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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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LAMP; |
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Admin @ si @ RaD2013b |
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2316 |
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Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Writer identification in handwritten musical scores with bags of notes |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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5 |
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1337-1345 |
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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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DAG |
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Admin @ si @ GFV2013 |
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2307 |
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Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
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Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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47 |
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3 |
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1073-1082 |
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Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
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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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DAG; 600.077; 602.101 |
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no |
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Admin @ si @ FFB2014 |
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2297 |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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A non-rigid appearance model for shape description and recognition |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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9 |
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3105--3113 |
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Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition |
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In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. |
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0031-3203 |
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DAG @ dag @ AFV2012 |
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1982 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
Pages |
3072-3083 |
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Keywords |
Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
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Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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Admin @ si @ GVB2012a |
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1992 |
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Author |
Mario Hernandez; Joao Sanchez; Jordi Vitria |
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Title |
Selected papers from Iberian Conference on Pattern Recognition and Image Analysis |
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Book Whole |
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Year |
2012 |
Publication |
Pattern Recognition |
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Volume |
45 |
Issue |
9 |
Pages |
3047-3582 |
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0031-3203 |
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OR;MV |
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Admin @ si @ HSV2012 |
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2069 |
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