Fast Affine Transform for Real-Time Machine Vision Applications

In this paper, we have proposed a fast affine transform method for real-time machine vision applications. Inspection of parts by machine vision requires accurate, fast, reliable, and consistent operations, where the transform of visual images plays an important role. Image transform is generally expensive in computation for real-time applications. For example, a transform including rotation and scaling would require four multiplications and four additions per pixel, which is going to be a great burden to process a large image. Our proposed method reduces the complexity substantially by removing four multiplications per pixel, which exploits the relationship between two neighboring pixels. In addition, this paper shows that the affine transform can be performed by fixed point operations with marginal error. Two interpolation methods are also tried on top of the proposed method in order to test the feasibility of fixed point operations. Experimental results indicated that the proposed algorithm was about six times faster than conventional ones without any interpolation and five times faster with bilinear interpolation.