Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

Texture feature based automated seeded region growing in abdominal MRI segmentation

A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.

Flotation bubble image segmentation based on seed region boundary growing

在一幅筹款图象分割模糊并且粘附的水泡基于种子区域并且边界成长(SRBG ) 用一个新分割方法被做。位于水泡顶的明亮的象素作为种子区域被提取。种子边界被划分成四条曲线：左顶、正确顶、正确底部、左底部。水泡被沿着相应方向移动这些曲线到水泡边界从种子边界分割。SRBG 方法能移开吵闹的区域，它避免在分割上和在分割下面问题。每个水泡独立被分割而非分割全部筹款图象。从 SRBG 方法的分割结果是比从分水岭算法的那些更精确的。

SEED REGION SELECTION AND HOMOGENEITY CRITERION FOR DOORPLATE IMAGE SEGMENTATION IN MOBILE ROBOT NAVIGATION

Focused on the seed region selection and homogeneity criterion in Seeded Region Growing (SRG), an unsupervised seed region selection and a polynomial fitting homogeneity criterion for SRG are proposed in this paper. First of all, making use of Peer Group Filtering (PGF) techniques, an unsupervised seed region selection algorithm is presented to construct a seed region. Then based on the constructed seed region a polynomial fitting homogeneity criterion is applied to solve the concrete problem of doorplate segmentation appearing in the robot navigation along a corridor. At last, experiments are performed and the results demonstrate the effectiveness of the proposed algorithm.

Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm

Light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out errors occurring in the production process. It is imperative to improve the accuracy of the inspection process to better achieve the goal of safe driving. This paper proposes an automatic inspection method for LED indicators for use on automobile meters. Firstly,red-green-blue (RGB) color images of LED indicators are acquired and converted into R,G,and B intensity images. A seeded region growing (SRG) algorithm,which selects seeds automatically based on Otsu's method,is then used to extract the LED indicator regions. Finally,a region matching process based on the seed and three area parameters of each region is applied to inspect the LED in-dicators one by one to locate any errors. Experiments on standard automobile meters showed that the inspection accuracy rate of this method was up to 99.52% and the inspection speed was faster compared with the manual method. Thus,the new method shows good prospects for practical application.

基于改进SRG法的叠加维吾尔文字提取算法

如何自动选取种子点的位置是基于区域生长法分割文字的关键问题。该算法通过横向线段检测算子提取出文字条中所有的横向线段;统计线段在饱和度、亮度上像素点的分布,根据统计分布使用Otsu法提取出纯净的文字基线,将它作为区域生长种子点的位置;通过生长法则分割文字。实验在四类不同的背景集中测试算法,其中花卉测试集的字母提取率达到72.31%。

An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.

不同药剂处理对受寒害木薯出苗生长的影响(英文)

The rooting and growth of frozen cassava under different chemical treatments were studied.The result has demonstrated that disinfection effect could increase rooting rate and seedling emergence rate of cassava seed stem and decrease the rate of mildew.The sportak treatment could generate better effect(rooting rate and seedling emergence rate were 63%,the mildew rate of stem was 28%).Among different rooting reagents,Genwang+lime treatment generated better effect on rooting rate and emergence rate of frozen cassava than these of other groups and control group.It was concluded from the effects of different rooting reagents on growth of seed stem that Genwang+lime treatment could promote elongation and growth of cassava significantly(the mean plant height of experimental groups inceased 8.58 cm compared with that of control group) while paclobutrazol+lime generated the best effect on crassation of stem(the stem diameter of experimental group increased 0.4 cm compared with that of control group).

基于区域的自动种子区域生长法的彩色图像分割算法

提出了一种基于区域的自动种子区域生长法进行彩色图像分割的算法.该方法首先应用分水岭算法对图像进行初始化分割,形成过分割效果.然后从分水岭算法形成的区域出发,根据一定的规则自动选出一些区域作为种子区域,进行种子区域生长.与传统的种子区域生长法(SRG)以图像中的像素点作为种子进行生长的方法不同,本方法以区域作为种子并以区域作为生长单位进行区域生长.实验结果表明,该算法能够产生较好的分割效果及较快的分割速度.

种子区域生长技术在彩色图像分割中的应用

提出一种基于种子区域生长(Seeded Region Growing,SRG)技术的彩色图像分割方法.该算法利用L*a*b*颜色空间的象素与其邻域的颜色差异及相对欧式距离自动选择种子;应用SRG技术由已知的种子生长出初始分割区域;根据融合了颜色空间和邻接关系的区域距离对初始区域进行分级合并.算法克服了传统区域生长方法不能自动选择种子且容易导致过分割的局限性.将新的分割方法应用到彩色图像,并得到与视觉判断相一致的有意义的分割结果.实验结果显示了所提出的方法对于不同自然彩色图像分割的有效性与适应性.

结合分水岭与自动种子区域生长的彩色图像分割算法

为了对彩色图像进行有效分割,提出了一种将分水岭算法与自动种子区域生长相结合的分割算法。该算法首先利用基于边缘信赖度的各向异性扩散方程对图像进行平滑预处理,以便在去除图像噪声的同时,保持图像的边缘信息,从而有效解决了分水岭算法的过分割现象;然后在此基础上,设计了一种基于色调均值差的自动种子区域选取算法,即根据色调均值和饱和度均值的相似性来对分水岭算法分割形成的区域进行种子区域生长,并利用小图像区域消解算法对区域生长所遗留的小区域进行消解,减少了图像中小区域的数目,从而实现了对彩色图像的有效分割。实验结果表明,该算法简单、快速,且能得到较其他算法更好的分割效果。

基于改进的混合高斯模型的运动目标检测方法

针对传统高斯模型学习速度慢问题,提出了一种基于新的背景模型更新模式的目标检测方法.首先,对彩色图像建立混合高斯模型,并且采用新方法更新背景模型,即不同的阶段使用不同的更新方程,然后由背景差分得到基本准确的前景图像,其次利用基于颜色差、亮度差和梯度差的阴影检测算法削除前景图像的运动阴影,最后利用形态学滤波、连通组件分析和种子区域增长进行后处理.实验结果表明,该方法不管在室内还是在室外都能很好地消除阴影,准确地提取运动目标.

肝脏CT图像三维分割研究

针对肝脏CT图像的特征,提出了一种将种子区域生长算法和改进Snake模型相结合的策略,实现了肝脏的三维分割提取。该方法先从CT图像序列中筛选出肝脏有明显成像边缘的一张切片,在其肝脏区域内选择若干个种子点,利用种子区域生长算法得到初始边缘,再利用改进的Snake模型对初始边缘进行优化,然后,将此切片的边缘轮廓作为与其相邻切片上的初始边缘,重复该过程,直到分割完所有切片。实验表明该算法具有较高效率,分割结果精确,所产生的分割结果可以作为三维重建合适的数据集。

基于区域生长的前视红外图像分割方法

红外成像制导技术以其优越的性能成为当今精确制导技术发展的主流。红外图像的精确分割是实现目标识别的基础。针对地面目标前视红外图像的特点和成像制导技术中图像分割的目的,提出了一种基于区域生长的前视红外图像分割算法,它首先在全局阈值分割的基础上选择出种子点所在区域,并在区域中定义局部灰度信息统计准则和策略选取出种子点;然后以目标模板面积作为参考,通过对分割效果的判断自动调整生长阈值进行区域生长以得到分割图像。实验结果表明,本方法分割出的目标完整准确,分割结果对基于边缘特征的目标匹配识别非常有利。

基于自动种子区域生长的超声B图像缺陷分割方法

提出一种基于自动种子区域生长的超声图像缺陷分割方法。首先使用最大类间方差(Otsu)分割法对超声B图像进行一次预分割;其次寻到绝对背景区,并且在此区域内自动设置种子起始点;然后利用区域生长法将缺陷从背景中分割;最后通过数字形态学降噪法来进一步提高缺陷的识别度。实验结果表明:该方法不仅能准确地分割出缺陷,且具有较好的缺陷边界信息,提高了对超声B图像的处理效率,有效地抑制了大部分图像噪声。

改进区域生长的医学超声图像分割方法研究

基于区域生长的方法是一种比较有效的超声图像分割方法。针对区域生长时的几个关键问题,优化选择一组能正确代表目标区域的种子像素。在分析像素邻域灰度的特点基础上,采用最大类间方差法,确定了目标区域的最佳分割阈值。通过改进区域生长中的邻域搜索策略,结合梯度图确定合理的生长准则,制定了一种新的区域生长的停止条件,在种子点邻域像素与种子像素比较过程中,对不符合生长条件的邻域像素不是直接排除在目标区域外,而是以此邻域像素为中点进行二次比较。最后给出的实验结果表明该方法改善了图像分割的质量。

一种改进的基于区域生长的彩色图像分割方法

提出一种改进的基于区域生长的彩色图像分割方法。首先,将图像从RGB颜色空间转换到YCbCr颜色空间。其次,自动选取种子点,利用区域生长法进行分割。最后,使用改进区域合并算法合并相似的、小尺寸的区域。对网上随机抽取的图像进行了测试,实验结果证明了提出方法的有效性。

可见光图像颜色特征与支持向量机相结合的绝缘子污秽状态识别方法

绝缘子污闪事故严重威胁着电力系统的安全运行,高压绝缘子污秽等级的准确识别是防污闪的重要研究内容。笔者提出了一种基于绝缘子可见光图像颜色特征和支持向量机技术,通过建立污秽等级与可见光图像颜色特征值的映射关系,实现绝缘子污秽状态识别的方法。以深圳供电局多个变电站不同污秽度红色陶瓷绝缘子数据为基础,采用改进种子区域生长法分割得到绝缘子盘面区域,提取RGB和HSV颜色空间36种特征,依据Fisher判据,筛选得到能表征绝缘子盘面污秽程度的S均值和S中值;设计支持向量机多值分类器,进行污秽等级划分。实验结果表明,该方法的绝缘子污秽等级准确率达96.67%,实现污秽状态的准确监测,为绝缘子污秽等级识别提供新思路。

基于纹理特征与区域生长的高分辨率遥感影像分割算法

影像分割是面向对象影像分析中的重要步骤。为了提高高分辨率遥感影像(high-resolution remote sensing image,HRI)分割算法的性能,提出一种新的影像分割算法,包含种子确定、基于种子区域生长(seeded region growing,SRG)的过分割(advanced SRG,ASRG)和层次区域生长(hierarchical region growing,HRG)3个步骤。利用Gabor纹理特征定义纹理均匀性,将种子自动放置在HRI中同一纹理组成区域的中心位置;在SRG阶段,将HRI光谱信息与斑块形状信息相结合,提出了一种新的合并规则,以提高SRG过分割的精度与分割结果中各个斑块排列的紧凑性;在HRG阶段,提出了一种自适应的阈值,可以更好地保持多尺度分割的特性;在实验部分,采用3景HRI验证了上述方法。利用监督的影像分割评价方法定量评价了该方法的分割精度,并与另外2种主流的遥感影像分割算法进行了对比。结果表明,该方法可以得到令人满意的分割效果。

一种局部纹理特征在区域生长中的应用

为克服现有算法不能自动选择种子且没有很好地利用纹理信息的局限性,提出了一种基于局部纹理特征的区域生长算法。算法分为两个阶段,即颜色量化和区域生长。先利用Mean Shift算法对颜色聚类,对图像预分割,用该局部量化纹理算法提取量化结果的纹理特征,由该纹理特征自动选择种子并分级合并区域。与经典的JSEG算法相比,该算法能够得到相似的分割结果,且计算复杂度低。