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1.
在Weber定律的启发下提出一种基于"恰可识别差异"的尺度空间自适应构建方法。Weber定律认为人类感知模式不仅与刺激的变化有关,而且与原刺激强度有关。基于此观点,在图像尺度空间的构建过程中,首先以Marr视觉理论的屋脊型边缘和阶梯型边缘的特征之和计算图像的信息量,再通过实验获得人类视觉能感知到"恰可识别差异"时的图像信息量变化,最后通过曲线拟合自适应构建图像的尺度空间。实验结果表明,该方法充分体现了人类视觉感知特性,与其他尺度空间构建方法相比,在目标匹配实验中,匹配数目提升了25%以上;在去噪实验中,本算法的去噪效果良好。 相似文献
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Non-Linear Scale-Spaces Isomorphic to the Linear Case with Applications to Scalar,Vector and Multispectral Images 总被引:1,自引:1,他引:0
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. 相似文献
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Saliency, Scale and Image Description 总被引:6,自引:0,他引:6
6.
本文提出了一种特征点检测方法,通过引入局部特性因子和层次因子分别实现了自适应特征点检测及分层描述,不需输入参数,避免了复杂的参数试探过程。由于采用的主要是几何信息,算法简单,且与人类视觉系统有着很好的一致性。试验结果比较理想。 相似文献
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Regularization, Scale-Space, and Edge Detection Filters 总被引:2,自引:0,他引:2
Mads Nielsen Luc Florack Rachid Deriche 《Journal of Mathematical Imaging and Vision》1997,7(4):291-307
Computational vision often needs to deal with derivatives ofdigital images. Such derivatives are not intrinsic properties ofdigital data; a paradigm is required to make them well-defined.Normally, a linear filtering is applied. This can be formulated interms of scale-space, functional minimization, or edge detectionfilters. The main emphasis of this paper is to connect these theoriesin order to gain insight in their similarities and differences. We donot want, in this paper, to take part in any discussion of how edgedetection must be performed, but will only link some of the current theories. We take regularization (or functional minimization) as astarting point, and show that it boils down to Gaussian scale-space ifwe require scale invariance and a semi-group constraint to besatisfied. This regularization implies the minimization of afunctional containing terms up to infinite order of differentiation.If the functional is truncated at second order, the Canny-Deriche filter arises. It is also shown that higher dimensional regularizationboils down to a rotated version of the one dimensional case, whenCartesian invariance is imposed and the image is vanishing at theborders. This means that the results from 1D regularization can beeasily generalized to higher dimensions. Finally we show how anefficient implementation of regularization of order n can be made byrecursive filtering using 2n multiplications and additions peroutput element without introducing any approximation. 相似文献
9.
Least Squares and Robust Estimation of Local Image Structure 总被引:5,自引:0,他引:5
Joost Van De Weijer Rein Van Den Boomgaard 《International Journal of Computer Vision》2005,64(2-3):143-155
Linear scale space methodology uses Gaussian probes at scale s to observe the differential structure. In observing the differential image structure through the Gaussian derivative probes at scale s we implicitly construct the Taylor series expansion of the smoothed image. The Gaussian facet model, as a generalization of the classic Haralick facet model, constructs a polynomial approximation of the unsmoothed image. The measured differential structure therefore is closer to the ‘real’ structure than the differential structure measured using Gaussian derivatives.At the points in an image where the differential structure changes abruptly (because of discontinuities in the imaging conditions, e.g. a material change, or a depth discontinuity) both the Gaussian derivatives and the Gaussian facet model diffuse the information from both sides of the discontinuity (smoothing across the edge).Robust estimators that are classically meant to deal with statistical outliers can also be used to deal with these ‘mixed model distributions’. In this paper we introduce the robust estimators of local image structure. Starting with the Gaussian facet model where we replace the quadratic error norm with a robust (Gaussian) error norm leads to a robust Gaussian facet model.We will show examples of using the robust differential structure estimators for luminance and color images, for zero and higher order differential structure. Furthermore we look at a ‘robustified’ structure tensor that forms the basis of robust orientation estimation.First online version published in June, 2005 相似文献
10.
Ulisses Braga-Neto 《Journal of Mathematical Imaging and Vision》2005,22(2-3):199-216
Among the major developments in Mathematical Morphology in the last two decades are the interrelated subjects of connectivity classes and connected operators. Braga-Neto and Goutsias have proposed an extension of the theory of connectivity classes to a multiscale setting, whereby one can assign connectivity to an object observed at different scales. In this paper, we study connected operators in the context of multiscale connectivity. We propose the notion of a -connected operator, that is, an operator connected at scale . We devote some attention to the study of binary -grain operators. In particular, we show that families of -grain openings and -grain closings, indexed by the connectivity scale parameter, are granulometries and anti-granulometries, respectively. We demonstrate the use of multiscale connected operators with image analysis applications. The first is the scale-space representation of grayscale images using multiscale levelings, where the role of scale is played by the connectivity scale. Then we discuss the application of multiscale connected openings in granulometric analysis, where both size and connectivity information are summarized. Finally, we describe an application of multiscale connected operators to an automatic target recognition problem.Ulisses Braga-Neto received the Baccalaureate degree in Electrical Engineering from the Universidade Federal de Pernambuco (UFPE), Brazil, in 1992, the Masters degree in Electrical Engineering from the Universidade Estadual de Campinas, Brazil, in 1994, the M.S.E. degree in Electrical and Computer Engineering and the M.S.E. degree in Mathematical Sciences, both from The Johns Hopkins University, in 1998, and the Ph.D. degree in Electrical and Computer Engineering, from The Johns Hopkins University, in 2001. He was a Post-Doctoral Fellow at the University of Texas MD Anderson Cancer Center and a Visiting Scholar at Texas A&M University, from 2002 to 2004. He is currently an Associate Researcher at the Aggeu Magalhães Research Center of the Osvaldo Cruz Foundation, Brazilian Ministry of Health. His research interests include Bioinformatics, Pattern Recognition, Image Analysis, and Mathematical Morphology. 相似文献