Image matching is one of the most important techniques in intelligent systems and is widely applied in many fields.
Firstly, based on integrated feature congruency, interesting target detection algorithm in complex natural backgrounds
images is studied in this paper. By detecting the abrupt changes, we can detect interesting target areas. In this paper, the
local image information is obtained by logGabor filter banks, and is represented by a collection of separate features. The
integrated features consist of some separable significant features. The integrated feature congruency model is presented
based on the integrated feature. We gain improved integrated feature congruency model by compensating noise. Then,
we get a new kind of phase-based image matching method (PIM) by combining this model and Rotation Invariant Phase
Only Correlation (RIPOC) algorithm. Experimental results show that the PIM algorithm is effective in detecting
interesting targets and locating the matching targets exactly. This algorithm is invariant to image illumination, contrast,
rotation and scaling. And this model is robust, general and accords with the human vision system (HVS) characteristics.