Digital Image Processing using Local Segmentation
|Supervisor Name||Dr. Peter Tischer|
|Keywords||Digital Image Processing, Local Segmentation, Intensity Thresholding, FUELS Denoising Algorithm, Computer Processors, Mass Storage Devices, Handwriting Recognition|
|Publication Date||April 15, 2015|
|Domain||Computer Science / IT|
Digital Image Processing using Local Segmentation 2002
A unifying philosophy for carrying out low level image processing called local segmentation is presented. Local segmentation provides a way to examine and understand existing algorithms, as well as a paradigm for creating new ones. Local segmentation may be applied to range of important image processing tasks. Using a traditional segmentation technique in intensity thresholding and a simple model selection criterion, the new FUELS denoising algorithm is shown to be highly competitive with state-of-the-art algorithms on a range of images. In an effort to improve the local segmentation, the minimum message length information theoretic criterion for model selection (MML) is used to select between models having different structure and complexity. This leads to further improvements in denoising performance. Both FUELS and the MML variants thereof require no special user supplied parameters, but instead learn from the image itself. It is believed that image processing in general could benet greatly from the application of the local segmentation methodology.
Image processing is a rapidly growing area of computer science. Its growth has been fueled by technological advances in digital imaging, computer processors and mass storage devices. Fields which traditionally used analog imaging are now switching to digital systems, for their exibility and affordability. Important examples are medicine, lm and video production, photography, remote sensing, and security monitoring. These and other sources produce huge volumes of digital image data every day, more than could ever be examined manually.
Digital image processing is concerned primarily with extracting useful information from images. Ideally, this is done by computers, with little or no human intervention. Image processing algorithms may be placed at three levels. At the lowest level are those techniques which deal directly with the raw, possibly noisy pixel values, with denoising and edge detection being good examples. In the middle are algorithms which utilise low level results for further means, such as segmentation and edge linking. At the highest level are those methods which attempt to extract semantic meaning from the information provided by the lower levels, for example, handwriting recognition.