Publication: Image Reconstruction By Dominant Point Detection And Least Squares Rational Quadratic BéZier
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Date
2019-02
Authors
Lim, Ai Hui
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Abstract
The techniques of boundary-based dominant point detection and curve fitting are important in the analysis of digital images. They provide key information of images for further processing and analyzing in lower amount of data, hence speed up the pro- cess of analysis. This relates to the processes of shape description, pattern recognition and image understanding. Boundary data of binary images are extracted and then rep- resented in coordinates of planar form. These data are then used for the processes of support region determination, dominant point detection and lastly, curve fitting. Re- gion of support plays a vital role in the process of locating precise dominant points of a set of boundary data. Support region of a point is a subset of boundary data points which bear the maximum level of linearity. Techniques consist of geometrical and sta- tistical approaches are presented to find proper support region. Two existing methods in finding support region are improved to enhance their performance. The methods are named as unit bending value and Marji & Siy method. Besides, three new methods are proposed in this work to determine support region, namely eigen-ratio, eccentric- ity with eigenvalues and convex hull area-length ratio. Curvature measurement is a common method to extract dominant points. Geometrical and statistical methods are applied to estimate curvatures and hence locating the dominant points. Data points with high curvature will be selected as dominant points to represent the shape of the xix
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Keywords
Reconstruction By Dominant Point Detection