A Tabu Search Algorithm for General Threshold Visual Cryptography Schemes

Abstract

In Visual Cryptography Schemes (VCSs), for message n transparencies are generated, such that the original message is visible if any k of them are stacked. VCS especially for large values of k and n, the pixel expansion’s reduction and enhancement of the recovered images’ display quality continue to be critical issues. In addition to this, it is challenging to develop a practical and systematic approach to threshold VCSs. An optimization-based pixel-expansion-free threshold VCSs approach has been proposed for binary secret images’ encryption. Along with contrast, blackness is also treated as a performance metric for assessing the recovered images’ display quality. An ideally secure technique for a secret image’s protection through its partition into shadow images (known as shadows) is the Visual Secret Sharing (VSS) scheme. Acquirement of a smaller shadow size or a higher contrast is the VSS schemes’ latest focus. The white pixels’ frequency has been utilized to demonstrate the recovered image’s contrast in this work. While the Probabilistic VSS (ProbVSS) scheme is non-expansible, it can also be readily deployed depending upon the traditional VSS scheme. Initially, this work has defined the problem as a mathematical optimization model such that, while contingent on blackness and density-balance constraints, there is the maximization of the recovered images’ contrast. Afterward, an algorithm dependent on the Tabu Search (TS) is devised in this work for this problem’s resolution. Multiple complicated combinatorial problems have been successfully resolved with the powerful TS algorithm. Moreover, this work has attempted to bolster the contrast through the density-balance constraint’s slight relaxation. Compared to the older techniques, the proposed optimization-based approach is superior regarding the recovered images’ display quality and the pixel expansion factor from the experimental outcomes.

Vehicle logistics intermodal route optimization based on Tabu search algorithm

“…In order to increase the security of secret files, Rao et al proposed an image encryption method using pixel expansion threshold, and used Tabu search to improve this method. Results showed that this algorithm can effectively solve complex combination problems 12 . Maha• et al proposed a Tabu search with time window to optimize the path at different time points in urban tourism in order to improve the vehicle path planning problem in urban tourism road network.…”

“…U r is the unit cost of vehicle pickup and reshipment at the railway station, U w is the unit cost of vehicle pickup and reshipment at the port, and U d is the unit cost of vehicle pickup and reshipment at the distribution center. In addition to the transportation cost calculation formula, the income calculation formula is also established, as shown in formula (12).…”

“…In formula (12), E Rs refers to the income of small vehicle railway transportation unit, E Ws is the income of small vehicle waterway transportation unit, E Gs is the income of small vehicle short-distance connection transportation unit, E k is the income of main engine plant pick-up and reshipment unit, E r is the income of railway station pick-up and reshipment unit, E w is the income of port pick-up and reshipment unit, and E d is the income of distribution center pick-up and reshipment unit. The cost and income of a single line can be obtained through formula (11) and formula (12), and the maximum profit objective function of the whole network of vehicle logistics multimodal transport network can be obtained by upward derivation, as shown in formula (13). ( 11) www.nature.com/scientificreports/…”

The Automatic Brain Tumor Segmentation Based on MRI Using Optimal Morphology Thresholding Methods

“…Primarily, the brain tumors are medically diagnosed. Thus, the algorithms for the automatic segmentation of the images applied to computerized tomography (CT) scan [1], positron emission tomography (PET) scan [2,3] and magnetic resonance imaging (MRI) [4][5][6][7][8][9][10][11][12] for segmenting the images of the brain tumors from the images of CT, PET and MRI, respectively. There are preprocessing and post processing developments.…”