A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each su...A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satis- factorily when compared to former algorithms.展开更多
Image fusion should consider the priori knowledge of the source images to be fused, such as the characteristics of the images and the goal of image fusion, that is to say, the knowledge about the input data and the ap...Image fusion should consider the priori knowledge of the source images to be fused, such as the characteristics of the images and the goal of image fusion, that is to say, the knowledge about the input data and the application plays a crucial role. This paper is concerned on multiresolution （MR） image fusion. Considering the characteristics of the multisensor （SAR and FLIR etc） and the goal of fusion, which is to achieve one image in possession of the contours feature and the target region feature. It seems more meaningful to combine features rather than pixels. A multisensor image fusion scheme based on K-means duster and steerable pyramid is presented. K-means cluster is used to segment out objects in FLIR images. The steerable pyramid is a multiresolution analysis method, which has a good property to extract contours information at different scales, Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficiency of the proposed scheme.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60272031), Educational Department Doctor Foundation of China (No. 20010335049), and Zhejiang Provincial Natural ScienceFoundation (No. ZD0212), China
文摘A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satis- factorily when compared to former algorithms.
基金This project was supported by National "863" High Technology Research and Development Program of China(2001AA135091) National Science Foundation of China +2 种基金Shanghai Key Scientific Project (02DZ15001) China PH.D. DisciplineSpecial Foundation (20020248029) China Aviation Science Foundation (02D57003) .
文摘Image fusion should consider the priori knowledge of the source images to be fused, such as the characteristics of the images and the goal of image fusion, that is to say, the knowledge about the input data and the application plays a crucial role. This paper is concerned on multiresolution （MR） image fusion. Considering the characteristics of the multisensor （SAR and FLIR etc） and the goal of fusion, which is to achieve one image in possession of the contours feature and the target region feature. It seems more meaningful to combine features rather than pixels. A multisensor image fusion scheme based on K-means duster and steerable pyramid is presented. K-means cluster is used to segment out objects in FLIR images. The steerable pyramid is a multiresolution analysis method, which has a good property to extract contours information at different scales, Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficiency of the proposed scheme.
