Description

Bank notes recognition for visually impaired people has been done by automatic method. This new approach can efficiently recognize the bank notes with features. With this new algorithm provides high accuracy: high true recognition rate and low false recognition rate, also provides high efficiency and recognition banknotes quickly. To make the system robust, speeded up robust features (SURF) algorithm proposed. SURF (Speeded up Robust Features) is a robust local feature detector, that can be used in computer vision tasks like object recognition or 3D reconstruction. SURF is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images. It uses an integer approximation to the determinant of Hessian blob detector, which can be computed extremely quickly with an integral image (3 integer operations). SURF descriptor is based on similar properties, with a complexity stripped down even further. The first step consists of fixing a reproducible orientation based on information from a circular region around the interest point. Then, we construct a square region aligned to the selected orientation, and extract the SURF descriptor from it. The proposed algorithm has been evaluated by dataset to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, and further by blind subjects. For feature description, SURF uses Wavelet responses in horizontal and vertical direction (again, use of integral images makes things easier).