A New Method to Improve the Difference of Gaussian Feature Detector
Hassan Amerehie1, Rouhollah Dianat2, Farshid Keynia3
1Hassan Amerehie, M.Sc. Computers – AI, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran.
2Dr. Rouhollah Dianat, Asst. Prof., Department of Computer Engineering – Faculty of Engineering – Qom University, Qom, Iran.
3Dr. Farshid Keynia, Asst. Prof., Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran.
Manuscript received on November 02, 2014. | Revised Manuscript received on November 04, 2014. | Manuscript published on November 05, 2014. | PP: 1-7 | Volume-4 Issue-5, November 2014. | Retrieval Number: D2327094414 /2014©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the basic requirements in images representation was the feature extraction and its proper description and has many applications in the image processing and the machine vision. Many of the local feature descriptors of image use the difference of Gaussian feature detector. This detector is too much invariant against the scale changes. In this paper, a procedure is presented to select a proper threshold for the standard deviation in Gaussian filter to improve the performance of difference of Gaussian detector. In this paper’s method, based on the properties of co-occurrence matrixes, the spatial dependences between available points in the image are divided into three general classes: sharp points, middle points and unsharp points, and then, on the basis of this division, the appropriate position is determined for stopping the development of standard deviation in Gaussian filter in some way that it is prevented to destroy the sharp points in the image and also to select the noise points as the key points of image.
Keywords: Difference of Gaussian (DOG), Feature Detector, Interest Point, Key Point