In contrast with machine vision, human can recognize an object from complex background with great flexibility. For
example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual
image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may
be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based
target recognition method by simulating the human recognition process. The component templates (equivalent to the
virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges
of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal
response in visual process. A phase correlation matching algorithm is then applied to match the templates with the
testing edge image. If all key component templates are matched with the examining object, then this object is recognized
as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars).
In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary
results show that the component-based recognition method is very promising.