Problem statement:The matching problem of complex objects is one of the most difficult task in the pattern recognition field. These problems are made difficult by seemingly infinite varieties of shapes and classes which are used. The difficulties are related to absolute shape measurement, given the impossibility of directly mapping shapes, as such, into a feature space. Approach: In this study, an object was modeled using boundaries pixel distance. The invariant has been resulted from the distance of each boundaries pixel to their central point. By performing linear regression on each set of sorted distances, a unique set of numerical features from the coefficients of this linear function has been produced. This unique set of numerical values is then proposed as an object's features. Results: The experiments show that the coefficient of linear function from boundaries' distance plot of each object has produced better recognition than polynomial function of degree more than one. Conclusion/Recommendations: More than 200 hundreds trademark's images have been tested and almost 90% of successful rate of accuracy has been achieved.
Abstract-The Traveling salesman problem (TSP) is to find a tour of a given number of cities (visiting each city exactly once) where the length of this tour is minimized. Testing every possibility for an N city tour would be N! Math additions. Genetic algorithms (GA) and Memetic algorithms (MA) are a relatively new optimization technique which can be applied to various problems, including those that are NPhard. The technique does not ensure an optimal solution, however it usually gives good approximations in a reasonable amount of time. They, therefore, would be good algorithms to try on the traveling salesman problem, one of the most famous NP-hard problems. In this paper I have proposed a algorithm to solve TSP using Genetic algorithms (GA) and Memetic algorithms (MA) with the crossover operator Edge Assembly Crossover (EAX) and also analyzed the result on different parameter like group size and mutation percentage and compared the result with other solutions.Keywords-NP Hard; GA(Genetic algorithms); TSP(Traveling salesman problem); MA(Memetic algorithms); EAX(Edge Assembly Crossover).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.