CAJ | 학술논문

为了对尺度和旋转变换下的纹理图像进行正确的分类, 将 Radon 变换和 V-系统相结合, 提出一种纹理分类的算法. 首先利用Radon变换将图像的旋转化为平移, 再对Radon变换后的图像进行V-变换; 利用V-系统的多小波特性, 经过一系列的降采样分解过程得到图像在 V-系统下的各层次能量表达, 并将这些能量作为纹理图像的特征描述. 由于 V-系统的多小波特性以及 Radon 变换对旋转的消除, 使得文中的特征描述在图像的放缩和旋转变换下有较强的鲁棒性. 在通用纹理数据库中的纹理分类实验结果表明了该算法的优越性能.
위료대척도화선전변환하적문리도상진행정학적분류, 장 Radon 변환화 V-계통상결합, 제출일충문리분류적산법. 수선이용Radon변환장도상적선전화위평이, 재대Radon변환후적도상진행V-변환; 이용V-계통적다소파특성, 경과일계렬적강채양분해과정득도도상재 V-계통하적각층차능량표체, 병장저사능량작위문리도상적특정묘술. 유우 V-계통적다소파특성이급 Radon 변환대선전적소제, 사득문중적특정묘술재도상적방축화선전변환하유교강적로봉성. 재통용문리수거고중적문리분류실험결과표명료해산법적우월성능.
To classify the scaled and rotated texture images correctly, this paper proposes a new algorithm for texture classification by combining Radon transform and the V-system. We firstly use the Radon trans-form to convert the image rotation into the image translation, and then apply the V-transform on the image obtained after Radon transform. The energies of the image on different levels under the V-system are ex-pressed by performing a series of downsampling process due to the multi-wavelet characteristics of the V-system. These obtained energies are used as the texture feature description. The feature description method in this paper is robust to the image scaling and rotation because of the multi-resolution characteris-tics of the V-system and elimination of rotation by applying Radon transform. Results of the experiments conducted on the standard texture datasets show that the proposed algorithm provides superior performance.