卟啉传感阵列图像特征值自动提取方法

针对卟啉传感阵列(PSA)图像,设计了一种特征提取算法。该算法首先利用radon变换的方法实现了PSA图像倾斜自动校正;而后通过二值投影与数学统计学结合的方法来进行网格划分,根据网格划分的卟啉点区域对图像进行局部阈值分割,从而定位出卟啉点区域,最后采用比例半径法从已定位的卟啉点区域内提取出每个卟啉点的特征。该算法解决了弱信号卟啉点识别难的问题,提取的特征值最大程度的反映了卟啉点的信息。通过对NH3、异戊二烯、庚醛等气体进行试验,结果表明该算法能够很好的提取出PSA图像中卟啉点的特征值。

Automatic feature extraction algorithm for porphyrin sensor array images

A feature extraction algorithm for porphyrin sensor array (PSA) images is presented. At the first step, it achieves an automatic tilt correction for PSA images based on radon transform. Then automatic mesh generation is applied to PSA images based on the method of combining projection binarization and mathematical statistics. The areas of porphyrin spots are locating by local threshold segmentation for the images which have achieved mesh generation. Finally, feature values of porphyrin spots are extracted by radius ratio method from the porphyrin spot areas. The algorithm solves the problem that identification of weak signal spots is quite difficult, and the extracted feature values are the maximum point to reflect the information of porphyrin spots. By ammonia, isoprene, aldehydes and other gases tests, the results show that the algorithm can effectively extract feature values of porphyrin spots in PSA images.