An enhanced image binarization method incorporating with Monte-Carlo simulation

We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.

Binary Fruit Fly Swarm Algorithms for the Set Covering Problem

Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.