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C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger |
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Title |
Limitations of visual gamma corrections in LCD displays |
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Year |
2014 |
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Displays |
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Dis |
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35 |
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5 |
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227–239 |
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Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration |
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A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. |
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CIC; DAG; 600.052; 600.077; 600.074 |
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Admin @ si @ PRK2014 |
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2511 |
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Author |
Kaida Xiao; Chenyang Fu; Dimosthenis Karatzas; Sophie Wuerger |
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Title |
Visual Gamma Correction for LCD Displays |
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Journal Article |
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Year |
2011 |
Publication |
Displays |
Abbreviated Journal |
DIS |
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32 |
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1 |
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17-23 |
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Keywords |
Display calibration; Psychophysics ; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration |
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An improved method for visual gamma correction is developed for LCD displays to increase the accuracy of digital colour reproduction. Rather than utilising a photometric measurement device, we use observ- ers’ visual luminance judgements for gamma correction. Eight half tone patterns were designed to gen- erate relative luminances from 1/9 to 8/9 for each colour channel. A psychophysical experiment was conducted on an LCD display to find the digital signals corresponding to each relative luminance by visually matching the half-tone background to a uniform colour patch. Both inter- and intra-observer vari- ability for the eight luminance matches in each channel were assessed and the luminance matches proved to be consistent across observers (DE00 < 3.5) and repeatable (DE00 < 2.2). Based on the individual observer judgements, the display opto-electronic transfer function (OETF) was estimated by using either a 3rd order polynomial regression or linear interpolation for each colour channel. The performance of the proposed method is evaluated by predicting the CIE tristimulus values of a set of coloured patches (using the observer-based OETFs) and comparing them to the expected CIE tristimulus values (using the OETF obtained from spectro-radiometric luminance measurements). The resulting colour differences range from 2 to 4.6 DE00. We conclude that this observer-based method of visual gamma correction is useful to estimate the OETF for LCD displays. Its major advantage is that no particular functional relationship between digital inputs and luminance outputs has to be assumed. |
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Elsevier |
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DAG |
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Admin @ si @ XFK2011 |
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1815 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Embedding of Graphs with Discrete Attributes Via Label Frequencies |
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2013 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
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3 |
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1360002-1360029 |
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Discrete attributed graphs; graph embedding; graph classification |
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Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. |
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DAG |
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Admin @ si @ GVB2013 |
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2305 |
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Author |
Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
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Title |
Multimodal page classification in administrative document image streams |
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2014 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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17 |
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4 |
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331-341 |
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Digital mail room; Multimodal page classification; Visual and textual document description |
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In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 |
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Admin @ si @ RFK2014 |
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2523 |
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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
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Year |
2022 |
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Big Data Research |
Abbreviated Journal |
BDR |
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29 |
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100332 |
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Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
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Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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August 28, 2022 |
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DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
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Admin @ si @ RBG2022a |
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3718 |
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