The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples
|School||Harbin Institute of Technology|
|Course||Computer Science and Technology|
|Keywords||class Harr feature extraction AdaBoost the smallest enclosing ball the rejection class the SVM One the Class|
Currently in the world, There are large numbers of paper currency circulates every day, it is a tough work for staves to sort the paper currency in the banking sector. How to sort the paper currency quickly and correctly becomes very important in the banks. It makes the cumbersome work simply, fast and reliably to use the sensitive and accurate paper currency sorter. The technology is to process and recognize national paper currency. However, the paper currency sorter yielded home in the home market has low sales ratio, mostly because of recognition rate、other issues such as low reject ratio of rejection notes. How to improve the image recognition rate and reduce the error recognition ratio of reject types is currently a puzzle, but also a urgent problem on the way of continuing to study the technology of paper currency processing.In this paper, recognition methods of paper currency based on the Harr-like feature and the minimal ball including most of the same samples is proposed. The extraction of Harr-like feature extracted features different from the features extracted through other effectively feature extraction method, which is single and lack of flexibility, it can also reduce the dimension of feature and ensure the the ability of classification. On the other hand, the method of the minimal ball including most samples brings a new solution for the reject types which can’t be rejected by the other recognition methods.The Harr-like feature extraction brings forward four recognition methods of paper currency which include Harr-like Minus method in the range of the whole image、Harr-like Sum method in the range of whole image、Harr-like Minus method in the range of fixed gridding and Harr-like Sum method in the range of fixed gridding through modifying the original two-types recognition method、the feature selection of AdaBoost and recognition methods for multiple types. A appropriate method which shows best performance in the test can be found.The methods of minimal ball including most of the same samples while different types in different balls can exclude the reject samples which should be rejected, and so it can solve the problem that the original recognition methods can’t effectively reject the reject types. It’s proved that the method of One Class is equivalent the minimal ball including most samples, so we can use the method of One Class in SVM to realise it.Experiments show that: the extraction of Harr-like feature can extract features which include more rich information, and it can also be used to identify paper currency ,which can effectively raise the recognition rate; the minimum ball including most samples is also a effective way, which can solve the reject problem.