Abstract :
The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.
Image Blur:
What is Blurring?
We all know what blurring is, don’t we? It’s that thing that happens when your camera is out of focus or the dog steals your glasses. What happens is that what should be seen as a sharp point gets smeared out, usually into a disc shape. In image terms this means that each pixel in the source image gets spread over and mixed into surrounding pixels. Another way to look at this is that each pixel in the destination image is made up out of a mixture of surrounding pixels from the source image. The operation we need for this is called convolution. This sounds complicated but thats only because mathematicians like to make things sound complicated in order to maintain that air of magic and keep the funding rolling in. Well, I’m onto them and I can reveal that convolution is not that complicated (at my level anyway). The way it works is this: we imagine sliding a rectangular array of numbers over our image. This array is called the convolution kernel. For every pixel in the image, we take the corresponding numbers from the kernel and the pixels they are over, multiply them together and add all the results together to make the new pixel. For