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Face classification using curvature-based multi-scale morphology

机译:使用基于曲率的多尺度形态学进行人脸分类

摘要

An image classification system uses curvature-based multi-scale morphology to classify an image by its most distinguishing features. The image is recorded in digital form. Curvature features associated with the image are determined. A structuring element is modulated based on the curvature features. The shape of the structuring element is controlled by making it a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. The structuring element modulated with the curvature features is superimposed on the image to determine a feature vector of the image using mathematical morphology. When this Curvature-based Multi-scale Morphology (CMM) technique is applied to face images, a high-dimensional feature vector is obtained. The dimensionality of this feature vector is reduced by using the PCA technique, and the low-dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance.
机译:图像分类系统使用基于曲率的多尺度形态学,通过其最鲜明的特征对图像进行分类。图像以数字形式记录。确定与图像关联的曲率特征。根据曲率特征调制结构元素。通过使其成为缩放因子和面部图像强度表面的主曲率的函数,来控制结构元素的形状。用数学特征将用曲率特征调制的结构元素叠加在图像上以确定图像的特征向量。当这种基于曲率的多尺度形态学(CMM)技术应用于人脸图像时,可以获得高维特征向量。通过使用PCA技术可减少此特征向量的维数,并使用增强型FLD模型(EFM)对低维特征向量进行分析,以实现出色的分类性能。

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