Finding an object in images with orientation invariance has many applications in computer vision and pattern recognition. Template matching is a typical approach for finding objects. However, template matching requires pixel additions and multiplications on each pixel in a template and for each pixel in images, which involves a significant amount of pixel operations. Many techniques have been investigated to reduce the computational cost including FFT techniques, partial illumination approaches, and coarse to fine methods. These techniques may work for finding objects with the same orientation. However, when the object orientation in templates is different from the orientation in images, the computational cost is prohibitive even for the latest fast template matching techniques. In this paper, by combining the ideas of moment invariants in pattern recognition, Green theorem from physics and Bresenham line algorithm from computer graphics, we propose a mask size independent and orientation invariant object finding technique. From theoretical analysis and experiments, we demonstrate that this new technique significantly reduces computational cost for orientation free object finding from 0(ܰN3M2) of the direct implementation to 0(ܰN2)
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