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。     

一种频域特征的飞机目标提取方法

针对军事遥感影像智能判读的目标定位与提取问题,将先验知识引入到目标提取过程中,提出一种基于频域特征的飞机目标提取方法.该方法通过绘制目标圆周剖面线将二维图像信号简化为一维信号,运用频谱能量的谐波叠置原理对飞机形状进行频谱分析,确定目标轮廓的固有频率;然后设计多尺度的Gabor滤波器组提取图像的纹理特征图,并进行特征图融合形成目标显著图;最后利用数学形态学的方法对目标显著图进行处理,得到最终的目标提取结果.实验结果表明,与基于无先验知识的视觉显著性目标检测方法相比,该方法抗干扰能力强,能够清楚、准确、完整地提取目标区域,是一种有效的飞机目标提取方法.     

基于二维图像三维重建的人脸特征提取技术研究

采用基于二维图像的三维重建对人脸特征进行提取.首先应用平行双目视觉原理获取人脸的二维图像,然后对图像进行预处理,消除图像上的噪音点,增强图像,以便提取特征点,对这些二维图像上的特征点进行优化计算,最后得到整体人脸的三维特征点信息.     

二维图像特征点自主提取算法研究

采用概率论理论,对二维图像进行灰度统计分析,采用计算标准差方法,对图像特征点区域定位并提取特征点.该方法提取特征点,仅需对抽样象素区域进行灰度标准差分析,避免了提取图像特征点,根据被处理图像的一些先验信息,利用试探方法确定阈值的局限性.通过分别对具有弱纹理及包含复杂背景的多物体自然二维图像的特征提取,证实了所提方法的有效性和可靠性,可满足机器视觉系统中自主、实时识别与提取二维图像特征点要求.     

基于特征块匹配的图像检索技术

提出了一种基于特征块匹配的图像检索算法.首先,利用小波变换的多尺度特性检测出图像的特征点,特征点比较全面地反映了图像中的视觉兴趣点;用以特征点为中心的特征块的前三阶颜色矩来描述特征块的特征;进一步统计出两个图像中匹配的特征块数目,计算图像间的相似距离.实验表明,算法中所使用的特征块更全面、更精确地描述了图像的视觉信息,实现相似度计算的方法简单、高效.该检索算法不仅检索精度高,还具有较好的旋转、尺度及视觉角度不变性.     

基于轮廓的图像检索

提出了一种针对多纹理图像的基于轮廓和纹理分割的检索策略.首先提取一幅图像中各个纹理基元的轮廓,计算轮廓的Fourier形状描绘子,根据形状描绘子对轮廓聚类分组.此时,原图像被分割成几组不同形状的纹理基元轮廓,采用Gabor小波变换分别提取各组纹理基元轮廓的特征,从而将原图像表示为Gabor小波特征空间中的特征点集.最后,采用对噪音不敏感的改进Hausdorff距离计算各特征点集之间的距离,便可实现多纹理图像的检索.与已有方法相比,实验结果表明,该方法具有更好的检索精度.     

一种新的基于Gabor小波的目标跟踪方法

针对利用Gabor小波进行目标特征点识别的过程中所选取的参数序列过多的问题,给出了一种新的参数选取方法。该方法利用Gabor多尺度参数,配合权重函数,通过能量函数计算出相应的方向参数,构成图像的特征点集合。这样得到的特征点集合,不但充分利用了Gabor小波变换中的尺度参数的特性,而且通过配合以特征点粗匹配算法,大大减轻了特征点序列过多所造成的算法耗时问题。仿真和实验表明,算法可广泛应用于各种图像目标识别与跟踪的系统中,具有良好的图像目标识别能力。     

基于局部纹理特征的钢卷侧面缺陷检测方法研究

针对钢卷侧面图像的纹理结构,提出了一套有效的钢卷侧面图像检测方法和一种新的Gabor滤波器参数设计方法,并将Gabor滤波器与LOG滤波器相结合对图像做预处理,有效的滤除了噪声并增强了图像纹理结构.另外,改进了基于"散度"的Zernike矩特征选取方法并结合SVM对预处理后的钢卷侧面图像子块分类,从而检测目标图像中是否含有缺陷.     

基于多项扩展式局部方向张量的兴趣点检测算子

针对移动机器人基于视觉的导航, 本文提出了一种具有尺度和旋转不变的新的兴趣点检测算法—PLOT算子(Polynomial Local Orientation Tensor). PLOT算子主要是基于图像信号多项扩展式的局部方向张量. 文中首先分析了PLOT算子中局部方向张量属性, 并选取合适的调节参数, 使得局部方向张量能够提取不变特征. 主要应用自主尺度选择计算局部方向张量, 进而确定特征尺度来获得尺度不变特征. 最终基于局部方向张量的最小特征值来检测真实兴趣点. 本文通过重复率准则评估了PLOT算子, 并与目前常用兴趣点检测算法作比较. 实验结果表明在实际场景图像中, PLOT算子对于旋转、尺度以及亮度变换表现很稳定.     

基于Gabor能量空间的尺度不变兴趣点检测方法(英文)

Interest point detection is a fundamental issue in many intermediate level vision problems and plays a significant role in vision systems. The previous interest point detectors are designed to detect some special image structures such as corners,junctions, line terminations and so on. These detectors based on some simplified 2D feature models will not work for image features that differ significantly from the models. In this paper, a scale invariant interest point detector, which is appropriate for most types of image features, is proposed based on an iterative method in the Gabor based energy space. It detects interest points by noting that there are some similarities in the phase domain for all types of image features, which are obtained by different detectors respectively. Firstly, this approach obtains the positions of candidate points by detecting the local maxima of a series of energy maps constructed by Gabor filter responses.Secondly, an iterative algorithm is adopted to select the corresponding characteristic scales and accurately locate the interest points simultaneously in the Gabor based energy space. Finally,in order to improve the real-time performance of the approach, a fast implementation of Gabor function is used to accelerate the process of energy space construction. Experiments show that this approach has a broader applicability than the other detectors and has a good performance under rotation and some other image changes.     

基于多通道Gabor滤波器的纹理图像非监督分类

提出了一种快速、实用的Gabor滤波器设计方法.首先根据人类视觉特性选定Gabor滤波器的频率带宽参数,利用图像大小确定滤波器的中心频率,构造一组多通道的Gabor滤波器在时域和频域中提取图像多方向和多分辨率的Gabor能量特征,对特征空间进行非线性变换和Gauss滤波处理,最后通过K means方法实现纹理图像的分割.实验结果表明,该方法能有较好的纹理分割效果.     

基于兴趣点的图像检索方法研究

提出了基于兴趣点的多种图像特征相结合的图像检索新方法。该方法利用兴趣点周围局部区域的环形颜色直方图和Gabor小波变换提取纹理特征作为刻画图像的主要特征,并结合兴趣点的空间分布对图像进行检索。该方法不仅弥补了单一特征无法真正表征图像的缺陷,还保证了检索算法对图像旋转、平移的识别不变性。与同类方法相比,该方法有效提高了图像检索的准确率。     

Interesting Interest Points

Not all interest points are equally interesting. The most valuable interest points lead to optimal performance of the computer vision method in which they are employed. But a measure of this kind will be dependent on the chosen vision application. We propose a more general performance measure based on spatial invariance of interest points under changing acquisition parameters by measuring the spatial recall rate. The scope of this paper is to investigate the performance of a number of existing well-established interest point detection methods. Automatic performance evaluation of interest points is hard because the true correspondence is generally unknown. We overcome this by providing an extensive data set with known spatial correspondence. The data is acquired with a camera mounted on a 6-axis industrial robot providing very accurate camera positioning. Furthermore the scene is scanned with a structured light scanner resulting in precise 3D surface information. In total 60 scenes are depicted ranging from model houses, building material, fruit and vegetables, fabric, printed media and more. Each scene is depicted from 119 camera positions and 19 individual LED illuminations are used for each position. The LED illumination provides the option for artificially relighting the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance of a number of interest point detectors. The highlights of the conclusions are that the fixed scale Harris corner detector performs overall best followed by the Hessian based detectors and the difference of Gaussian (DoG). The methods based on scale space features have an overall better performance than other methods especially when varying the distance to the scene, where especially FAST corner detector, Edge Based Regions (EBR) and Intensity Based Regions (IBR) have a poor performance. The performance of Maximally Stable Extremal Regions (MSER) is moderate. We observe a relatively large decline in performance with both changes in viewpoint and light direction. Some of our observations support previous findings while others contradict these findings.     

Interest point detection through multiobjective genetic programming

The detection of stable and informative image points is one of the most important low-level problems in modern computer vision. This paper proposes a multiobjective genetic programming (MO-GP) approach for the automatic synthesis of operators that detect interest points. The proposal is unique for interest point detection because it poses a MO formulation of the point detection problem. The search objectives for the MO-GP search consider three properties that are widely expressed as desirable for an interest point detector, these are: (1) stability; (2) point dispersion; and (3) high information content. The results suggest that the point detection task is a MO problem, and that different operators can provide different trade-offs among the objectives. In fact, MO-GP is able to find several sets of Pareto optimal operators, whose performance is validated on standardized procedures including an extensive test with 500 images; as a result, we could say that all solutions found by the system dominate previously man-made detectors in the Pareto sense. In conclusion, the MO formulation of the interest point detection problem provides the appropriate framework for the automatic design of image operators that achieve interesting trade-offs between relevant performance criteria that are meaningful for a variety of vision tasks.     

用兴趣点凸包和SVM加权反馈实现图像检索

针对采用环状颜色直方图的图像检索方法存在的不足,提出一种基于兴趣点凸包的图像特征提取方法,通过对用小波变换检测出的必趣点递归求出它们的凸包,并将每个凸包上的兴趣点按一定的算法安插在相应的桶内,对每个桶求出颜色直方图,利用桶与桶之间的相似度定义两幅图像的相似度.这种特征提取方法可有效抑制兴趣点集合中出现游离兴趣点的情况,结合基于兴趣点的空间离散度和Gabor小波纹理等特征实现图像检索,可有效提高图像检索精度.最后,提出一种新的相关反馈方法,通过利用支持向量机分类结果设置权值来改进移动查询点相关反馈方法.实际图像数据库上的实验表明,引入这种反馈方法后可将图像检索的查准率提高20%左右,查全率提高10%左右.