Automated tablet quality assurance and identification for hospital pharmacies

The tablet quality checking and identification in hospital pharmacies is done manually and does not use any automated solution. Manual sorting and handling makes this activity laborious and error-prone. This paper describes a low cost solution that is characterised by a small size of the infrastructure involved. Discussed are design and implementation details of Tablet Inspection System based on Machine Vision. The described process uses a dedicated sequence of operation to perform dispensing, scanning and sorting using mini factory setup. Machine Vision System uses a novel Genetic Evolution algorithm. The algorithm provides robust and scalable output. Due to its versatile nature and easy shape recognition ability the approach can be easily adapted to a large variety of medical tablets. The proposed solution attempts to follow the concept of single objective with multiple optima in GA that is designed to scan multiple number of tablets in one cycle of operation

Genetic algorithms for clustering in machine vision

The paper presents a genetic algorithm for clustering objects in images based on their visual features. In particular, a novel solution code (named Boolean Matching Code) and a correspondent reproduction operator (the Single Gene Crossover) are defined specifically for clustering and are compared with other standard genetic approaches. The paper describes the clustering algorithm in detail, in order to show the suitability of the genetic paradigm and underline the importance of effective tuning of algorithm parameters to the application. The algorithm is evaluated on some test sets and an example of its application in automated visual inspection is presented