R. A. Salam and M.A. Rodrigues
Mining Image Features in an Automatic TwoDimensional Shape Recognition System
243 - 249
2008
2
1
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/688
https://publications.waset.org/vol/13
World Academy of Science, Engineering and Technology
The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the preprocessing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this preprocessing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Open Science Index 13, 2008