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Abstract
In recent research, automatic target recognition (ATR) of infrared targets has been taking a lot of interest to the researchers. A rotation invariant method is useful in target recognition, classification and image analysis to reduce the number of training data. In particular, rotation invariant method, Radon transform, is an effective technique that is used for medical care such as computerized tomography (CT) image. This paper proposes a new rotation invariant algorithm for target recognition. The proposed method combines the gradient information and radon transform. The propose method, called gradient Radon (G-Radon), is applied to synthesized infrared images and compared with traditional radon transform and Zernike moments for validation.
Highlights
automatic target recognition (ATR) of infrared targets has been taking a lot of interest to the researchers [1]
Histogram of oriented gradients (HOG) [7] is a famous algorithm with excellent performance when combined with support vector machines (SVM)
This paper proposes a new rotation invariant algorithm, G-radon, to achieve effective recognition
Summary
ATR of infrared targets has been taking a lot of interest to the researchers [1]. Histogram of oriented gradients (HOG) [7] is a famous algorithm with excellent performance when combined with SVM A popular invariant feature is based on the moment including non-orthogonal moments [9]-[11] and orthogonal moments [12]-[14] These methods have property such as less sensitive to noise and very accurate in image reconstruction. SIFT selects the local maxima and generates a descriptor as feature points by using a histogram of the surrounding area in an image. This method serves to strength the distortion or partial change of the image, but a significant amount of calculation occurs to generate the feature vector of 128 dimensions [15, 16]
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