联合Gabor和分形的医学影像图像局部特征描述

提出一种联合Gabor小波和分形的医学影像肿瘤外轮廓局部结构特征描述方法.该方法基于Meyer卡通-纹理图像分解模型,利用Gabor尺度空间核函数进行局部尺度描述,以相邻尺度曲率间特征差异度量尺度,通过轮廓局部分形维提取结构不规则特征.该方法可使对医学影像肿瘤外轮廓的描述更具诊断意义.     

基于Gabor滤波器和聚类的图像配准算法

提出一种基于多尺度、多方向Gabor滤波器提取图像局部不变特征并用AP聚类进行约束的配准算法。该方法首先利用Gabor尺度空间核函数对图像进行尺度空间分解,在每一层尺度图像的不同方向上提取Harris角点,在以Harris角点为中心的固定大小的搜索窗内搜索三维尺度空间的极值点作为局部特征点的位置和特征尺度;在特征子区域内用梯度描述特征点;将得到的两幅图像的特征点AP聚类分析,实现m:n的粗匹配,最终通过各类之间的欧式距离实现对应点的匹配,通过AP聚类可有效排除多相似内容的图像之间的误匹配。实验结果表明,该算法能够提取稳健的精确特征点,并且可以有效去除多相似内容图像带来的匹配误差,实现图像的配准。     

基于Hessian矩阵和Gabor函数的局部兴趣点检测

局部特征方法是基于内容的图像与视频检索的重要方法。提出一种新的基于Hessian矩阵和Gabor函数的尺度不变局部特征点检测方法(Hessian-Gabor Detector)。该方法首先利用基于Hessian矩阵的检测子定位图像在空间域上的候选特征点位置,然后用基于Gabor函数的算子检测候选兴趣点在尺度空间的特征尺度,从而获得具有尺度不变特性的局部特征点。实验证明,与DOG、Harris-Laplace等方法相比,计算简单。应用于图像匹配中,能够显著地提高匹配效率。     

基于Gabor多尺度空间的不变兴趣点检测

针对以往仿射不变兴趣点的特征尺度不能直接断定的问题, 提出一种基于Gabor多尺度空间的不变兴趣点检测算法。该算法主要包括三个步骤：应用Gabor滤波器组与图像卷积建立图像Gabor多尺度空间; 通过极大值准则检测兴趣点并直接断定特征尺度; 采用二阶矩矩阵描述兴趣点局部结构。实验结果表明, 相比较其他Hessian-Affine、MSER等算法, 该算法在图像模糊和JPEG压缩情况下可重复率和可匹配率均取得最好结果, 是一种能有效直接提取特征尺度的兴趣点检测算法。     

一种基于图像表观的鲁棒姿态估计方法

提出一种利用图像的表观特征进行头部姿态估计的方法.该方法首先使用了一维Gabor 滤波器对头部图像进行特征提取,然后对提取得到的一维Gabor 特征进一步使用了基于核函数的局部费舍尔判别分析方法增强特征的判别能力.与传统二维Gabor 特征相比,一维Gabor 特征除了在计算速度和存储空间上具有明显的优势以外,更与姿态紧密相关.而基于核函数的局部费舍尔判别分析方法,能够解决姿态问题中存在的非线性问题和多模态问题.大量的实验结果表明,该算法对于姿态估计问题是有效的.特别需要指出的是,该算法具有良好的推广能力     

基于Gabor特征量和核函数判决分析方法的人脸识别

研究了基于Gabor特征量和核函数判决方法的人脸识别方法,即首先利用Gabor滤波器组对输入样本进行处理,获得Gabor特征量;然后利用核函数判决方法实现人脸识别。Gabor滤波器组通过提取具有空间频率、空间位置和取向选择性的特征,较好克服了实际中由于表情和光照不同带来的变化;而核函数判决分析方法具有提取输入样本空间的非线性最佳鉴别特征的优点。实验仿真表明了该方法的有效性。     

基于多尺度融合的甲状腺结节图像特征提取

甲状腺结节是一种常见的多发病,超声技术是该疾病首选的检查方法。在超声图像中提取区分甲状腺结节良恶性的纹理特征并进行判别具有广阔的临床应用前景。双树复小波变换（Dual-tree complex wavelet transform,DT-CWT）和Gabor小波是纹理特征提取的常用方法。本文提出一种基于多尺度的DT-CWT和Gabor特征融合的甲状腺结节识别方法。该方法首先通过高斯金字塔将甲状腺超声图像分解到多尺度空间,然后提取图像的DT-CWT和Gabor的多尺度特征,最后实现特征融合。通过应用支持向量机（Support vector machine,SVM）分类器实现分类,验证特征提取方法的有效性。实验结果表明,本文提出的方法能达到较高的识别率。     

基于局部中频Gabor滤波器组的人脸识别

针对传统Gabor滤波器组在人脸识别过程中特征提取时间长、计算量大的问题,提出一种利用局部Gabor滤波器组进行人脸图像中频特征提取的方法.选择中频带的Gabor滤波器构造局部中频Gabor滤波器组;提取局部Gabor中频特征;采用线性判别分析法(linear discriminate analysis,LDA)进一步提取Fisher特征,得到图像的Gabor+ Fisher特征,利用最近邻法进行人脸图像识别.基于ORL和AR人脸库的实验结果表明,基于此局部Gabor滤波器组的人脸识别方法较传统的Gabor特征提取方法降低了40％的特征维数,加快了特征提取速度,提高了人脸识别率.     

一种SAR图像稳健特征点提取方法

针对合成孔径雷达(SAR)图像自动配准问题,提出了一种新的SAR图像特征点提取方法.先对SAR图像灰度值进行对数变换处理,将乘性噪声转化为加性噪声,然后利用Gabor滤波器取代高斯滤波器建立尺度空间,使SAR图像在低尺度仍较好地保留细节,增加了提取特征点数目,并设置了对比度双门限,有效地抑制了伪特征点,从而提高SAR图像配准的精度和速度.实验结果表明,SAR图像稳健特征点提取方法是有效的.     

基于非线性尺度空间的图像配准技术

针对尺度不变的二值化角点(BRISK)算法抗噪性能较低,未充分利用图像的边缘的问题,提出了一种基于非线性尺度空间的图像配准技术。该算法在构造尺度空间的时候采用非线性滤波器构造图像非线性尺度空间,同时利用AGAST算法在构建的非线性空间里提取具有显著特征的角点,结合旋转不变性的BRIEF算法构造128位的二值化描述子,采用汉明距离匹配描述子。实验结果表明,该算法能大幅度提高关键点的提取质量,获得了较好特征点重复检测率,增强了特征点鲁棒性以及提高了描述子的匹配率。     

Localized image watermarking based on feature points of scale-space representation

This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, the watermark is embedded after scale normalization according to the local characteristic scale. Characteristic scale is the maximum scale of the scale-space representation of an image at the feature point. By binding watermarking with the local characteristics of an image, resilience against affine transformations can be obtained easily. Experimental results show that the proposed method is robust against various image processing steps including affine transformations, cropping, filtering and JPEG compression.     

Temporal Scale Selection in Time-Causal Scale Space

When designing and developing scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect the extent of the underlying structures in the signal. This paper presents a theory and in-depth theoretical analysis about the scale selection properties of methods for automatically selecting local temporal scales in time-dependent signals based on local extrema over temporal scales of scale-normalized temporal derivative responses. Specifically, this paper develops a novel theoretical framework for performing such temporal scale selection over a time-causal and time-recursive temporal domain as is necessary when processing continuous video or audio streams in real time or when modelling biological perception. For a recently developed time-causal and time-recursive scale-space concept defined by convolution with a scale-invariant limit kernel, we show that it is possible to transfer a large number of the desirable scale selection properties that hold for the Gaussian scale-space concept over a non-causal temporal domain to this temporal scale-space concept over a truly time-causal domain. Specifically, we show that for this temporal scale-space concept, it is possible to achieve true temporal scale invariance although the temporal scale levels have to be discrete, which is a novel theoretical construction. The analysis starts from a detailed comparison of different temporal scale-space concepts and their relative advantages and disadvantages, leading the focus to a class of recently extended time-causal and time-recursive temporal scale-space concepts based on first-order integrators or equivalently truncated exponential kernels coupled in cascade. Specifically, by the discrete nature of the temporal scale levels in this class of time-causal scale-space concepts, we study two special cases of distributing the intermediate temporal scale levels, by using either a uniform distribution in terms of the variance of the composed temporal scale-space kernel or a logarithmic distribution. In the case of a uniform distribution of the temporal scale levels, we show that scale selection based on local extrema of scale-normalized derivatives over temporal scales makes it possible to estimate the temporal duration of sparse local features defined in terms of temporal extrema of first- or second-order temporal derivative responses. For dense features modelled as a sine wave, the lack of temporal scale invariance does, however, constitute a major limitation for handling dense temporal structures of different temporal duration in a uniform manner. In the case of a logarithmic distribution of the temporal scale levels, specifically taken to the limit of a time-causal limit kernel with an infinitely dense distribution of the temporal scale levels towards zero temporal scale, we show that it is possible to achieve true temporal scale invariance to handle dense features modelled as a sine wave in a uniform manner over different temporal durations of the temporal structures as well to achieve more general temporal scale invariance for any signal over any temporal scaling transformation with a scaling factor that is an integer power of the distribution parameter of the time-causal limit kernel. It is shown how these temporal scale selection properties developed for a pure temporal domain carry over to feature detectors defined over time-causal spatio-temporal and spectro-temporal domains.     

基于尺度不变特征点的抗几何攻击水印算法

利用基于尺度空间理论的特征点检测方法,检测出的特征点不但在大尺度缩放变换下重复率高,而且每个特征点的特征尺度随图像缩放尺度等比例变化。以特征尺度及特征点相对位置作为参考,生成一种几何变换自适应图形作为水印嵌入的区域。随着特征点相对位置的变化,自适应图形会产生相应变化,达到抗几何攻击的目的。实验表明,该算法能有效抵抗各种几何攻击,具有较好的性能。     

一种基于局部Gabor滤波器组的手写体汉字识别方法

针对单一尺度的Gabor滤波器组只对某一特定粗细的手写体汉字敏感的缺点，提出了一种新颖的多尺度局部Gabor滤波器组。为了评估该方法的识别性能，提出了一个基于Gabor特征的手写体汉字识别系统，实验表明多尺度全局Gabor滤波器组在识别性能上明显提高，局部Gabor滤波器组在基本保持识别性能的情况下，特征维数明显降低，计算量和内存需求减少。该方法的创新之处在于选取局部Gabor滤波器，对863 HCL2000手写体汉字数据库的识别，最高平均识别率达到了92.32%，表明了该方法在手写体汉字识别中的有效性。     

Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation in terms of recursive filters over time, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain a new family of temporal scale-space kernels with better temporal characteristics compared to a more traditional approach of using a uniform distribution of the intermediate temporal scale levels. Specifically, the new family of time-causal kernels has much faster temporal response properties (shorter temporal delays) compared to the kernels obtained from a uniform distribution. When increasing the number of temporal scale levels, the temporal scale-space kernels in the new family do also converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales. Thereby, the new representation allows for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.     

Feature Detection with Automatic Scale Selection

The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of -normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure.Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation.In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.     

基于局部连接度和差异度算子的水平集纹理图像分割

目的 复杂纹理的图像分割一直是图像分割的难题,现有的一些纹理图像分割方法主要通过提取图像确定方向的灰度变化特征或者提取图像的局部灰度相似性特征得到特征图像,从而进行纹理图像的分割,然而,自然纹理中普遍存在局部形态相似和方向不确定的现象,导致现有方法不能准确地分割纹理图像。方法 本文提出局部连接算子和局部差异算子来描述局部纹理的形态相似性和局部纹理的差异度。一方面,通过设定一定阈值,将局部区域的灰度差异分为两类,分析两类差异的分布特征,从而提取图像的形态特性及局部连接度算子;另一方面,设置一种无方向性的灰度差异分析算子,提取图像局部的灰度差异值从而得到局部差异度算子。两个算子结合以更好地提取纹理图像的局部特征,然后通过融合局部相似度特征、局部差异度特征和灰度信息,构造水平集能量泛函,进而通过最小化能量泛函实现纹理图像分割。结果 相比基于Gabor变换、结构张量、局部相似度因子的纹理分割方法,提出的局部算子能够更好地区分自然图像的不同纹理区域,且对实验图像的平均分割准确率高达97%,远高于其他方法。因此,提出的模型对于自然纹理图像具有更好的分割效果。结论 本文提出了两种新颖的纹理特征局部描述子：局部连接度算子和局部差异度算子,能够有效地提取纹理特征,且有一定的互补性。实验表明,提出的方法对于复杂自然纹理图像具有良好的分割效果。     

基于Gabor滤波和KPCA的人脸识别方法

尽管核主分量分析能够有效地提取非线性特征,并成功地应用于人脸识别,但是抽取对光照、表情不敏感的特征仍然是亟待解决的问题。该文提出了一种结合Gabor特征和核主分量分析的人脸识别方法。首先通过Gabor滤波器对人脸图像滤波,并通过实验分析了Gabor滤波器参数的选择,然后采用核主分量分析的方法降低Gabor特征的维数．最后采用最近邻分类器进行识别。由于采用了Gabor滤波,该方法对光照、表情具有鲁棒性,在ORL人脸库上的实验结果表强,该方法在识别性能上优于核主分量分析方法。