Abstract—The SIFT descriptor is one of the most widely used descriptors which has considerable stability against changes such as rotation, scale, affine of the image, and illumination. However, because of the greater emphasis on its insensitivity to geometric changes, this descriptor is weak in various illuminations. Therefore, in this article an attempt has been made to boost the SIFT descriptor against changes in illuminations through the use of techniques of creating pictures in various illumination conditions and by extracting the desired features of these conditions. For this purpose, we have used the Power-Law Transform, and the results of the implementation testing have been successful. The efficiency of the proposed algorithm and of the base algorithm of SIFT with regard to the data set ALOI have been investigated, and it has been found that by adding this method to the base SIFT descriptor , the rate of recognition improves by five percent. Moreover, there will be a better response to changes in illumination.
Index Terms—Illumination variance, object recognition, power-law transform, SIFT descriptor.
Reza Javanmard Alitappeh, Islamic Azad University Qazvin, (e-mail: R_Javanmard@b-iust.ac.ir)
Fariborz Mahmoudi, Islamic Azad University of Qazvin, Iran ( e-mail: Fzmahmoudi@gmail.com). He is now @Home team leader at MRL, Qazvin, Iran
Cite: Reza Javanmard Alitappeh and Fariborz Mahmoudi, "MGS-SIFT: A New Illumination Invariant Feature Basedon SIFT Descriptor," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 99-103, 2013.
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