傅立叶描述子识别物体的形状

傅立叶描述子是分析和识别物体形状的重要方法之一,利用基于曲线多边形近似的连续傅立叶变换方法计算傅立叶描述子,并通过形状的主方向消除边界起始点相位影响的方法,定义了新的具有旋转,平移和尺度不变性的归一化傅立叶描述子,与使用离散傅立叶变换和模归一化的传统傅立叶描述了相比,新的归一化傅立叶描述于同时保留了模与相位特性,因此能够更好地识别物体的形状,实验表明这种新的归一化傅立叶描述子比传统的傅立叶描述子能够更加高效,准确地识别物体的形状。

USING FOURIER DESCRIPTORS TO RECOGNIZE OBJECT''''S SHAPE

Fourier descriptor is an important method used in shape analysis and recognition. In this paper, an efficient and accurate method is used to calculate Fourier descriptors of the shape by polygonal approximation, and a method of removing the phase effect of the start point of the border through a shape's main orientation is presented. A new normalized Fourier descriptor is defined, which is invariant in the rotation, translation and scale transform. The experiments show the accuracy and efficiency of the new normalized Fourier descriptors in shape recognition.