高斯尺度参数自适应算法研究

为了避免计算过于复杂或因丢弃过多关键信息而造成失真过大的问题,在高斯尺度空间的构造中正确选用尺度参数,以使图像信息的变化呈现均匀的特点就显得尤其重要。目前许多高斯尺度空间应用中采用的层之间的尺度参数关系并不明确,有可能使得分层效果不理想。基于视觉特征模型提出一种自适应高斯尺度参数的算法,并通过实验验证了它的有效性,从而为图像的高层次处理如目标识别等提供信息量稳定变化的尺度空间。     

基于SIFT的图像特征提取算法研究

图像特征点提取的性能决定着图像匹配的效率,提出了一种基于多尺度空间的SIFT算法,该算法对图像旋转、尺度缩放、亮度变化等方面具有较好适应性,分析了SIFT算法的基本模型与基于尺度空间的关键点的位置、尺度及方向参数的获取方式,实验证明,该算法对不同尺度空间上特征点的尺度、方向、大小信息获取具有较高的准确率,算法的稳定性与鲁棒性强。     

基于视觉特征的尺度空间信息量度量

图像的多尺度表示指的是从原始图像出发,导出一系列越来越平滑、简化的图像。这种简化意味着信息的丢失。如果能定量描述每一个尺度中图像的信息,这对于多尺度表示来说有着重要的作用。虽然Sporring等人提出的尺度空间信息熵度量能解决一些问题,但是并不满足从视觉理论和直观的基础上提出的尺度空间信息量度量的基本要求,例如形态不变性等,为此在M arr视觉理论基础上定义了一个新的具有视觉意义的尺度空间信息度量,并在典型的高斯尺度空间中,证明了它确实满足从视觉理论和直观的基础上提出的尺度空间信息量度量的基本要求。数值试验验证了这种定义在视觉上是可靠的,从而为图像尺度的自适应选择提供了一种可靠的方法。     

基于多尺度下特征点的检测

提出了一种在不同尺度空间下特征点提取的方法.该方法通过构造图像设高斯金字塔和高斯差分金字塔,进行极值检测,然后在极值点中去除低对比度的点并消除边界点的响应,得到关键点,最后计算关键点的方位和模的大小,从而得到特征点.利用该方法把取得的特征点对图像旋转、亮度变化、尺度缩放等情况下保持不变,此外对视角变化、仿射变换、噪声也保持一定的稳定性.给出了实验参数,并且对实验结果进行分析.     

抑制光照不均匀影响的图像特征点提取算法

为解决在光照不均匀情况下图像特征点提取算法表现效果不佳的问题,提出了一种改进的尺度不变特征转换（Scale Invariant Feature Transform,SIFT）算法抑制光照不均的影响。该方法在尺度空间构造中对输入的图像进行频域上的高斯高通滤波处理来滤除光照成分,并结合Top-hat变换弱化高斯滤波器参数选取难度,利用高斯卷积构建基于光照滤除与参数弱化的高斯差分金字塔,融合SIFT算法生成具有良好光照不变性的GT-SIFT描述子,进行特征点提取与匹配。实验结果表明,与传统算法相比改进算法在光照不均匀条件下具有更好的鲁棒性,图像特征点提取与匹配效果更好。     

基于分数阶微分的尺度不变特征变换图像匹配算法

利用分数阶微积分运算处理图像信息,有利于强化和提取图像的纹理细节,使图像得到增强,更有利于对图像特征的提取。为了提高图像匹配的正确性,用基于分数阶微积分图像处理方法,提出了改进的尺度不变特征变换(SIFT)匹配算法,将高斯滤波和分数阶微分滤波相结合,用分数阶微分对图像特征进行强化,检测出更加稳定的尺度空间极值点,然后筛选出更多和更准确的匹配特征点,最后进行图像匹配。实验表明,在SIFT中引入分数阶微积分的应用,能够得到更多的特征关键点,提高图像匹配的正确性。     

基于高斯金字塔的遥感云图多尺度特征提取

提出了一种针对可见光遥感图像云图的多尺度特征提取方法。该方法通过高斯金字塔将遥感云图分解到多尺度空间,以此为基础将图像的灰度特征进行多尺度延拓,从而得到图像的多尺度特征矢量。实验结果表明在相同的特征算法和分类器条件下,多尺度延拓能够提升分类精度,更加有效地实现云图和地物的分类。     

一种图像特征点匹配算法——Haar-Gaussian&NICD算法

提出了一种基于哈尔小波分解变换和高斯尺度空间的图像特征点匹配算法.首先利用哈尔小波变换对基础图像进行3层行列分解,然后利用高斯函数卷积核对这些分解图像,进行尺度变换.提出了一个小波高斯金字塔塔林的概念,即对通过小波变换产生的多张不同分辨率的基础图像分别进行高斯尺度变换进而产生一个个独立的高斯金字塔,进而产生独立的高斯差分金字塔林,完成特征点检测.再引进规范化强对比度描述子对特征点进行描述.结果表明:Haar-Gaussia&NICD算法的效果和SIFT算法相当,特征点数量优于SIFT算法,在局部特征匹配方面要更有优势;而且和NICD描述子搭配使用,在运行速度方面要比SIFT算法更快.     

基于高斯尺度空间粗糙度描述子的花粉图像分类识别

针对现有粗糙度描述子大多依赖于灰度值平均值,容易造成图像信息的丢失的问题,提出了一种新的基于高斯尺度空间粗糙度描述子的特征提取方法,并应用于花粉图像的分类和识别。首先,采用高斯金字塔算法,将花粉图像分割成不同层次的尺度空间;然后,在各个尺度空间上提取图像的粗糙度纹理特征;其次,通过计算粗糙度频率直方图的统计分布,提取不同尺度空间的粗糙度描述子(SSRHD);最后,采用欧氏距离计算图像的相似度。通过Confocal和Pollenmonitor图像库上的仿真结果表明,与基于隐马尔可夫模型的轮廓描述子(DHMMD)相比,该描述子在Confocal图像库上的平均正确识别率(CRR)提高了2.32%、平均错误识别率(FRR)降低了0.1%,而在Pollenmonitor图像库上的平均识别率也提高了1.2%。实验结果表明,该描述子能较好地描述花粉颗粒图像的纹理分布,对于花粉图像的旋转和姿态变化也具有良好的鲁棒性。     

基于多尺度HOG的草图检索

草图检索是图像处理领域中的重要研究内容。提出了一种将高斯金字塔和局部HOG特征融合的特征提取改进方法,并将其用于草图检索。采用高斯金字塔将图像分解到多尺度空间,在所有尺度上进行兴趣点提取,获得基于兴趣点的多尺度HOG特征。利用图像的多尺度HOG特征集生成视觉词典,最终形成与视觉词典相关的特征描述向量,通过相似度匹配实现草图检索。将该算法与单一尺度下的HOG算法及其他几种算法比较,实验结果表明了其可行性和有效性。     

Generalized Gaussian Scale-Space Axiomatics Comprising Linear Scale-Space,Affine Scale-Space and Spatio-Temporal Scale-Space

This paper describes a generalized axiomatic scale-space theory that makes it possible to derive the notions of linear scale-space, affine Gaussian scale-space and linear spatio-temporal scale-space using a similar set of assumptions (scale-space axioms).     

Scale-space properties of the multiscale morphologicaldilation-erosion

A multiscale morphological dilation-erosion smoothing operation and its associated scale-space expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scale-space monotonic property for signal extrema is demonstrated. Scale-space fingerprints from this approach have advantages over Gaussian scale-space fingerprints in that: they are defined for negative values of the scale parameter; have monotonic properties in two and higher dimensions; do not cause features to be shifted by the smoothing; and allow efficient computation. The application of reduced multiscale dilation-erosion fingerprints to the surface matching of terrain is demonstrated     

Exploring and exploiting the structure of saddle points in Gaussian scale space

When an image is filtered with a Gaussian of width σ and σ is considered as an extra dimension, the image is extended to a Gaussian scale-space (GSS) image. In earlier work it was shown that the GSS-image contains an intensity-based hierarchical structure that can be represented as a binary ordered rooted tree. Key elements in the construction of the tree are iso-intensity manifolds and scale-space saddles.A scale-space saddle is a critical point in scale space. When it connects two different parts of an iso-intensity manifold, it is called “dividing”, otherwise it is called “void”. Each dividing scale-space saddle is connected to an extremum in the original image via a curve in scale space containing critical points. Using the nesting of the iso-intensity manifolds in the GSS-image and the dividing scale-space saddles, each extremum is connected to another extremum. In the tree structure, the dividing scale-space saddles form the connecting elements in the hierarchy: they are the nodes of the tree. The extrema of the image form the leaves, while the critical curves are represented as the edges.To identify the dividing scale-space saddles, a global investigation of the scale-space saddles and the iso-intensity manifolds through them is needed.In this paper an overview of the situations that can occur is given. In each case it is shown how to distinguish between void and dividing scale-space saddles. Furthermore, examples are given, and the difference between selecting the dividing and the void scale-space saddles is shown. Also relevant geometric properties of GSS images are discussed, as well as their implications for algorithms used for the tree extraction.As main result, it is not necessary to search through the whole GSS image to find regions related to each relevant scale-space saddle. This yields a considerable reduction in complexity and computation time, as shown in two examples.     

Scale-space derived from B-splines

This paper proposes a scale-space theory based on B-spline kernels. Our aim is twofold: 1) present a general framework, and show how B-splines provide a flexible tool to design various scale-space representations. In particular, we focus on the design of continuous scale-space and dyadic scale-space frame representations. A general algorithm is presented for fast implementation of continuous scale-space at rational scales. In the dyadic case, efficient frame algorithms are derived using B-spline techniques to analyze the geometry of an image. The relationship between several scale-space approaches is explored. The behavior of edge models, the properties of completeness, causality, and other properties in such a scale-space representation are examined in the framework of B-splines. It is shown that, besides the good properties inherited from the Gaussian kernel, the B-spline derived scale-space exhibits many advantages for modeling visual mechanism including the efficiency, compactness, orientation feature and parallel structure     

Non-Linear Scale-Spaces Isomorphic to the Linear Case with Applications to Scalar,Vector and Multispectral Images

A basic requirement of scale-space representations in general is that of scale causality, which states that local extrema in the image should not be enhanced when resolution is diminished. We consider a special class of nonlinear scale-spaces consistent with this constraint, which can be linearised by a suitable isomorphism in the grey-scale domain so as to reproduce the familiar Gaussian scale-space. We consider instances in which nonlinear representations may be the preferred choice, as well as instances in which they enter by necessity. We also establish their relation to morphological scale-space representations based on a quadratic structuring function.     

Linear Scale-Space has First been Proposed in Japan

Linear scale-space is considered to be a modern bottom-up tool in computer vision. The American and European vision community, however, is unaware of the fact that it has already been axiomatically derived in 1959 in a Japanese paper by Taizo Iijima. This result formed the starting point of vast linear scale-space research in Japan ranging from various axiomatic derivations over deep structure analysis to applications to optical character recognition. Since the outcomes of these activities are unknown to western scale-space researchers, we give an overview of the contribution to the development of linear scale-space theories and analyses. In particular, we review four Japanese axiomatic approaches that substantiate linear scale-space theories proposed between 1959 and 1981. By juxtaposing them to ten American or European axiomatics, we present an overview of the state-of-the-art in Gaussian scale-space axiomatics. Furthermore, we show that many techniques for analysing linear scale-space have also been pioneered by Japanese researchers.     

Scale-space for discrete signals

A basic and extensive treatment of discrete aspects of the scale-space theory is presented. A genuinely discrete scale-space theory is developed and its connection to the continuous scale-space theory is explained. Special attention is given to discretization effects, which occur when results from the continuous scale-space theory are to be implemented computationally. The 1D problem is solved completely in an axiomatic manner. For the 2D problem, the author discusses how the 2D discrete scale space should be constructed. The main results are as follows: the proper way to apply the scale-space theory to discrete signals and discrete images is by discretization of the diffusion equation, not the convolution integral; the discrete scale space obtained in this way can be described by convolution with the kernel, which is the discrete analog of the Gaussian kernel, a scale-space implementation based on the sampled Gaussian kernel might lead to undesirable effects and computational problems, especially at fine levels of scale; the 1D discrete smoothing transformations can be characterized exactly and a complete catalogue is given; all finite support 1D discrete smoothing transformations arise from repeated averaging over two adjacent elements (the limit case of such an averaging process is described); and the symmetric 1D discrete smoothing kernels are nonnegative and unimodal, in both the spatial and the frequency domain     

Gauss比较函数与复杂遥感图像中线状目标的无偏自动检测

建立基于 Gauss 尺度空间的比较函数,对遥感图像中的道路结构进行特征描述、分离和定位.在此基础上,结合道路特征的图形和图像特征,提出了基于局部方向能量的线状目标检测算法,并根据道路的拓扑特征和几何特征进行假设验证、编组、融合,提取有效的道路线特征,应用于城市遥感图像中不同宽度和材质的主干道路和小路的提取.该算法计算复杂度小,在阴影遮挡和道路影像不明显的情况下对道路线特征具有良好的分辨能力.对 Gauss比较函数的定位和抗零点漂移性能也进行了详细的分析.     

The Monogenic Scale-Space: A Unifying Approach to Phase-Based Image Processing in Scale-Space

In this paper we address the topics of scale-space and phase-based image processing in a unifying framework. In contrast to the common opinion, the Gaussian kernel is not the unique choice for a linear scale-space. Instead, we chose the Poisson kernel since it is closely related to the monogenic signal, a 2D generalization of the analytic signal, where the Riesz transform replaces the Hilbert transform. The Riesz transform itself yields the flux of the Poisson scale-space and the combination of flux and scale-space, the monogenic scale-space, provides the local features phase-vector and attenuation in scale-space. Under certain assumptions, the latter two again form a monogenic scale-space which gives deeper insight to low-level image processing. In particular, we discuss edge detection by a new approach to phase congruency and its relation to amplitude based methods, reconstruction from local amplitude and local phase, and the evaluation of the local frequency.