基于Gabor特征和增强Fisher模型的目标检测和识别

Objects Detection and Classification Based on Gabor Features and Enhanced Fisher Discriminant Model

研究基于Gabor特征和增强Fisher线性判别模型(EFM)的目标检测和识别问题。用Gabor滤波器族对样本和场景图像进行分解,得到高维特征向量。然后利用主成分分析(PCA)将高维特征向量变换到低维空间,根据新的特征幅值检测场景图像中可能存在的车辆目标,并对检测到的目标用EFM进行特征分析后,与样本训练得到的特征进行相似性分类。实验证明本文算法在降低特征维数的同时,仍能较好地识别车辆目标。本文还对车辆个数和位置确定等问题也提出解决方法,并用实验对算法进行验证。

An approach for detection and classification of objects based on Gabor features and enhanced fisher discriminant model （ EFM ） is presented in this paper . Decomposed by Gabor filters , the dimensions of Gabor features of object images and models are very large . Principal component analysis （ PCA ） is used to extract the master components and reduce dimensions of Gabor features. Whether there are vehicle objects or not is primarily justified by the magnitude of the Gabor features. If candidate object is detected, EFM is carried out to compare its features to those of models to determine which one it belongs to-vehicles or back ground. The experiments prove the proposed arithmetic can get good results while reducing the feature dimensions.Furthermore, arithmetics for determining vehicle＇s number and positions are also discussed. And the experimental results also validate their feasibility.