为了提高虹膜定位的精度和准确性,从而进一步提高虹膜识别系统的识别率,提出了一种基于矢量场卷积(Vector Field Convolution,VFC)的虹膜定位算法,用于精确定位虹膜内边界。首先利用最小灰度平均值法自动确定VFC模型的初始化轮廓,在活...为了提高虹膜定位的精度和准确性,从而进一步提高虹膜识别系统的识别率,提出了一种基于矢量场卷积(Vector Field Convolution,VFC)的虹膜定位算法,用于精确定位虹膜内边界。首先利用最小灰度平均值法自动确定VFC模型的初始化轮廓,在活动轮廓内外力作用下实现虹膜内边界定位;然后对于虹膜外边界,采用改进的Daugman算法进行定位。利用多个虹膜图库进行了大量实验,并与几种常见的虹膜定位算法进行了比较,实验结果表明:该方法定位准确度更高,虹膜内边界定位更接近真实边界,定位结果有明显改善。展开更多
Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it caus...Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.展开更多
文摘为了提高虹膜定位的精度和准确性,从而进一步提高虹膜识别系统的识别率,提出了一种基于矢量场卷积(Vector Field Convolution,VFC)的虹膜定位算法,用于精确定位虹膜内边界。首先利用最小灰度平均值法自动确定VFC模型的初始化轮廓,在活动轮廓内外力作用下实现虹膜内边界定位;然后对于虹膜外边界,采用改进的Daugman算法进行定位。利用多个虹膜图库进行了大量实验,并与几种常见的虹膜定位算法进行了比较,实验结果表明:该方法定位准确度更高,虹膜内边界定位更接近真实边界,定位结果有明显改善。
文摘Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.
