Coin recognition using image abstraction and spiral decomposition

This paper presents a novel approach for coin image recognition. The approach enables measuring the similarity between full color multi-component coin images and needs no cost intensive image segmentation. A novel procedure, based on strong edges of the coin image, is exploited to derive an abstract image. Spiral decomposition of pixels in the abstract image is then used to extract a set of compact and effective features. The query set and the image database used in the tests are scanned, photographed, or collected from the web. The results are compared with three other well-known approaches within the literature. Experimental results show significant improvement in the Recall ratio using the proposed features.