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Author |
Hany Salah Eldeen |
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Title |
Colour Naming in Context through a Perceptual Model |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
130 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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no |
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Call Number |
Admin @ si @ Eld2009 |
Serial |
2389 |
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Author |
Naila Murray |
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Title |
Perceptual Feature Detection |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
131 |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ Mur2009 |
Serial |
2390 |
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Author |
Josep M. Gonfaus |
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Title |
Semantic Segmentation of Images Using Random Ferns |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
132 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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ISE |
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no |
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Call Number |
Admin @ si @ Gon2009 |
Serial |
2391 |
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Author |
Alejandro Gonzalez Alzate |
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Title |
Evaluation of spatiotemporal descriptors for pedestrian detection in video sequences |
Type |
Report |
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Year |
2011 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
166 |
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Address |
Bellaterra (Spain) |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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ADAS |
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no |
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Call Number |
Admin @ si @ Gon2011 |
Serial |
1932 |
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Author |
Yainuvis Socarras |
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Title |
Image segmentation for improving pedestrian detection |
Type |
Report |
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Year |
2011 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
167 |
Issue |
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Address |
Bellaterra (Spain) |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Notes |
ADAS; |
Approved |
no |
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Call Number |
Admin @ si @ Soc2011 |
Serial |
1933 |
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Author |
Maria del Camp Davesa |
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Title |
Human action categorization in image sequences |
Type |
Report |
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Year |
2011 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
169 |
Issue |
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Pages |
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Address |
Bellaterra (Spain) |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Notes |
CiC;CIC |
Approved |
no |
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Call Number |
Admin @ si @ Dav2011 |
Serial |
1934 |
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Permanent link to this record |
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Author |
Monica Piñol |
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Title |
Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning |
Type |
Report |
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Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
162 |
Issue |
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Pages |
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Abstract |
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Address |
Bellaterra (Barcelona) |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ Piñ2010 |
Serial |
1936 |
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Permanent link to this record |
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Author |
Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera |
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Title |
Automatic Human Behavior Analysis in ADHD |
Type |
Conference Article |
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Year |
2012 |
Publication |
Eunethydis 2nd International ADHD Conference |
Abbreviated Journal |
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Abstract |
Poster |
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Conference |
EUNETHYDIS |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ EMI2012a |
Serial |
2058 |
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Permanent link to this record |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
265-269 |
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Keywords |
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Abstract |
In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods. |
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Address |
Washington; USA; August 2013 |
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Edition |
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ISSN |
1520-5363 |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ DTR2013b |
Serial |
2331 |
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Permanent link to this record |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Handwritten Word Spotting with Corrected Attributes |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1017-1024 |
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Abstract |
We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. |
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Address |
Sydney; Australia; December 2013 |
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Edition |
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ISSN |
1550-5499 |
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Conference |
ICCV |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ AGF2013 |
Serial |
2327 |
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Permanent link to this record |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi |
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Title |
Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars |
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 |
Issue |
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Pages |
133-140 |
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Abstract |
In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. |
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Address |
Madeira; Portugal; June 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
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Conference |
IbPRIA |
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Notes |
DAG; 605.203 |
Approved |
no |
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Call Number |
Admin @ si @ ACS2013 |
Serial |
2328 |
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Permanent link to this record |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Handwritten Line Detection via an EM Algorithm |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
718-722 |
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Abstract |
In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. |
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Address |
Washington; USA; August 2013 |
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Edition |
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ISSN |
1520-5363 |
ISBN |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ CrT2013 |
Serial |
2329 |
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Permanent link to this record |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Document noise removal using sparse representations over learned dictionary |
Type |
Conference Article |
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Year |
2013 |
Publication |
Symposium on Document engineering |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
161-168 |
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Abstract |
best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art. |
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Address |
Barcelona; October 2013 |
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ISBN |
978-1-4503-1789-4 |
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Conference |
ACM-DocEng |
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Notes |
DAG; 600.061 |
Approved |
no |
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Call Number |
Admin @ si @ DTR2013a |
Serial |
2330 |
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Permanent link to this record |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A Deformable HOG-based Shape Descriptor |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1022-1026 |
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Keywords |
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Abstract |
In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval |
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Address |
Washington; USA; August 2013 |
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Edition |
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ISSN |
1520-5363 |
ISBN |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ AFV2013 |
Serial |
2326 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Notation-invariant patch-based wall detector in architectural floor plans |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Graphics Recognition. New Trends and Challenges |
Abbreviated Journal |
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Volume |
7423 |
Issue |
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Pages |
79--88 |
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Keywords |
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Abstract |
Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-36823-3 |
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Notes |
DAG; 600.045; 600.056; 605.203 |
Approved |
no |
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Call Number |
Admin @ si @ HMS2013 |
Serial |
2322 |
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Permanent link to this record |