CAJ | 학술논문

形状表示是模式识别和计算机视觉中最重要的研究内容之一。针对传统形状表示算法对形状的整体特征和细节信息不能同时描述、通用性不高的问题，提出了一种基于高斯多尺度分析下的椭圆傅里叶描述算子。提出的算法利用高斯函数与目标形状的复坐标函数进行卷积，通过选择高斯曲线的参数，将形状的边界信息呈现到不同的尺度空间之中；利用椭圆傅里叶变换将其展开得到表示该形状的特征向量。实验结果表明，该方法的优点在于描述同类形状时，特征向量之间的相关系数高，具有很好的平移、旋转以及尺度不变性；在描述不同类形状时，相关系数低，有很强的形状区分能力。该方法在形状分类实验中也有较高的检索准确率。
형상표시시모식식별화계산궤시각중최중요적연구내용지일。침대전통형상표시산법대형상적정체특정화세절신식불능동시묘술、통용성불고적문제，제출료일충기우고사다척도분석하적타원부리협묘술산자。제출적산법이용고사함수여목표형상적복좌표함수진행권적，통과선택고사곡선적삼수，장형상적변계신식정현도불동적척도공간지중；이용타원부리협변환장기전개득도표시해형상적특정향량。실험결과표명，해방법적우점재우묘술동류형상시，특정향량지간적상관계수고，구유흔호적평이、선전이급척도불변성；재묘술불동류형상시，상관계수저，유흔강적형상구분능력。해방법재형상분류실험중야유교고적검색준학솔。
Shape representation is one of the most important research contents in the field of pattern recognition and com-puter vision. Considering that the traditional shape representation algorithm cannot describe the whole characteristics and the detail information well at the same time and the versatility is also not desired, a new elliptic Fourier descriptor based on the Gaussian multiscale analysis is proposed in this paper. This algorithm makes convolution between Gauss function and complex coordinate function of the target object. Through the choice of parameters of Gaussian curve, the boundary information can be presented into different scale spaces. And then it can get a shape characteristic vector through elliptic Fourier transform. When this method is used to describe the shapes of a same kind, the correlation coefficients between the characteristic vectors are very high. On the contrary, the coefficients are very low when describing the shapes of different kinds. The experimental results show that this method has good translation, rotation and scale invariance, strong shape dis-crimination ability and more accurate results in the shape classification and recognition experiment.