Current Issue : July - September Volume : 2015 Issue Number : 3 Articles : 4 Articles
Bit-plane complexity segmentation (BPCS) steganography is advantageous in its capacity and imperceptibility.The important step\nof BPCS steganography is how to locate noisy regions in a cover image exactly. The regular method, black-and-white border\ncomplexity, is a simple and easy way, but it is not always useful, especially for periodical patterns. Run-length irregularity and\nborder noisiness are introduced in this paper to work out this problem. Canonical Cray coding (CGC) is also used to replace\npure binary coding (PBC), because CGC makes use of characteristic of human vision system. Conjugation operation is applied\nto convert simple blocks into complex ones. In order to contradict BPCS steganalysis, improved BPCS steganography algorithm\nadopted different bit-planes with different complexity. The higher the bit-plane is, the smaller the complexity is. It is proven that\nthe improved BPCS steganography is superior to BPCS steganography by experiment....
Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to\nestimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract\nand highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on\na frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved\nsaliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature\nvalue computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency\nand spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only\nused local contrast and spatial attention information to simulate human�s visual attention stimuli. This improved approach resulted\nin a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of\nFlying Apsaras in the Dunhuang GrottoMurals showed that the proposed visual saliency detection approach is very effective when\ncompared with five other state-of-the-art approaches....
In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other\npost processing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed\nthe shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along\nwith contextual information. For feature representation we used articulation invariant representation; dynamic programming\nis then utilized for better shape matching followed by manifold learning based post processing modified mutual kNN graph to\nfurther improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by\nso-called bulls-eye score with and without normalization of modified mutual kNN graph which clearly indicates the importance of\nnormalization. Finally, our method demonstrated better results compared to other methods.We also computed the computational\ntime with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore,\nto show consistency of post processing method, we also performed experiments on challenging ORL and YALE face datasets and\nimproved baseline results....
To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction\nalgorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is\nemployed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based\nimage matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR)\nframes are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration\nalgorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical\nflow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement\nare obtained by the proposed algorithm, when applied to video sequence....
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