International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8, No.3 (2015), pp.211-216 http://dx.doi.org/10.14257/ijsip.2015.8.3.19 ISSN: 2005-4254 IJSIP Copyright ⓒ 2015 SERSC Image type-based Assessment of SIFT and FAST Algorithms Muthukrishnan, R 1 and Ravi, J 2 1 Assistant Professor Department of Statistics, Bharathiar University, Coimbatore, India 2 Assistant Professor Department of Statistics, PGP College of Arts &Science, Namakkal, India muthukrishnan70@rediffmail.com 1 , raviking2008@gmail.com Abstract Identifying the interest points in an image is a key step in image processing and computer vision tasks. Every corner of the images represents a lot of information. Extracting the true corners is the main object to image processing, which can reduce much of the time and calculations. Many algorithms have been suggested in the image processing to detect the true corners, based on the robust statistics. In this paper the corner detection algorithms SIFT and FAST have been studied in image processing under the various image formats. Also, it can provide a direction to the researchers to use the algorithm for the suitable image format and to develop a new algorithm which can detect the exact corners of an image/blurred image. The FAST corner detection method compared with the results of SIFT corner detection method. Experimental results show that the FAST corner detection gives better results compared to SIFT method. All the experiments are carried out MATLAB software. Keywords: Corner detection, SIFT, FAST, robust features 1. Introduction Corner detection is a vital research area in computer vision. Corner is an important local feature in images. Corner detection has played an important role in image matching [1], outline capturing system [2-3], image representation [4] and other fields. The gray level corner detection can be classified four performances of robustness and it must be specified all of the corner detection, such as detection, localization, stability and complexity. Corner and edges are the main role in image matching. The SIFT and FAST corner detection are the recently developed method for image matching. The scale variation and rotations SIFT can give better performance and the recently focuses on FAST corner point detection through machine learning approach that has good and better performance and also low resource requirements. There are recently developed many applications that are related to corner detection, including image matching, image stitching, object identification and stereo matching, among many others. A corner can be defined as the intersection of two edges. The result of image processing directly affected for true and quality of the corners. The corner detection, keeps useful information and improving the efficiency. A number of corner detectors have been proposed by the researchers. A variety of quantitatively evaluation methods of corner detection algorithm have been proposed [5-7]. FAST and SIFT method are characterized by its speed and its independence to other local features, using corners own features to detect corners directly. In this paper, these two corner