An automatic change detection (CD) method based on level set evolution in remote sensing images is proposed. The CD problem is formulated as a segmentation issue to discriminate the changed class from the unchanged class in the difference images. The strategy of the level set initialization is considered and neighborhood constraints are added to the level set energy model. In addition, a coarse-to-fine procedure is adopted. A chief advantage of our approach is to be able to obtain correct results even when the difference image contains different types of changes. Furthermore, the proposed method is robust against noise and yields smooth boundaries of changed regions without manual parameter adjustment. We implement the proposed method in a multiresolution framework and validate the algorithm systematically with a variety of remote sensing images by low- as well as high-spatial resolution sensors, including Landsat-5 TM, SPOT5, IKONOS, etc.
A novel minutiae-based method is proposed to match deformed fingerprints. We combine minutiae structures and descriptors to obtain multiple pairs of reference minutiae and globally align two sets of minutiae to get a common overlapping region based on these pairs of reference minutiae. At the matching stage, our proposed approach aligns local areas in two fingerprints with new reference minutiae pairs that are selected from matched minutiae pairs. After registration of the fingerprints according to the local correspondence, the number of matched minutiae can be counted using tight thresholds. Experimental results confirm that the proposed algorithm is effective for fingerprint matching with nonlinear distortions.
We propose a novel and accurate technique based on edge map for the determination of core points in fingerprint images. An edge map is obtained by detecting the edges on a smoothed orientation map. An edge pixel deletion operation is then performed on the edge map based on gradient information. Last, upper and lower core points can be extracted by analyzing the orientation consistency of a few edge pixels. Experimental results show that the proposed method can effectively detect the core points with high speed for all types of fingerprints.