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David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title  |
A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting |
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Journal Article |
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2015 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
Issue |
3 |
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223-234 |
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Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation |
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Abstract |
The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 |
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Admin @ si @ ART2015 |
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2679 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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150 |
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A |
Pages |
106–115 |
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Keywords |
Reinforcement learning; Q-learning; Bag of features; Descriptors |
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This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. |
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ADAS; 600.055; 600.076 |
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Admin @ si @ PST2015 |
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2473 |
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Author |
Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez |
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Title |
An Efficient Approach to Onboard Stereo Vision System Pose Estimation |
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Year |
2008 |
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IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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9 |
Issue |
3 |
Pages |
476–490 |
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Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system |
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This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. |
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IEEE |
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ADAS |
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ADAS @ adas @ SDP2008 |
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1000 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An iterative multiresolution scheme for SFM with missing data |
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Journal Article |
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Year |
2009 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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34 |
Issue |
3 |
Pages |
240–258 |
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Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach. |
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ADAS |
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ADAS @ adas @ JSL2009a |
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1163 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes |
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Journal Article |
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Year |
2010 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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28 |
Issue |
1 |
Pages |
164-176 |
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Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach. |
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0262-8856 |
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ADAS |
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ADAS @ adas @ JSL2010 |
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1278 |
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