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Author |
Maria del Camp Davesa |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Human action categorization in image sequences |
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Report |
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Year |
2011 |
Publication |
CVC Technical Report |
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169 |
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Bellaterra (Spain) |
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Computer Vision Center |
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Master's thesis |
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CiC;CIC |
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no |
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Admin @ si @ Dav2011 |
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1934 |
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Author |
Monica Piñol |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning |
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Report |
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2010 |
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CVC Technical Report |
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162 |
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Bellaterra (Barcelona) |
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Computer Vision Center |
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Master's thesis |
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ADAS |
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no |
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Admin @ si @ Piñ2010 |
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1936 |
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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 |
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Conference Article |
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Year |
2012 |
Publication |
Eunethydis 2nd International ADHD Conference |
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Poster |
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EUNETHYDIS |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ EMI2012a |
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2058 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
![download PDF file pdf](img/file_PDF.gif)
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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 |
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Pages |
265-269 |
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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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Washington; USA; August 2013 |
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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 |
![download PDF file pdf](img/file_PDF.gif)
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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 |
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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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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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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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ISSN |
0302-9743 |
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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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1520-5363 |
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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 |
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Volume |
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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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978-1-4503-1789-4 |
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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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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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1520-5363 |
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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 |
![download PDF file pdf](img/file_PDF.gif)
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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 |
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Volume |
7423 |
Issue |
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Pages |
79--88 |
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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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Springer Berlin Heidelberg |
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LNCS |
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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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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Quantitative analysis of non-verbal communication for competence analysis |
Type |
Conference Article |
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Year |
2013 |
Publication |
16th Catalan Conference on Artificial Intelligence |
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Volume |
256 |
Issue |
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Pages |
105-114 |
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Vic; October 2013 |
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CCIA |
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Notes |
HUPBA;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ CCE2013 |
Serial |
2324 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; David Fernandez; Ernest Valveny; Josep Llados; Gemma Sanchez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Unsupervised wall detector in architectural floor plan |
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 |
1245-1249 |
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Abstract |
Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision. |
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Address |
Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.061; 600.056; 600.045 |
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no |
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Call Number |
Admin @ si @ HFV2013 |
Serial |
2319 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez;Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Perceptual retrieval of architectural floor plans |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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Abstract |
This paper proposes a runlength histogram signature as a percetual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query,
similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Preliminary results show the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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Notes |
DAG; 600.045; 600.056; 600.061 |
Approved |
no |
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Call Number |
Admin @ si @ HFF2013a |
Serial |
2320 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
![download PDF file pdf](img/file_PDF.gif)
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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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DAG; 600.045 |
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2321 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired |
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Journal Article |
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2014 |
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Computer |
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COMP |
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47 |
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4 |
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52-58 |
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Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices. |
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0018-9162 |
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LAMP; |
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Admin @ si @ TSR2014a |
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2317 |
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