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Author |
Maria Salamo; Sergio Escalera; Petia Radeva |
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Title |
Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender |
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Conference Article |
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Year |
2009 |
Publication |
8th International Conference on Case-Based Reasoning |
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Volume |
5650 |
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298–312 |
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Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches. |
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Seattle, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02998-1 |
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ICCBR |
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HuPBA; MILAB |
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no |
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BCNPCL @ bcnpcl @ SER2009 |
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1187 |
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Author |
Khalid El Asnaoui; Petia Radeva |
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Title |
Automatically Assess Day Similarity Using Visual Lifelogs |
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Journal Article |
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Year |
2020 |
Publication |
International Journal of Intelligent Systems |
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IJIS |
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29 |
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298–310 |
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Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results. |
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MILAB; no proj |
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AsR2020 |
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3409 |
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Author |
O. Fors; A. Richichi; Xavier Otazu; J. Nuñez |
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A new wavelet-based approach for the automated treatment of large sets of lunar occultation data |
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2008 |
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Astronomy and Astrohysics |
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480 |
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297–304 |
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CIC |
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CAT @ cat @ FRO2008 |
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934 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Recognition of Multi-oriented Touching Characters in Graphical Documents |
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Conference Article |
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Year |
2008 |
Publication |
Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, |
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16 |
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297–304 |
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ICVGIP ’08 |
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DAG |
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no |
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DAG @ dag @ RPL2008c |
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1080 |
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Author |
Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans |
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Title |
Improved RGB-D-T based Face Recognition |
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Journal Article |
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Year |
2016 |
Publication |
IET Biometrics |
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BIO |
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Volume |
5 |
Issue |
4 |
Pages |
297 - 303 |
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Abstract |
Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. |
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Notes |
HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @ OCN2016 |
Serial |
2854 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Top-Down Deep Appearance Attention for Action Recognition |
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Conference Article |
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Year |
2017 |
Publication |
20th Scandinavian Conference on Image Analysis |
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Volume |
10269 |
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Pages |
297-309 |
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Keywords |
Action recognition; CNNs; Feature fusion |
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Abstract |
Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches. |
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Tromso; June 2017 |
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SCIA |
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Notes |
LAMP; 600.109; 600.068; 600.120 |
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no |
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Call Number |
Admin @ si @ RKW2017b |
Serial |
3039 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Towards Modelling an Attention-Based Text Localization Process |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
7887 |
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Pages |
296-303 |
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Keywords |
text localization; visual attention; eye guidance |
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Abstract |
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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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ CKL2013 |
Serial |
2291 |
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Author |
Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta |
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Title |
Diaphragm border detection in coronary X-ray angiographies: New method and applications |
Type |
Journal Article |
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Year |
2014 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
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Volume |
38 |
Issue |
4 |
Pages |
296-305 |
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Abstract |
X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. |
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Notes |
MILAB; LAMP; 600.079 |
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no |
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Call Number |
Admin @ si @ PCR2014 |
Serial |
2468 |
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Author |
Laura Igual; Santiago Segui; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Eigenmotion-Based Detection of Intestinal Contractions |
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Conference Article |
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Year |
2007 |
Publication |
Computer Analysis of Images and Patterns, 12th International Conference |
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Volume |
4673 |
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293–300 |
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Address |
Vienna (Austria) |
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978-3-540-74271-5 |
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CAIP |
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Notes |
OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ ISV2007a |
Serial |
895 |
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Author |
David Fernandez; Josep Llados; Alicia Fornes |
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Title |
A graph-based approach for segmenting touching lines in historical handwritten documents |
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Journal Article |
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Year |
2014 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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Volume |
17 |
Issue |
3 |
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293-312 |
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Keywords |
Text line segmentation; Handwritten documents; Document image processing; Historical document analysis |
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Abstract |
Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data. |
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Springer Berlin Heidelberg |
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ISSN |
1433-2833 |
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Notes |
DAG; 600.056; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ FLF2014 |
Serial |
2459 |
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Author |
David Aldavert; Marçal Rusiñol |
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Title |
Manuscript text line detection and segmentation using second-order derivatives analysis |
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Conference Article |
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Year |
2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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293 - 298 |
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Keywords |
text line detection; text line segmentation; text region detection; second-order derivatives |
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In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 302.065; 600.121 |
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Admin @ si @ AlR2018a |
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3104 |
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Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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Conference Article |
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Year |
2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Angers (France) |
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ICINCO |
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RV;ADAS |
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no |
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Admin @ si @ RLA2007 |
Serial |
900 |
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Author |
Albert Gordo; Ernest Valveny |
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Title |
The diagonal split: A pre-segmentation step for page layout analysis & classification |
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Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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290–297 |
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Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives. |
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Address |
Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-02171-8 |
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IbPRIA |
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DAG |
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no |
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DAG @ dag @ Gov2009b |
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1176 |
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Author |
Wenjuan Gong; Andrew Bagdanov; Xavier Roca; Jordi Gonzalez |
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Title |
Automatic Key Pose Selection for 3D Human Action Recognition |
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Conference Article |
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2010 |
Publication |
6th International Conference on Articulated Motion and Deformable Objects |
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6169 |
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290–299 |
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This article describes a novel approach to the modeling of human actions in 3D. The method we propose is based on a “bag of poses” model that represents human actions as histograms of key-pose occurrences over the course of a video sequence. Actions are first represented as 3D poses using a sequence of 36 direction cosines corresponding to the angles 12 joints form with the world coordinate frame in an articulated human body model. These pose representations are then projected to three-dimensional, action-specific principal eigenspaces which we refer to as aSpaces. We introduce a method for key-pose selection based on a local-motion energy optimization criterion and we show that this method is more stable and more resistant to noisy data than other key-poses selection criteria for action recognition. |
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Springer Verlag |
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0302-9743 |
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978-3-642-14060-0 |
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AMDO |
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DAG @ dag @ GBR2010 |
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1317 |
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Author |
Fernando Vilariño; Gerard Lacey; Jiang Zhou; Hugh Mulcahy; Stephen Patchett |
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Title |
Automatic Labeling of Colonoscopy Video for Cancer Detection |
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2007 |
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In Proc. berian Conference, IbPRIA |
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290-297 |
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800 |
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MV;SIAI |
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fernando @ fernando @ |
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2431 |
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