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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Combining structural and statistical strategies for unsupervised wall detection in floor plans |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated data
to learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing the
process in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on
4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045 |
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no |
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Call Number |
Admin @ si @ HVS2013a |
Serial |
2321 |
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Author |
Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization |
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Conference Article |
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Year |
2013 |
Publication |
Proceedings of the International Conference on Computer Vision Theory and Applications |
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Volume |
1 |
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162-171 |
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Keywords |
Colonoscopy; Blood vessel; Linear features; Valley detection |
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This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects,such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization
algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance. |
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Barcelona; February 2013 |
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SciTePress |
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800 |
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VISIGRAPP |
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Notes |
MV; 600.054; 600.057;SIAI |
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no |
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IAM @ iam @ NBS2013 |
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2198 |
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Author |
Gioacchino Vino; Angel Sappa |
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Title |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach |
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Conference Article |
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Year |
2013 |
Publication |
10th International Conference on Image Analysis and Recognition |
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7950 |
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354-363 |
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This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. |
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Póvoa de Varzim; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-39093-7 |
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ICIAR |
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ADAS; 600.055 |
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no |
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Admin @ si @ ViS2013 |
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2562 |
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Author |
Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo |
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Title |
Multispectral Stereo Image Correspondence |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th International Conference on Computer Analysis of Images and Patterns |
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8048 |
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Pages |
217-224 |
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This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach. |
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York; uk; August 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40245-6 |
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CAIP |
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ADAS; 600.055 |
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no |
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Call Number |
Admin @ si @ PST2013 |
Serial |
2561 |
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Author |
Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Title |
Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction |
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Journal Article |
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Year |
2013 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
100 |
Issue |
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Pages |
183–196 |
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Keywords |
Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction |
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Abstract |
This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. |
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Elsevier |
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ISE |
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no |
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Admin @ si @ HAR2013 |
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1808 |
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Author |
David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich |
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Title |
Traffic sign recognition for computer vision project-based learning |
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Journal Article |
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Year |
2013 |
Publication |
IEEE Transactions on Education |
Abbreviated Journal |
T-EDUC |
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Volume |
56 |
Issue |
3 |
Pages |
364-371 |
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Keywords |
traffic signs |
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Abstract |
This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. |
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0018-9359 |
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Notes |
ADAS; CIC |
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no |
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Admin @ si @ GSL2013; ADAS @ adas @ |
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2160 |
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Author |
Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados |
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Title |
Classification of Administrative Document Images by Logo Identification |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.056; 600.045; 605.203 |
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no |
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Admin @ si @ |
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2348 |
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Author |
Ivo Everts; Jan van Gemert; Theo Gevers |
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Title |
Evaluation of Color STIPs for Human Action Recognition |
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Conference Article |
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Year |
2013 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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2850-2857 |
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This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition. |
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Portland; oregon; June 2013 |
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1063-6919 |
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CVPR |
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ALTRES;ISE |
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no |
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Call Number |
Admin @ si @ EGG2013 |
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2364 |
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Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
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Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
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Journal Article |
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Year |
2013 |
Publication |
Expert Systems |
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EXSY |
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30 |
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2 |
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101-114 |
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Human action recognition;body-part detection;hidden Markov model |
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This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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ISE |
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no |
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Admin @ si @ CBG2013 |
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1809 |
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Author |
S.Grau; Ana Puig; Sergio Escalera; Maria Salamo |
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Title |
Intelligent Interactive Volume Classification |
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Conference Article |
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Year |
2013 |
Publication |
Pacific Graphics |
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32 |
Issue |
7 |
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23-28 |
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This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification. |
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978-3-905674-50-7 |
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PG |
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HuPBA; 600.046;MILAB |
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no |
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Admin @ si @ GPE2013b |
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2355 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1078-1082 |
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This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 600.056; 600.061; 601.152 |
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no |
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Admin @ si @ DLB2013a |
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2358 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Texture-independent recognition of facial expressions in image snapshots and videos |
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Journal Article |
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Year |
2013 |
Publication |
Machine Vision and Applications |
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MVA |
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24 |
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4 |
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811-820 |
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This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines. |
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Springer-Verlag |
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0932-8092 |
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OR; 600.046; 605.203;MV |
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no |
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Admin @ si @ RaD2013 |
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2230 |
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Author |
Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders |
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Title |
Selective Search for Object Recognition |
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Journal Article |
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Year |
2013 |
Publication |
International Journal of Computer Vision |
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IJCV |
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104 |
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2 |
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154-171 |
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This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). |
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0920-5691 |
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ALTRES;ISE |
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Admin @ si @ USG2013 |
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2362 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Towards Modelling an Attention-Based Text Localization Process |
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Conference Article |
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2013 |
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6th Iberian Conference on Pattern Recognition and Image Analysis |
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7887 |
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296-303 |
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text localization; visual attention; eye guidance |
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This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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DAG |
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Admin @ si @ CKL2013 |
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2291 |
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Jorge Bernal; David Vazquez (eds) |
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Computer vision Trends and Challenges |
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2013 |
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Computer vision Trends and Challenges |
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CVCRD; Computer Vision |
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This book contains the papers presented at the Eighth CVC Workshop on Computer Vision Trends and Challenges (CVCR&D'2013). The workshop was held at the Computer Vision Center (Universitat Autònoma de Barcelona), the October 25th, 2013. The CVC workshops provide an excellent opportunity for young researchers and project engineers to share new ideas and knowledge about the progress of their work, and also, to discuss about challenges and future perspectives. In addition, the workshop is the welcome event for new people that recently have joined the institute.
The program of CVCR&D is organized in a single-track single-day workshop. It comprises several sessions dedicated to specific topics. For each session, a doctor working on the topic introduces the general research lines. The PhD students expose their specific research. A poster session will be held for open questions. Session topics cover the current research lines and development projects of the CVC: Medical Imaging, Medical Imaging, Color & Texture Analysis, Object Recognition, Image Sequence Evaluation, Advanced Driver Assistance Systems, Machine Vision, Document Analysis, Pattern Recognition and Applications. We want to thank all paper authors and Program Committee members. Their contribution shows that the CVC has a dynamic, active, and promising scientific community.
We hope you all enjoy this Eighth workshop and we are looking forward to meeting you and new people next year in the Ninth CVCR&D. |
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Jorge Bernal; David Vazquez |
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978-84-940902-2-6 |
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ADAS @ adas @ BeV2013 |
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2339 |
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