基于Legendre正交矩的三维边界检测算子

本文提出了一种新的基于Legendre正交矩的三维边界检测算子。该算子可通过任意大小的窗口自动检测出三维灰度图像中所包含的三维物体边界,具有比一般算子更高的方向灵敏度和表面精度,同时具有明确的几何模型意义。计算量是采用矩运算的一个必须考虑的因素,通过预先计算模板的算法,大大减少了计算量。我们在实验中把该算子和算法结合光线跟踪方法应用于三维医学图像的体绘制当中取得了理想的效果,表明Legendre正交矩三维边界检测算子是一种行之有效的三维边界检测算子。     

二值图边界的快速提取

本文提出了一种快速提取二值图边界的方法，并详细讨论了硬件设计方案．该方法采用2×2窗口来跟踪边界，并能将连通区的所有边界（包括区域内部空洞的边界）全部提取出来．在硬件设计方案中，为了提高速度，采用了窗口并行存取技术。对若干图象进行处理的结果表明，对于多数大小为512×512的图象，提取边界的时间可在40ms以内．     

图象匹配时矩的高效算法

用不变矩作为相似度度量进行图象匹配，可减少图象平移、旋转以及比例缩放对匹配性能的影响。但不变矩的计算量大，使匹配速度变慢。本文根据图象匹配的特点，提出了一种适用于图象匹配时的矩的增量算法—IACM算法。用此算法可大大提高用不变矩进行图象匹配的速度。     

相关峰实时模板判别方法的研究

本文提出了一种联合变换相关峰的实时判别方法，该方法采用参数图象的自相关峰分布作为实时模板，将相关输出结果与实时模板进行比较，通过计算二者的相似度确定目标图象与参考图象的一致性。实验结果对理论分析进行了良好的验证。     

旋转人脸的模板匹配定位与旋转角度估算方法

本文实现了一种依靠非旋转的人脸模板对平面内旋转的人脸图象进行匹配定位与角度估算的方法。相对于通过由不同旋转角度的模板构成的集合中的成员分别作用于目标图象进行匹配定位的方法，本文的方法减轻了计算负担。而且本文将圆投影法与Zernike矩法相结合实现了旋转人脸图象的定位与旋转角度估算。其匹配的准确性比较高，而且具有良好的抗噪声特性。     

基于边界和模糊集理论的三维图像分割

本文提出了一种基于边界和模糊集理论的三维图象分割方法，利用高斯－拉普拉斯算子检测出图象中各结构的边界后，通过区域增长获取感光趣结构的三维二值模板，由高斯－拉普拉斯图象和二值模板出发，利用模糊集理论，确定感兴趣结构的三维模糊模板，从而达到在体积图象中分割出感兴趣结构的目的。     

自适应直方图均衡方法研究与应用

本文介绍了直方图均衡（HE）和自适应直方图均衡（AHE）对比度增强方法，进而提出了一种基于幂函数形式的加权自适应直方图均衡法（WAHE），并给出其滑动窗口权矩阵生成方法及直方图分布计算方法。最后给出数字X光胸腔图象的处理结果。     

图象匹配的快速映射定位法

本文证明了用投影特征进行图象匹配定位的唯一性。用Walsh函数作核函数计算投影特征,可加快计算过程。利用窗口图象的相对位移与投影序列之间的线性模型进行了快速映射定位,提出了两种组合精定位方法。实验结果证实,本文方法的定位精度与SSDA相近,但速度提高了十多倍。     

多帧超分辨率重建中的图像插值

为了实现多帧超分辨率重建,必须从图像序列中提取子像素信息,因此要求图像的配准精确到子像素级。本文提出了一种超分辨率重建中运动矩阵的构造方法。为了提高矩阵的构造精度,该方法引入图像插值,用性能好但计算量较小的三次插值算子实现插值运算,并比较了三种常用插值核的性能。实验结果表明,选择适当的插值函数不仅不会明显增加计算量,而且可以显著提高矩阵的构造精度,从而大大提高多帧超分辨率重建图像的质量。     

双三次卷积模板插值算法的FPGA实现

为了能在硬件上实现高质量、高效果的视频图像缩放、旋转等,文章研究分析了双三次插值算法的实现,对双三次插值计算进行离散化,得到相应的计算模板,简化计算过程。提出了一种在FPGA上实现双三次卷积模板算法的方法。实验结果表明:该算法的缩放效果优于双线性插值算法,略低于双三次插值算法,但计算性能上明显高于双三次插值算法。双三次卷积模板插值算法是一种能够在有限的资源上实现较好效果的插值算法。     

高速的9×9尺寸模板Zernike矩边缘算子

根据Zernike矩基本原理,提出了基于9×9模板尺寸的Zernike矩边缘算子。首先推导Zernike矩模板计算过程,然后计算出2个9×9模板,并选取二值图像和经过5次平滑的模糊图像为对象对新算子进行了测试。结果表明:对于理想的二值图像,新算子具有较强的边缘细化能力,且边缘检测精度也较高;而对于模糊图像,虽然新算子的边缘检测效果也较好,但应注意边缘图像表征与亚像素坐标数据间存在不一致的现象。最后,采用先粗后精定位思想,使新算子的运算速度大幅提高,耗时仅为0.19s。     

Efficient computation of local geometric moments

Local moments have attracted attention as local features in applications such as edge detection and texture segmentation. The main reason for this is that they are inherently integral-based features, so that their use reduces the effect of uncorrelated noise. The computation of local moments, when viewed as a neighborhood operation, can be interpreted as a convolution of the image with a set of masks. Nevertheless, moments computed inside overlapping windows are not independent and convolution does not take this fact into account. By introducing a matrix formulation and the concept of accumulation moments, this paper presents an algorithm which is computationally much more efficient than convolving and yet as simple.     

Analysis of principle deviation of subpixel Zernike moments operator

In original Zernike moments subpixel edge operator, ideal step edge model is chosen for calculating four parameters of a pixel point, and therefore principle deviation is caused by edge model. In order to discuss the effect of principle deviation on edge location, sampled edge model is chosen for calculating edge parameters, principle deviation of edge translation l is derived based on Zernike moments edge detection theory, and modified formula of l is given for Zernike moments operator with masks of 5×5 size. Both theory analysis and testing result demonstrate that principle deviation is zero when edge translation l is limited in central pixel of a sampled window. In another case, the modification of l should be considered, which results in edge location accuracy of non-subpixel level for this subpixel edge operator.     

一种从视频压缩码流中精确提取运动对象的快速算法

针对目前大部分视频对象分割方法相当复杂而且计算量大的问题,提出了一种在压缩域粗分割,在空域精细分割的方法。该方法利用压缩域中运动向量进行聚类,得到运动对象的初始分割。将分割模板通过运动参数映射到参考帧I帧,,解码初始分割区域进行Canny边缘俭测和边缘跟踪,即可得到精确的对象轮廓。该方法使得处理的数据量保持最小,节约了处理时间并得到了像素级精度的分割对象。     

一种应用Tsallis熵的红外图像边缘检测算法

提出一种基于Jensen-Tsallis熵的红外图像的边缘检测方法,通过分析红外图像的成像特点,利用红外图像的局部的熵信息,计算滑动窗口内图像的Jensen-Tsallis散度-方向对,由Jensen-Tsallis散度确定候选边缘像素,再利用Jensen-Tsallis的方向信息细化并连接边缘像素,实验结果表明,该算法可以有效地检测红外图像边缘.     

Color image processing by using binary quaternion-moment-preservingthresholding technique

This paper presents a new moment-preserving thresholding technique, called the binary quaternion-moment-preserving (BQMP) thresholding, for color image data. Based on representing color data by the quaternions, the statistical parameters of color data can be expressed through the definition of quaternion moments. Analytical formulas of the BQMP thresholding can thus be determined by using the algebra of the quaternions. The computation time for the BQMP thresholding is of order of the data size. By using the BQMP thresholding, quaternion-moment-based operators are designed for the application of color image processing, such as color image compression, multiclass clustering of color data, and subpixel color edge detection. The experimental results show that the proposed operator for color image compression can have output picture quality acceptable to human eyes. In addition, the proposed edge operator can detect the color edge at the subpixel level. Therefore, the proposed BQMP thresholding can be used as a tool for color image processing.     

基于灰度梯度和反正切函数拟合的亚像素边缘检测算法

在数字图像处理和机器视觉中,边缘检测是一个基本问题和关键环节,因此,精确地检测图像边缘是非常必要的。本文提出了一种基于灰度梯度和反正切函数拟合的亚像素边缘检测算法。首先,通过Canny算子粗略提取输入图像的边缘;然后对初步得到的边缘像素逐点提取灰度梯度方向,以提取的梯度方向为坐标轴正方向建立灰度梯度直角坐标系;最后利用最小二乘法,采用反正切函数拟合图像边缘的灰度梯度,获得亚像素边缘位置。利用Microsoft Visual Studio 2008平台进行实验,结果表明：与基于小波变换及基于Zernike矩的亚像素边缘检测方法相比,本文所提算法定位精度较高,检测速度较快,能够更完整地检测出图像的平滑边缘。     

A moment-based three-dimensional edge operator

A three-dimensional edge operator for detecting anatomical structures in medical imaging is presented. It uses the spatial moments of the gray-level surface, and operates in three dimensions with any window size. It allows the location and the contrast surface, as well as the surface orientation, to be estimated. The computation of the discrete version is reported. Bias and errors due to the spatial sampling and noise are analyzed at both a theoretical and experimental level. The moment-based operator is compared with other well-known edge operators using simple shaped primitives for which the analytical solution is known. The 3-D rendering of real data is then provided by merging the operator in a ray-tracing framework     

Componentwise edge detection by Laplacian operator masks

This note introduces componentwise edge detection technique for Laplacian operator masks. Laplacian operator masks are expressed by means of gradient components. The absolute sums of the gradient components at image points form the gradient image, from which the edges are extracted. The results obtained are comparable to those obtained by the gradient sum and max operators or Sobel and Prewitt masks.