Research On Geometric Invariant Feature Points Extraction Algorithm In Watermarking

Geometrical invariant feature extraction, which can provide reference points, has been a key factor to second generation image or video watermarking technology. It also plays a very important role in pattern recognition, face and organ recognition and image content searching.Feature points extraction based on curvature scale space needs to detect the edge of the image before detecting feature points. After the edge is detected, the image is observed under different scales and the points attaining the local maxima in curvature are selected as feature points that are invariant to geometrical transforms. However, the algorithm is not so effective to meet the real-time requirement because the edge detection is costs too much time. Harris-Laplace corner detector is a multi-scale gray level image corner detector, but the computation is very complicated because each level of scaled image must be calculated to extract feature points.By improving Harris-Laplace algorithm, a novel algorithm is presented, which combines scale space theory and Harris detector and gives every scale a weight value to mark the importance of every weight. Using the weighted average Harris response, the feature points are stable to some geometrical transforms such as cropping and scaling while the computation is relatively smaller. Observing the image through large scale can aqquire coarse content of the images while observing it through small scale can get fine content of images. According to this hypothesis, iterative convergent Harris detector is another corner detector based on scale space which first extracts feature points on the coarse scale and traces them to locate the exact position while the scale becomes fine. In this way, the algorithm has better performance against noise while reducing the whole computation.On the base of the above-mentioned research, an experiment platform of benchmarking feature points extractions is designed and established. According to the experiment, the proposed algorithms can not only guarantee the quality of the extracted feature points but also reduce the computation time compared to conventional algorithms.