Automatic extraction of contour lines and feature points from profile images

N. Okamoto, W. Chen, N. Iida, T. Minami
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引用次数: 4

Abstract

The paper proposes a new extraction algorithm for contour lines and feature points from profile images for automatic personal identification. First the authors input a side view of a human head within a dark background under uniform lighting conditions by a CCD camera, then transform the input to a color difference signal image. After enhancing edges of the image using Sobel operators they binarize the luminance level of each pixel and portray a black profile in a white background. Next they differentiate the black profile, binarize the differentiated results, and depict a contour line using a thinning operator. Finally they encode the extracted contour line using Freeman's chain code and, using the encoded data, calculate the digital curvature in concave sections of the contour line and determine the concave feature points. Next they draw straight lines connecting the adjacent feature points and specify the convex feature points on the contour line which are farthest from the straight lines. They can obtain feature points of the profile automatically and reliably.
轮廓线和特征点的自动提取
提出了一种新的轮廓线和特征点提取算法,用于人脸自动识别。首先,作者通过CCD相机在均匀照明条件下输入黑暗背景下的人体侧视图,然后将输入转换为色差信号图像。在使用Sobel算子增强图像的边缘后,他们对每个像素的亮度水平进行二值化,并在白色背景下描绘黑色轮廓。接下来,他们对黑色轮廓进行微分,对微分结果进行二值化,并使用细化算子描绘等高线。最后利用Freeman链编码对提取的轮廓线进行编码,利用编码后的数据计算轮廓线凹段的数字曲率,确定凹特征点。然后绘制连接相邻特征点的直线,并在等高线上指定距离直线最远的凸特征点。它们能够自动、可靠地获取轮廓的特征点。
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