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This paper presents a new algorithm for nighttime contrast enhancement. The proposed algorithm modifies the traditional histogram equalization algorithm to maintain the color information of the original nighttime images. The algorithm has a low computational cost that makes it suitable for real-time hardware implementation. In addition, its efficient hardware implementation is detailed on a Xilinx...
With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology...
Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
This work proposes a decision support technology to minimize risks while choosing among competitive investment projects. The technology combines two fuzzy-statistical methods, providing two stages of investment projects' evaluation. At the first stage preliminary selection of projects with small risks is made on the basis of the expertons method [2],[3]. The second stage makes more precise decisions...
The success of any statistical steganalysis algorithm depends on the choice of features extracted and the classifier employed. This paper proposes steganalysis using random forests (SURF) employing HCS (Huffman Code Statistics) features and FR Index (ratio of File size to Resolution). The proposed algorithm is validated over an image database of over 30,000 images spanning various sizes, resolutions,...
A blind color image steganalyzer is proposed, in which the features are extracted from Contourlet domain. Statistical features of Contourlet coefficients and cooccurrence metrics of subband images are used as features. For evaluating the proposed steganalysis method, some popular steganography methods such as OutGuess, JPHS, Model-based and Jsteg are used with payloads of 10% to 25%. To reduce the...
Statistically-based parsers for large corpora, in particular the Penn Tree Bank (PTB), typically have not used all the linguistic information encoded in the annotated trees on which they are trained. In particular, they have not in general used information that records the effects of derivations, such as empty categories and the representation of displaced phrases, as is the case with passive, topicalization,...
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