A real-time embedded solution for skew correction in banknote analysis
Several industrial applications do require embedded solutions both for compacting the hardware occupation and reducing energy consumption, and for achieving high speed performance. This paper presents a computer vision system developed for correcting image skew in applications for banknote analysis and classification. The system must be very efficient and run on a fixed-point DSP with limited computational resources. Consequently, we propose three innovative improvements to basic and general-purpose image processing techniques that can be helpful in other computer vision applications on embedded devices. In particular, we address: a) an efficient labeling with an union-find approach for hole filling, b) a fast Hough transform implementation, and c) a very high-speed estimation of affine transformation for skew correction. The reported results demonstrate both the accuracy and the efficiency of the system, also in presence of severe skew. In terms of efficiency, the computational time is reduced of about two orders of magnitude.