Title :
Improving edge-based feature extraction using feature fusion
Author :
Nercessian, Shahan ; Panetta, Karen ; Agaian, Sos
Author_Institution :
Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA
Abstract :
Feature extraction is arguably the most important stage of an automatic object detection system. It is in this stage where the results of previous processing steps are interpreted to somehow characterize an object. Developing methods for feature extraction and feature vector generation using information from edge maps is a natural progression, as edge detection determines structure in images. A new edge-based feature extraction scheme is introduced based on the feature fusion of two existing methods. A generalized set of kernels for edge detection is also presented. The experimental results show that the detection of different objects of interests is improved using the new method.
Keywords :
edge detection; feature extraction; image fusion; object detection; automatic object detection system; edge detection; edge-based feature extraction; feature fusion; feature vector generation; Data mining; Feature extraction; Fusion power generation; Image edge detection; Kernel; Object detection; Support vector machine classification; Support vector machines; X-ray detection; X-ray detectors; edge-based feature vectors; feature extraction; object detection;
Conference_Titel :
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2383-5
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2008.4811356