基于小波变换的三维网格物体盲水印方案

提出了一种基于小波变换的3D网格物体鲁棒性盲水印方案.首先将3D物体模型转换到仿射不变空间,抽取三维物体重心到顶点的距离生成一个一维的离散信号;将该离散信号进行小波变换,改变其高频系数以嵌入水印,然后经过小波逆变换生成带水印的3D物体模型.在仿射不变空间下,实现了3D物体模型对平移、旋转、比例变换的鲁棒性,采用小波变换使3D模型具有很强的水印不可见性以及一定的噪声鲁棒性.试验结果表明,该方法不仅对于旋转、平移、比例变换具有很强的鲁棒性,而且具有良好的水印不可见性.     

一种DCT变换的三维网格物体盲水印方法

提出了一种基于DCT变换的3D网格物体鲁棒性盲水印方案。首先将3D物体模型转换到仿射不变空间,抽取三维物体重心到顶点的距离生成一个一维的离散信号,将该离散信号进行DCT变换,改变其系数以嵌入水印。然后经过逆DCT变换生成带水印的3D物体模型。在仿射不变空间下,实现了3D物体模型对平移、旋转、比例变换的鲁棒性,采用DCT变换使3D模型具有很强的水印不可见性,而且具有一定的噪声鲁棒性。试验结果也表明该方法不仅对于旋转、平移、比例变换具有很强的鲁棒性,而且具有良好的水印不可见性。     

基于三维网格物体的鲁棒性盲水印方案

提出了一种基于3D网格物体的鲁棒性盲水印方案。先将3D物体模型转换到仿射不变空间，然后把顶点坐标转换到球面坐标，利用球面坐标来嵌入水印。由于在仿射不变空间下，3D物体模型不受平移、旋转和比例变换的影响，故该水印方案对于此类变化具有很强的鲁棒性。试验结果也证明了这一点，且该水印具有良好的不可见性。     

Wavelet-based affine invariant representation: a tool forrecognizing planar objects in 3D space

A technique is developed to construct a representation of planar objects undergoing a general affine transformation. The representation can be used to describe planar or nearly planar objects in a three-dimensional space, observed by a camera under arbitrary orientations. The technique is based upon object contours, parameterized by an affine invariant parameter and the dyadic wavelet transform. The role of the wavelet transform is the extraction of multiresolution affine invariant features from the affine invariant contour representation. A dissimilarity function is also developed and used to distinguish among different object representations. This function makes use of the extrema on the representations, thus making its computation very efficient. A study of the effect of using different wavelet functions and their order or vanishing moments is also carried out. Experimental results show that the performance of the proposed representation is better than that of other existing methods, particularly when objects are heavily corrupted with noise     

Part I: Modeling image curves using invariant 3-D object curvemodels-a path to 3-D recognition and shape estimation from imagecontours

This paper and its companion are concerned with the problems of 3-D object recognition and shape estimation from image curves using a 3-D object curve model that is invariant to affine transformation onto the image space, and a binocular stereo imaging system. The objects of interest here are the ones that have markings (e.g., characters, letters, special drawings and symbols, etc.) on their surfaces. The 3-D curves on the object are modeled as B-splines, which are characterized by a set of parameters (the control points) from which the 3-D curve can be totally generated. The B-splines are invariant under affine transformations. That means that the affine projected object curve onto the image space is a B-spline whose control points are related to the object control points through the affine transformation. Part I deals with issues relating to the curve modeling process. In particular, the authors address the problems of estimating the control points from the data curve, and of deciding on the “best” order B-spline and the “best” number of control points to be used to model the image or object curve(s). A minimum mean-square error (mmse) estimation technique which is invariant to affine transformations is presented as a noniterative, simple, and fast approach for control point estimation. The “best” B-spline is decided upon using a Bayesian selection rule. Finally, we present a matching algorithm that allocates a sample curve to one of p prototype curves when the sample curve is an a priori unknown affine transformation of one of the prototype curves stored in the data base. The approach is tried on a variety of images of real objects     

Affine invariant comparison of point-sets using convex hulls and hausdorff distances

Many object recognition or identification applications involve comparing features associated with point-sets. This paper presents an affine invariant point-set matching technique which measures the similarity between two point-sets by embedding them into an affine invariant feature space. The developed technique assumes no a priori knowledge of reference points, as is the case in many identification problems. Reference points of a point-set are obtained based on its convex hull. An enhanced version of the Modified Hausdorff Distance is also introduced and used in the feature space for comparing two point-sets. It should be noted that the technique does not attempt to obtain correspondences between the point-sets. The introduced technique is applied to two real databases and its performance is found favorable as compared to three other affine invariant matching techniques.     

Multimorphing: A tool for shape synthesis and analysis

Morphing is a shape transformation where the shape of one object is deformed to the shape of the other object. It is used as an animation or a modeling technique. Classical morphing operates between two input objects but this concept can be extended to multiple input objects – the so called multimorphing. Shapes generated by the multimorphing form a space of shapes motivated by an affine space. Besides the analogy with an affine space we also introduce an inner product and a concept of an orthogonal projection. We also show how to explore space of shapes and how to systematically generate new shapes. The paper focuses on the boundary representation, although some ideas are more general and can be used for other representations, too.     

Stratification approach for 3-D euclidean reconstruction of nonrigid objects from uncalibrated image sequences.

This paper addresses the problem of 3-D reconstruction of nonrigid objects from uncalibrated image sequences. Under the assumption of affine camera and that the nonrigid object is composed of a rigid part and a deformation part, we propose a stratification approach to recover the structure of nonrigid objects by first reconstructing the structure in affine space and then upgrading it to the Euclidean space. The novelty and main features of the method lies in several aspects. First, we propose a deformation weight constraint to the problem and prove the invariability between the recovered structure and shape bases under this constraint. The constraint was not observed by previous studies. Second, we propose a constrained power factorization algorithm to recover the deformation structure in affine space. The algorithm overcomes some limitations of a previous singular-value-decomposition-based method. It can even work with missing data in the tracking matrix. Third, we propose to separate the rigid features from the deformation ones in 3-D affine space, which makes the detection more accurate and robust. The stratification matrix is estimated from the rigid features, which may relax the influence of large tracking errors in the deformation part. Extensive experiments on synthetic data and real sequences validate the proposed method and show improvements over existing solutions.     

Object tracking by energy maximization

We describe a robust algorithm for object tracking in long image sequences which extends the dynamic Hough transform to detect arbitrary shapes undergoing arbitrary affine motion. The proposed tracking algorithm processes the whole image sequence globally. First, the object boundary is represented in lookup-table form, and we then perform an operation that estimates the energy of the motion trajectory in the parameter space. We assign an extra term in our cost function to incorporate smoothness of changes due to rotation or scaling of the object. There is no need for training or initialization, and an efficient implementation can be achieved with coarse-to-fine dynamic programming and pruning. The method is shown to be robust under noise and occlusion and capable of tracking multiple objects.     

Fourier–Mellin registration of line-delineated tabular document images

Image registration (or alignment) is a useful preprocessing tool for assisting in manual data extraction from handwritten forms, as well as for preparing documents for batch OCR of specific page regions. A new technique is presented for fast registration of lined tabular document images in the presence of a global affine transformation, using the Discrete Fourier--Mellin Transform (DFMT). Each component of the affine transform is handled separately, which dramatically reduces the total parameter space of the problem. This method is robust and deals with all components of the affine transform in a uniform way by working in the frequency domain. The DFMT is extended to handle shear, which can approximate a small amount of perspective distortion. In order to limit registration to foreground pixels only, and to eliminate Fourier edge effects, a novel, locally adaptive foreground-background segmentation algorithm is introduced, based on the median filter, which eliminates the need for Blackman windowing as usually required by DFMT image registration. A novel information-theoretic optimization of the median filter is presented. An original method is demonstrated for automatically obtaining blank document templates from a set of registered document images.