文章基本信息

标题：Object detection and recognition by using enhanced Speeded Up Robust Feature
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作者：Tawfiq A. Al-asadi ; Ahmed J. Obaid
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2016
卷号：16
期号：4
页码：66-71
出版社：International Journal of Computer Science and Network Security
摘要：In image processing field there is an attention for detection objects, regions and points then made decision in case found it in a single or collection of images may called test or image data set, for this task we have used an algorithm that used in many computer vision application and also considered very fast by compared to others this algorithm can detect and describe local features for any interest object and extract features or descriptor points from it and compare these features/ descriptor by the features that extracted from origin image, matching process has been done among features and decision made based on similar features found, this algorithm called Speeded Up Robust Features (SURF) algorithm. In this paper we used enhanced Speeded Up Robust Features ""SURF"" algorithm, our model counting the features in either object and origin image in data set, then matching percentage calculated using a metric of counting the size of inlier matching features towards outlier features, Radom Sample Consensus (RANSAC) algorithm has been combined with SURF for eliminated error matching that happen in features, then decision has been given based on that metric if the object is present or not. In case object found Speeded UP Robust Features ""SURF"" algorithm can detect the position of the interest object in origin image by using geometric transform. In this paper we have used our metric and enhanced model to made decision and write result finally for each compared process and also write some information that used for matching procedure finally we can distinguish each calculating percentage and valid strength features matched that used for finding the interest objects under different circumstances.
关键词：Object detection; object recognition; feature matching; SURF.