基于平移、转动和大小变动对象的不变性识别

Translation, Rotation and Scale-Invariant Object Recognition

IOR(不变对象识别)是基于对象在平移、转动、大小变化等情况下的识别方法。该方法分两步:第一步,预处理阶段,通过归一化转动惯量和采用一种新的编码方法来抽取对象的不变拓扑特征,并存入一个数据库中。第二步,识别阶段,采用了一种亲笔最近相邻算法(HNN)。首先自学习预处理得到的数据,并得到对象的总的特征。再通过HNN算法来识别对象。该方法用于实际车牌识别,能得到满意的结果。

A method for object recognition, invariant under translation, rotation, and scaling is addressed. The first step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in which vectors obtained in the preprocessing step are used as inputs to it .The algorithm is tested in character recognition, using the 26 upper case letters of the alphabet. Only four different orientations and one size (for each letter) are used for training. Recognition is tested with 17 different size and 14 rotations. The results are encouraging, since we achieved 98%correct recognition. Tolerance to boundary deformations and random noise is tested. Results for character recognition in 搑eal?images of car plates are presented as well.