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Author  |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Fast: Facilitated and accurate scene text proposals through fcn guided pruning |
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Journal Article |
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Year |
2019 |
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Pattern Recognition Letters |
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PRL |
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Volume |
119 |
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112-120 |
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Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. |
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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ BGN2019 |
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3342 |
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Author |
Eduardo Aguilar; Petia Radeva |
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Title |
Uncertainty-aware integration of local and flat classifiers for food recognition |
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2020 |
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Pattern Recognition Letters |
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136 |
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237-243 |
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Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. |
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MILAB; no proj |
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Admin @ si @ AgR2020 |
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3525 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
A model for image generation and symbol recognition through the deformation of lineal shapes |
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Year |
2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
Issue |
15 |
Pages |
2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models |
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2007 |
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Pattern Recognition Letters |
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PRL |
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28 |
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15 |
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2116-2126 |
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ADAS |
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ADAS @ adas @ DoS2007c |
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877 |
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Fadi Dornaika; Angel Sappa |
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Title |
Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression |
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2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
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5 |
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535–543 |
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This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. |
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Elsevier Science Inc. |
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0167-8655 |
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ADAS |
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no |
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ADAS @ adas @ DoS2009a |
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1115 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Compact color texture description for texture classification |
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Journal Article |
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2015 |
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Pattern Recognition Letters |
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PRL |
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51 |
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16-22 |
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Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This
gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive
evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively. |
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LAMP; 600.068; 600.079;ADAS |
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no |
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Call Number |
Admin @ si @ KRW2015a |
Serial |
2587 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multispectral Piecewise Planar Stereo using Manhattan-World Assumption |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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34 |
Issue |
1 |
Pages |
52-61 |
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Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images |
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This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. |
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ADAS; 600.054; 600.055; 605.203 |
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no |
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Call Number |
Admin @ si @ BLS2013 |
Serial |
2245 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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27 |
Issue |
8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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no |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Puig |
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Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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2014 |
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Pattern Recognition Letters |
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PRL |
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46 |
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1-10 |
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Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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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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Admin @ si @ SEP2014 |
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2550 |
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Author |
Gemma Sanchez; Josep Llados; K. Tombre |
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A mean string algorithm to compute the average among a set of 2D shapes |
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2002 |
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Pattern Recognition Letters |
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23 |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
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Understanding trained CNNs by indexing neuron selectivity |
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2020 |
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Pattern Recognition Letters |
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136 |
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318-325 |
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The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. |
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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ RVL2019 |
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3310 |
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Jaume Amores; N. Sebe; Petia Radeva |
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Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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2006 |
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Pattern Recognition Letters |
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27 |
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3 |
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201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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Jaume Amores; Petia Radeva |
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Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005b |
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592 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Feature Selection on Node Statistics Based Embedding of Graphs |
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2012 |
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Pattern Recognition Letters |
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PRL |
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33 |
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15 |
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1980–1990 |
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Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
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Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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Admin @ si @ GVB2012b |
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1993 |
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Jordi Vitria; J. Llacer |
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Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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17 |
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14 |
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1491–1498 |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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