A novel robust digital image watermarking scheme which combines image feature extraction and image normalization is proposed. The goal is to resist both geometrical and signal processing attacks. We adopt a feature extraction method called Mexican Hat wavelet scale interaction. The extracted feature points can survive various attacks such as common signal processing, JPEG compression, and geometric distortions. Thus, these feature points can be used as reference points for both watermark embedding and detection. The normalized image of a rotated image (object) is the same as the normalized version of the original image. As a result, the watermark detection task can be much simplified when it is done on the normalized image without referencing to the original image. However, because image normalization is sensitive to image local variation, we apply image normalization to non-overlapped image disks separately. The center of each disk is an extracted feature point. Several copies of one 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, printing and scanning process, row or column removal, shearing, rotation, scaling, local warping, cropping, and linear transformation.