为了克服对高相似度图象的误判，从形态学角度提出一种基于联合变换相关器的图象相似度定义，并将改善高相似度图象识别的方法划分为非原理性改进和原理性改进两大类，其中，为进行非原理性改进，提出将结构光模式应用于联合图象编码；为进行原理性改进，对联合图象采用基于形态学击中击不中变换的互被编码算法，通过对工业零件基本形状的仿真识别来区分高相似度图象，结果证明，该方法是有效的。

Joint transform correlator based on opto-electronic hybrid processing is discussed to recognize high similarity industrial parts images. Experiment system of joint transform correlator is presented. Muitilevel simulating targets based on basic shapes are constructed in order to simulate different industrial images. Joint image similarity degree for joint transform correlator is defined based on morphology method. Joint images are graded based on image similarity degree. Two kinds of method based on principle improment and non-principle improment are refined for high similarity image recognition in order to reduce false decision. Octagon with holes, octagon and pentagon are selected as input images from basic shapes and pentagon is selected as reference image. Structure light pattern is put forward to encode joint images for non-principle improment. Complementary encoding method based on morphological hit-or-miss transform is applied to code joint images for principle improment. Distinct effect is acquired with industrial basic shapes recognition by computer simulation. The results indicate that we can recognize high similarity images by raising JTC recognize ability or reducing image similarity degree.