基于四叉树分割的JPEG隐写分析

当前主要的隐写分析方法都是对整幅图像进行特征提取,而忽略了图像的内容差异。该文提出一种基于四叉树分割的JPEG隐写分析方法,该方法根据图像块的纹理复杂度进行图像分割,对具有相同统计特性的子图像分别进行隐写检测特征的提取,并构造相应的分类器,通过加权融合得到最终的检测结果。实验结果表明该方法具有良好的性能,尤其是在训练与测试图像的统计特性具有较大差异时,该算法的检测准确率提高更加明显。     

Morphological dilation image coding with context weights prediction

This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient’s predicted significance degree. It includes two key dilation technologies: (1) controlling dilation process with context weights to reduce the output of insignificant coefficients and (2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict a coefficient’s significance degree more accurately, which can be used for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.     

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.     

一种使用四叉树分割的分形信息隐藏算法

目前的正交分形信息隐藏算法在进行正交分形编码时都是等分割原始图像,这样无法自适应图像内容且耗时,并且在处理彩色图像时没能考虑分量之间的相关性以及拼贴误差,因此鲁棒性较差。提出一种采用四叉树分割的正交分形信息隐藏算法。该算法能够根据图像特征实现原始图像的四叉树分割,同时考虑彩色图像R、G、B三个分量的相关性,提取亮度分量进行分形编码,并利用拼贴误差特性,动态调整分型参数的修改量来嵌入秘密图像。实验及应用表明,该算法编码时间短、具有很好的透明性以及对JPEG压缩、加噪、滤波、几何形变等具有较好的鲁棒性。     

A pseudo top-hat mathematical morphological approach to edge detection in dark regions

Edge detection is an important pre-processing step in image segmentation. Conventionally, edges are detected according to gradient property, then processed by the thresholding technique. By such an approach, fine edge details in dark region of the image are eliminated. It is annoying sometimes as they are as useful as those in bright region, although this is caused by unevenly distributed lighting in many machine vision applications. In this paper, a novel mathematical morphological edge detection algorithm, based on pseudo top-hat transformation which is derived from top-hat transformation, is proposed to preserve these edge details as well as prominent ones. The algorithm is also presented in detail. Comprehensive experimental results show that the proposed algorithm is efficient for edge details extraction in place of shading while preserving distinguish features.     

基于四叉树结构索引的约束TIN的构建

提出了一个用四义树索引对平面区域进行划分并在此基础上用前沿推进法实现平面点集TIN构建的算法。对于实际应用中所遇到的数据，四叉树索引能够在有限层次内对平面区域进行有效划分，从而保证了TIN构建程序的运行效率。该算法能适用于任意的初始边界约束条件，并对边界和点集的分布特征具有良好的适应能力，因此该算法适用于复杂地形的DTM建模。