A Comparison between SIFT descriptor and Zernike moments feature extraction on geometric shapes representation
سال انتشار: 1395
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 518
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شناسه ملی سند علمی:
NPECE01_221
تاریخ نمایه سازی: 6 بهمن 1395
چکیده مقاله:
The process of image retrieval and recognition is based on image feature extraction. These features have specific structures in the image such as points, edges or objects. The feature extraction is considered as a method in order to represent the images with sufficient accuracy. Yet, one challenging problem in computer vision is how to extract ideal features that can reflect the intrinsic content of the images. So the proposed study summarizes two robustness feature extraction methods: Sift descriptors and Zernike moments. The robustness of studying approaches has been tested against different types of distortion. The processing technique and performance comparison of Zernike moments and SIFT is described based on the precision-recall criterion
کلیدواژه ها:
Scale Invariant Feature Transform (SIFT) ، Zernike moments (ZMs) ، Featuresextraction ، Geometric shapes (GS)
نویسندگان
Narges Ahangar
Faculty of Electrical. Computer and IT Engineering, Islamic Azad University. Karaj Branch, Iran
Azam Bastanfard
Faculty of Electrical. Computer and IT Engineering, Islamic Azad University. Karaj Branch, Iran