The image watermarking scheme based on scale invariant feature transform(SIFT) is proposed for resisting rotation scale translation(RST) attacks.

The accuracy was obtained in both feature matching and tracking using the scale and rotation invariance of the scale invariant feature transform(SIFT) feature points together with some reasonable constraints.

The Scale Invariant Feature Transform(SIFT) is invariant to image scaling,translation and rotation.

SIFT flow is first adopted to perform a pixel-wise matching and alignment,then the aligned images can be decomposed following the similar procedure for image sequences of the same scene.

Moreover,the precision of object recognition can be improved effectively by the combination of SIFT features and normalized DLT algorithm.

Aiming at the problem,according to the characters of the infrared thermal imager,a stabilization algorithm of SIFT-RANSAC is proposed for motion parameters estimation with SIFT features and RANSAC method.

An approach for object matching based on SIFT feature in multiple-camera surveillance system

SIFT feature was used to extract special features which were processed through dimensional reduction,then the distinctive trained Gaussian mixture model(GMM) was used for target recognition.

Using filter template the noise can be removed efficiently, after filtering, the SIFT feature is extracted.

Then the trilinear constraint and trifocal-tensor-based pixel transfer error computation method is introduced to remove the outliers produced by the SIFT algorithm.

Based on the special spatial relationships against rotational stereo, the traditional SIFT algorithm is improved suits with rotational st.

The feature used by SIFT algorithm(SIFT feature)is a vector based on local gradient,which can resist a lot of image variation such as extension,compression and rotation,meeting the practical requirements of 3D object recognition.

Geometrically Robust Second Generation Image Watermarking Based on SIFT;