TexRet

Author(s):  
Girish Katkar ◽  
Pravin Ghosekar

The TEXRET-System, a texture retrieval system based on soft-computing technologies is being developed. The importance of this kind of system is increasing due to the massive access to digital image databases, which also demand the existence of systems that can understand human high-level requests. The TEXRET system has the following features: (i) direct access from the Internet, (ii) high interactivity, (iii) texture retrieval using human-like or fuzzy description of the textures, (iv) content-based texture retrieval using user-feedback, and (v) synthesis or generation of the requested textures when these are not found in the database, which allows a growing of the database. One of the main system features is synthesis of the requested textures when these are not found in the database, which allows a growing of the database. Missing textures are synthesized interactively using Markov Random Fields and interactive genetic algorithms. This paper is centered on the texture synthesis of the textures.

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.


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