In this dissertation, the development and performance evaluation of new techniques for image registration and image geometric normalization, which are based on feature points extracted from images are investigated.
A feature point extraction method based on scale-interaction of Mexican-hat wavelets is proposed. This feature point extractor can handle images of different scales by using a range of scaling factors for the Mexican-hat wavelet leading to feature points for different scaling factors. Experimental results show that the extracted feature points are invariant to image rotation and translation, and are robust to image degradations such as blurring, noise contamination, brightness change, etc. Further, the proposed feature extractor can handle images with scale change efficiently.
A new algorithm is proposed for registration of geometrically distorted images, which may have partial overlap and may have undergone additional degradations. The global 2D affine transformations are considered in the registration process. Three main steps constitute the algorithm: extracting feature point using a feature point extractor based on scale-interaction of Mexican-hat wavelets, obtaining the correspondence between the feature points of the reference and the target images using Zernike moments of neighborhoods centered on the feature points, and estimating the transformation parameters between the first and the second images using an iterative weighted least squares algorithm. Experimental results show that the proposed algorithm leads to excellent registration accuracy using several types of images, even in cases with partial overlap between images. Further, it is robust against many image degradations and it can handle images of different scales effectively.
A new technique for image geometric normalization is proposed. The locations of a set of feature points, extracted from the image, are used to obtain the normalization parameters needed to normalize the image. The geometric distortions considered in the proposed normalization technique include translation, rotation, and scaling. Experimental results show that the proposed technique yields good normalization accuracy and it is robust to many image degradations such as image compression, brightness change, noise contamination and image cropping.
A blind watermarking technique for images is proposed, as an example of the possible applications of the presented geometric normalization technique. In order to enhance robustness of the watermarking technique to geometric distortions, the normalization technique is used to normalize the image, to be watermarked, during the embedding process. In the watermark detection stage, the normalization parameters for the possibly distorted watermarked image are obtained and used to transform the watermark into its normalized form. The transformed watermark is, then, correlated with the image to indicate whether the watermark is present in the image or not. Experimental results show that the proposed watermarking technique achieves good robustness to geometric distortions that include image translation, rotation, and scaling.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/888 |
| Date | 23 April 2008 |
| Creators | Yasein, Mohamed Seddeik |
| Contributors | Agathoklis, Panajotis |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
Page generated in 0.0027 seconds