基于RGB的干枣图像区域阈值分割算法的应用研究

针对目前基于RGB模型的干枣彩色图像研究中的一系列问题,提取干枣图像大小与实际大小相差较大,算法复杂、速度慢,很难达到在线检测的要求,提出一种利用机器视觉技术以提取干枣图像目标的新方法,分别统计R、G、B各分量的灰度值,绘制相应直方图;通过分析比较,选取R-B分量灰度直方分布图作为确定阈值分割的依据,利用大津法自适应求出阈值,然后提出区域阈值分割方法,并对干枣图像进行阈值分割。多次实验证明,利用该算法提取干枣大小与实际大小非常接近,稳定性高,检测速度快,可以满足进行干枣的形状、表面缺陷等检测要求,能够满足机器视觉技术在线检测要求。     

票据识别系统数据分割与提取

在手写体票据识别系统研究过程中,从带表格及噪声的背景中获取数据字符是至关重要的环节。实践证明［１］,阈值图象分割方法是一种简单而有效的数据获取方法,该文以国家统一发票为研究对象,在样本直方图分析的基础上,讨论了阈值选取和样本明暗度之间的关系．并由此设计了一种新的票据数据获取过程结构。与通常方法比较,将使系统整体速度有明显的提高。     

基于颜色直方图的图象检索

针对基于颜色直方图的静态图象检索,提出了自动构造全局参考颜色表和自带参考颜色表的算法,解决了Ｓｗａｉｎ算法中存储量与计算量过大的问题,同时给出了一个发层聚类构造二叉树式分层索引数据结构的方法 。     

基于Camshift算法的神经丝蛋白自动跟踪

神经丝蛋白体型细长、形态多变,跟踪难度大.为了能够准确稳定的获取神经丝蛋白质的运动状态,利用了改进的Camshift算法实现了对神经丝蛋白运动的自动跟踪.利用HSV颜色空间的颜色直方图建立目标模型,结合预测点对目标特征点加权.通过在目标颜色概率模型中引入核函数,利用核密度梯度来进行目标搜索,最终在每帧图像中获取目标的具...     

The revealing power of a test case

‘Propagation, infection, and execution analysis’ (termed PIE) is used for predicting where faults can more easily hide in software. To make such predictions, programs are dynamically executed with test cases, and information concerning the test cases is collected into a histogram, each bin of which represents a single test case. The score in a bin predicts the likelihood that the test case will reveal a fault through the production of a failure (if a fault exists in the set of program locations that the test case executes). Preliminary experiments using program mutations suggest that the histogram technique presented in this paper can rank test cases according to their fault revealing ability.     

基于梯度方向直方图的干枯植物与裸土偏振光谱识别研究

从地物光谱图像识别的关键问题出发,将改进的梯度方向直方图(光谱梯度方向直方图)与"光谱总反射率"应用到偏振光谱识别研究中,实现了对5种干枯植物与3种裸土的识别.在研究中,先联合光谱梯度方向直方图特征向量的模与光谱总反射率进行层次聚类分析,可以识别6种研究对象,再单独利用光谱梯度方向直方图特征的模进行层次聚类分析识别其余2种研究对象,从而可以实现全部研究对象偏振光谱识别.     

一种直接直方图修正方法

本文提出了的方法通过将直方图均衡作为直方图规定的特例考虑,既可实现直方图均衡又可实现直方图规定,从而实现了这２种方法的统一,而且直方图规定的结果好于文献「１」方法。     

Reversible data hiding based on automatic contrast enhancement using histogram expansion

A lot of Reversible Data Hiding (RDH) methods aim to generate a stego image infinitely approaches the original image while the quality of the original image is leaved out of consideration. Juxtaposed with a plain image, a contrast enhanced version always improves the user experience significantly. Reversible Data Hiding with Contrast Enhancement (RDHCE) enhances the stego image contrast combined with its payloads and enables the cover image to be regained accurately after the payloads have been extracted. This study presents a novel RDHCE method using histogram expansion. First, a new local histogram selecting strategy is proposed to improve the contrast of the whole image. Meanwhile, the global average brightness is used as a reference to determine the shifting direction of the local histogram to prevent the image from being over-enhanced. Moreover, the contrast can be improved adaptively when a reasonable number of data is embedded at the selected embedding points. Experimental results show that, with a given payload, the proposed method achieves better contrast and maintains good visual quality compared with state-of-the-arts.