Call For Paper Volume:4 Issue:10 Oct'2017 |

EVALUATING THE PERFORMANCE OF THE SIFT AND CANNY BASED FEATURE DETECTION TECHNIQUES

Publication Date : 05/06/2015



Author(s) :

Simranjeet Kaur , Gagandeep Singh.


Volume/Issue :
Volume 2
,
Issue 6
(06 - 2015)



Abstract :

Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and illustrate local features in images. For almost any object in an image, attractive points on the object may be extracted to offer a "feature description" of the object. This description, extracted from a training image, will then be utilized to recognize the object when attempting to find the object in a test image including a number of other objects. To execute reliable recognition, it is essential that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. Such points typically lie on high-contrast regions of the image, such as for example object edges. This paper has evaluated the performance of CANNY and SIFT based feature extraction techniques. The proposed technique is designed and implemented in MATLAB using image processing toolbox.


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EVALUATING THE PERFORMANCE OF THE SIFT AND CANNY BASED FEATURE DETECTION TECHNIQUES

June 4, 2015