With the growth of computer power, Digital Image Processing plays a more
and more important role in the modern world, including the field of industry,
medical, communications, spaceflight technology etc. There is no clear
definition how to divide the digital image processing, but normally, digital
image processing includes three main steps: low-level, mid-level and highlevel
processing.
Low-level processing involves primitive operations, such as: image preprocessing
to reduce the noise, contrast enhancement, and image sharpening.
Mid-level processing on images involves tasks such as segmentation (partitioning
an image into regions or objects), description of those objects to
reduce them to a form suitable for computer processing, and classification
(recognition) of individual objects. Finally, higher-level processing involves
"making sense" of an ensemble of recognised objects, as in image analysis.
Based on the theory just described in the last paragraph, this thesis is
organised in three parts: Colour Edge and Face Detection; Hand motion
detection; Hand Gesture Detection and Medical Image Processing.
II
In Colour Edge Detection, two new images G-image and R-image are
built through colour space transform, after that, the two edges extracted
from G-image and R-image respectively are combined to obtain the final
new edge. In Face Detection, a skin model is built first, then the boundary
condition of this skin model can be extracted to cover almost all of the skin
pixels. After skin detection, the knowledge about size, size ratio, locations
of ears and mouth is used to recognise the face in the skin regions.
In Hand Motion Detection, frame differe is compared with an automatically
chosen threshold in order to identify the moving object. For some special
situations, with slow or smooth object motion, the background modelling
and frame differencing are combined in order to improve the performance.
In Hand Gesture Recognition, 3 features of every testing image are input
to Gaussian Mixture Model (GMM), and then the Expectation Maximization
algorithm (EM)is used to compare the GMM from testing images and GMM
from training images in order to classify the results.
In Medical Image Processing (mammograms), the Artificial Neural Network
(ANN) and clustering rule are applied to choose the feature. Two
classifier, ANN and Support Vector Machine (SVM), have been applied to
classify the results, in this processing, the balance learning theory and optimized
decision has been developed are applied to improve the performance.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/5384 |
| Date | January 2011 |
| Creators | Wang, Dong |
| Contributors | Jiang, Jianmin, Ipson, Stanley S. |
| Publisher | University of Bradford, The Digital Media & Systems Research Institute, School of Computing, Informatics & Media |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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