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TopOne of the most important parts of image splicing and reconstructing images which have been cut into pieces is edge detection. Edge detection techniques proposed by Canny (1986) and Perona and Malik (1990) allow each of the pieces of the split image to be examined. After the edges have been determined, they can be examined and compared. This involves shape and colour matching in order to appropriately reconstruct the image.
Edge detection has been a topic of great discussion, many, including Ziou and Tabbone (1998) have created algorithms and formulas for specific types of edge detection which have varied results depending on the goal of a particular project. Many edge detection algorithms require blurring and differentiating of the image. This makes it difficult to achieve a number of requirements, specifically for image splicing, where those edges are required to be joined to rebuild the image.
This is where our edge detection software is different from the current tech- techniques, one of which was proposed by Ma and Manjunath (1997). After those edges have been located, they need to be processed in such a way that allows the matching of an edge to its corresponding pair. This is particularly true when it comes to matching colour images which have been split up. Matching the colour edges successfully, previously examined by Mirmehdi and Petrou (2000) and Deng and Manjunath (2001), while correctly splicing the colour image back together is a very important part of our scheme which should require no blurring or altering of the image. This will allow a more accurate reconstruction of the original image.
Ng and Chang (2004) give an account of image splicing. This research area has been taken into account by numerous researchers, however the main focus has been detecting images that have been spliced (Chen et al., 2007; Hsu & Chang, 2006, 2007; Ng et al., 2004; Shi et al., 2007; Zhang et al., 2008) rather than images which have been split up and then trying to reconstruct them using these splicing techniques.