Abstract—Nowadays, using image processing techniques on the image taken by camera at the top of production line for detection of faulty products is widespread. The quality of images can decline due to the distance between camera and production line while trying to get whole image of the bigger products. Therefore, in this study we employed camera array instead of only one camera and placed it at a nearer position to production line. As the images taken from cameras in camera array contain specific parts of the whole product, it is required to stitch images and obtain single image of the product before analyze it. Production line speed or the product position can be vary so it is required to find the optimal stitching points in order to get single image. As an optimization technique micro genetic algorithm (μGA) which refers to Genetic Algorithm (GA) with small population size and re-initialization process is popular for its proper and fast solutions. In this paper, we have developed a method using μGA known by its high convergence rate and low termination possibility at a local optimum to be used in image stitching process. Using μGA known by its high convergence rate, less need of computational source and rare pre-mature solution provided us optimal and fast solutions. Experimental results show that μGA, which outperforms conventional GA, gives good results in a reasonable time and it can be used in image stitching process as an alternative.
Index Terms—image stitching, micro genetic algorithm, genetic algorithm, camera arrays, production line
Cite: Hasan Yetis, Mehmet Baygin, and Mehmet Karakose, "A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines," Journal of Image and Graphics, Vol. 5, No. 1, pp. 20-24, June 2017. doi: 10.18178/joig.5.1.20-24
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