Robust and Effective Component-based Banknote Recognition by SURF Features.
Robust and Effective Component-based Banknote Recognition by SURF Features.
复制标题
DOI：
10.1109/wocc.2011.5872294
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Tian Y
中科院分区：
文献类型：
--
作者：
Hasanuzzaman FM;Yang X;Tian Y
基于摄像头的计算机视觉技术能够帮助视障人士自动识别纸币。一个好的用于盲人或视障人士的纸币识别算法应该具有以下特点：1）100%的准确率，2）对不同环境和遮挡的各种条件具有鲁棒性。现有的纸币识别算法大多局限于在特定条件下工作。在本文中，我们提出了一个基于组件的框架，通过使用加速鲁棒特征（SURF）的钞票识别。基于组件的框架是有效的，在收集更多的类特定的信息和鲁棒性，在处理部分遮挡和视点变化。此外，SURF的评估表明，它在处理背景噪声，图像旋转，规模和照明变化的有效性。为了验证所提出的方法的鲁棒性和通用性，我们已经收集了大量的钞票数据集，从各种条件，包括遮挡，混乱的背景，旋转，照明，缩放和视点的变化。该算法在我们具有挑战性的数据集上实现了100%的识别率。
Camera-based computer vision technology is able to assist visually impaired people to automatically recognize banknotes. A good banknote recognition algorithm for blind or visually impaired people should have the following features: 1) 100% accuracy, and 2) robustness to various conditions in different environments and occlusions. Most existing algorithms of banknote recognition are limited to work for restricted conditions. In this paper we propose a component-based framework for banknote recognition by using Speeded Up Robust Features (SURF). The component-based framework is effective in collecting more class-specific information and robust in dealing with partial occlusion and viewpoint changes. Furthermore, the evaluation of SURF demonstrates its effectiveness in handling background noise, image rotation, scale, and illumination changes. To authenticate the robustness and generalizability of the proposed approach, we have collected a large dataset of banknotes from a variety of conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves 100% recognition rate on our challenging dataset.