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T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger |
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Title |
The achromatic locus: Effect of navigation direction in color space |
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Journal Article |
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2014 |
Publication |
Journal of Vision |
Abbreviated Journal |
VSS |
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14 (1) |
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25 |
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1-11 |
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Keywords |
achromatic; unique hues; color constancy; luminance; color space |
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Abstract |
5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes. |
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DAG; 600.077 |
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no |
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Admin @ si @ CPX2014 |
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2418 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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Journal Article |
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Year |
2016 |
Publication |
Signal Processing |
Abbreviated Journal |
SP |
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125 |
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36-47 |
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Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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Notes ![sorted by Notes field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
DAG; 600.061; 600.077 |
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no |
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Admin @ si @ DTR2016 |
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2946 |
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Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
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A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
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2014 |
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Manuscript Cultures |
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7 |
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47-58 |
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With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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Notes ![sorted by Notes field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
DAG; 600.061; 600.077 |
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no |
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Admin @ si @ |
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3190 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
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Journal Article |
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Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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19 |
Issue |
4 |
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335-349 |
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scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
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Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
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Notes ![sorted by Notes field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
DAG; 600.056; 601.197 |
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no |
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Admin @ si @ GoK2016a |
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2862 |
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Author |
Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Knowledge-driven understanding of images in comic books |
Type |
Journal Article |
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Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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18 |
Issue |
3 |
Pages |
199-221 |
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Document Understanding; comics analysis; expert system |
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Abstract |
Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. |
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Springer Berlin Heidelberg |
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1433-2833 |
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Notes ![sorted by Notes field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
DAG; 600.056; 600.077 |
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Call Number |
RGK2015 |
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2595 |
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