31273280
Adaptive-Hierarchical-Filtering Technique for Image Enhancement
This paper describes a novel adaptive-hierarchical-filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moire pattern during the reproduction of the image by different computer peripherals such as color copier, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moire distortion may result when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moire patterns in the frequency and spatial domain. Based on the analysis, a set of hierarchical filter is developed to suppress the moire artifacts and enhance the image adaptively. The hierarchical filter consists of a set of variable-length low-pass filters and high-pass filters. The low-pass filters have a nice inheritance of canceling aliased low frequency components (moire distortion). High-pass filtering is also applied to sharpen image edges. An image classifier is developed to determine that an edge is either a global true edge (for sharpening enhancement) or a local halftone's micro-structural edge (for LPF for moire reduction), so that adaptive filter technique can be applied to achieve the smooth transition between sharp edges and smooth halftone regions. Thus, we achieves overall high-quality output images.Experimental results have been shown the effectiveness of the presented technique that works well on wide combinations of above-mentioned 6 factors, which will be explained during the following sections, for high-quality magazine image reproduction.
26234971
A Color Consistency Algorithm Between Different Printers
Color consistency between different color peripherals su as printers, scanners and digital cameras is a well-kno problem. In this paper, a color consistency algorith between different printers is proposed. Each printer has its own characteristics. The co behavior of a printer can be modeled by a nonline characteristic function. We approximate the characteris function by a three-dimensional look-up table. Then, transfer function between two printers can be construc based on their characteristic functions. The transfer funct is to compensate one’s color behavior and simula another’s color behavior. After applying the transfe function, the color difference ( ∆E) and hue difference can be reduced to two to three times smaller.
29878206
An architecture for interoperable program understanding tools
Program understanding tools are currently not interoperable, leading researchers to waste significant resources reinventing already existing tools. Even commercial environments that have been designed to support the construction of program understanding tools have serious flaws in this regard. This paper discusses CORUM (Common Object-based Re-engineering Unified Model), an architecture to support interoperability between program understanding tools, and it provides several examples of CORUM's use in the construction of new tools for concept recognition and program visualization.
45863395
First Order Rough Logic-Revisited
Based on axiomatic rough set theory, first order rough logic was developed earlier. In this paper, a new model theory for that logic is introduced. With this new semantic, first order rough logic is shown to be equivalent to first order S5, and hence consistent and complete.
34342377
Database Mining on Derived Attributes
Selecting a desirable set of attributes is essential in data mining. In this paper, we develop the notion of universal model, on which a complete set of all possible derived attributes can be generated. Then, for any data mining task, the relation can select desirable attributes by interpreting them from the universal model. The development of the universal model is based on the relation lattice, which was initiated by T. T. Lee around 1983. However, we define the lattice differently. The relation lattice theory, as rough sets, is based on the partitions induced by attributes.
10629196
A New Rough Sets Model Based on Database Systems
Rough sets theory was proposed by Pawlak in the early 1980s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.
15380898
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
From the processing point of view, data mining is machine derivation of interesting properties (to human) from the stored data. Hence, the notion of machine oriented data modeling is explored: An attribute value, in a relational model, is a meaningful label (a property) of a set of entities (granule). A model using these granules themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. The model provides a good database compaction and data mining environment. For moderate size databases, finding association rules, decision rules, and etc., can be reduced to easy computation of set theoretical operations of granules. In the second part, these notions are extended to real world objects, where the universe is granulated (clustered) into granules by binary relations. Data modeling and mining with such additional semantics are formulated and investigated. In such models, data mining is essentially a machine “calculus” of granules-granular computing.