摘要：The goal of this study is to build a system that detects and classifies the car objects amidst background clutter and mild occlusion. This study addresses the issues to classify objects of real-world images containing side views of cars with cluttered background with that of non-car images with natural scenes. The threshold technique with background subtraction is used to segment the background region to extract the object of interest. The background-segmented image with region of interest is divided into equal sized blocks of sub-images. The spectral texture features are extracted from each sub-block. The features of the objects are fed to the back-propagation neural classifier. Thus the performance of the neural classifier is compared with various categories of block size. Quantitative evaluation shows improved results of 85.5%. A critical evaluation of present approach under the proposed standards is presented.
