Object Detection with Gabor Filters and Cumulative Histograms

This paper proposes an algorithm for segmentation and extracting an object region by using Gabor filters. Gabor filters are exploited to extract spatial frequency in some orientations, and not only the outputs of Gabor filters but also color informations are used to construct the features at each image pixel. The criterion is devised so as to consider the similarity, the region size and the region shape factors in order to efficiently merge the features. In general, a complex object may be segmented into multiple regions. However for purpose of detecting such complex object, we represent the object region by the normalized cumulative histogram of features. From experimental results, it is found that the proposed algorithm is able to efficiently detect the object regions such as cars in images of usual traffic scenes.