CAJ | 학술논문

提出了一种基于 Contourlet Ⅱ KPCA-Krawtchouk 矩的印章图像配准算法。首先对印章图像进行 Contourlet 分解并提取低频分量，然后利用 KPCA 提取低频分量的主成分，并计算其 Krawtchouk 矩不变量，构成描述关键点的特征向量，最后计算关键点特征向量之间的欧氏距离找出相匹配的关键点对。采用 Logistic 映射混沌粒子群算法寻找最优阈值，大大加快了算法运行速度。实验结果表明，该算法不仅配准结果精确，且运行时间明显减少。
제출료일충기우 Contourlet Ⅱ KPCA-Krawtchouk 구적인장도상배준산법。수선대인장도상진행 Contourlet 분해병제취저빈분량，연후이용 KPCA 제취저빈분량적주성분，병계산기 Krawtchouk 구불변량，구성묘술관건점적특정향량，최후계산관건점특정향량지간적구씨거리조출상필배적관건점대。채용 Logistic 영사혼돈입자군산법심조최우역치，대대가쾌료산법운행속도。실험결과표명，해산법불부배준결과정학，차운행시간명현감소。
Seal image registration based on KPCA-Krawtchouk moments in contourlet domain is proposed . Firstly , the seal image is decomposed by contourlet and extract the low frequency components by KPCA, then calculate Krawtchouk moment invariants of that, constitute feature vector which can decrypt the key points , finally , calculation the Euclidean distance between key features vectors to find matches the key point pairs . At the same time , using Logistic map chaotic particle swarm algorithm to find the optimal threshold , can accelerate the algorithm speed greatly . A large number of experimental results show that the image registration based on KPCA-Krawtchouk moments in contourlet domain algorithm registration result is very accurate , and the running time reduces about 60%.