Visual saliency is a type of visual feature which simulates visual attention selection mechanism in biological system and has better robustness and invariance. A rapid infrared image and visible light image registration method based on visual saliency and SIFT (scale invariant feature transform) is proposed in this paper. The method adopts amplitude modulation Fourier transform to construct saliency map, and the image salient points are achieved preliminarily by salient threshold. Subsequently, this method calculates the local entropy for these salient points because of entropy's character for information measurement, then these points are reordered and screened based on the strategy of entropy priority. The screened results are thought as centers for salient regions. Morphological operation is used for growing and merging for neighbor salient scene region in image. Aiming at the abstracted salient region for image, PCA (principal component analysis)-SIFT algorithm is proposed, which can produce the compressed SIFT registration features and largely reduce the computational cost of image registration. The proposed algorithm adopts random sampling conformance method to remove the mistaken point pairs before calculating the model parameter of affine transformation for registration between infrared image and visible lights. The experimental results indicate that the method has good invariance under image scale, rotation, translation, and illumination variation and can realize effective registration between infrared image and visible lights. Compared with some classical algorithms, the proposed method has advantage in registration accuracy and registration speed obviously.
In the recent past, the infrared and visible image registration problem has also been addressed visual saliency, where the saliency map is constructed by using amplitude modulation Fourier transform REF .
Registration of infrared and visible light image based on visual saliency and scale invariant feature transform
EURASIP Journal on Image and Video Processing
Computer Science