This work focuses on the application of the reconstruction method of differentiated backprojection （DBP）-projection onto convex sets （POCS） in the interior problem.First,we present the definition of the interior problem and real truncated Hilbert transform,and then outline the implementation steps of DBP-POCS.After that,we introduce the middle-part known condition for region of interest （ROI） accurate reconstruction and the unique condition of the interior problem,and verify the uniqueness and stability of the interior problem accurate reconstruction through numerical experiments,and then compare the results for the interior problem in reconstruction images using filtered backprojection （FBP）.In addition,the authors also design the application models of ROI reconstruction and make an initial attempt to the application of DBP-POCS method in the interior problem.
A color-intensity feature extraction method is proposed aimed at supplementing conventional image hashing algorithms that only consider intensity of the image. An image is mapped to a set of blocks represented by their dominant colors and average intensities. The dominant color is defined by hue and saturation with the hue value adjusted to make the principal colors more uniformly distributed. The average intensity is extracted from the Y component in the YCbCr space. By quantizing the color and intensity components, a feature vector is formed in a cylindrical coordinate system for each image block, which may be used to generate an intermediate hash. Euclidean distance is modified and a similarity metric introduced to measure the degree of similarity between images in terms of the color-intensity features. This is used to validate effectiveness of the proposed feature vector. Experiments show that the color-intensity feature is robust to normal image processing while sensitive to malicious alteration, in particular, color modification.
