基于再采样的图像重采样伪作检测

重采样是图像篡改中的一种典型操作,针对现有重采样伪作检测算法对于JPEG压缩格式的图像检测效果不理想,也无法准确估计其中的缩放因子的问题,提出了基于再采样的图像重采样检测算法。该算法先将待检验的JPEG图像以缩放因子小于1进行再次重采样,以削弱JPEG压缩对算法的影响,再利用重采样信号二阶导数具有的周期性来进行重采样操作检测。实验结果表明,该算法具有很强的抗JPEG压缩能力,同时能够准确估计真实的缩放因子。另外,该算法对于经过不同缩放因子而得到的图像进行合成时的重采样操作,也有明显的检测效果。     

双活动轮廓模型*

针对形变模型方法存在压力太大活动轮廓容易从弱边界处溢出,压力太小则不能推动活动轮廓移动的压力项选择,提出基于双活动轮廓的形变模型。该模型建立内外活动轮廓的对应和约束,根据约束控制内外活动轮廓的演化;并可捕捉凹陷区域边界,且通过内外活动轮廓界定出弱边界区域。     

Finsler active contours

In this paper, we propose an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the isotropic case, the Euclidean metric is locally multiplied by a scalar conformal factor based on image information such that the weighted length of curves lying on points of interest (typically edges) is small. The conformal factor which is chosen depends only upon position and is in this sense isotropic. While directional information has been studied previously for other segmentation frameworks, here we show that if one desires to add directionality in the conformal active contour framework, then one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming based schemes. Finally we demonstrate the technique by extracting roads from aerial imagery, blood vessels from medical angiograms, and neural tracts from diffusion-weighted magnetic resonance imagery.     

Morphing active contours

A method for deforming curves in a given image to a desired position in a second image is introduced. The algorithm is based on deforming the first image toward the second one via a partial differential equation (PDE), while tracking the deformation of the curves of interest in the first image with an additional, coupled PDE; both the images and the curves on the frame/slices of interest are used for tracking. The technique can be applied to object tracking and sequential segmentation. The topology of the deforming curve can change without any special topology handling procedures added to the scheme. This permits, for example, the automatic tracking of scenes where, due to occlusions, the topology of the objects of interest changes from frame to frame. In addition, this work introduces the concept of projecting velocities to obtain systems of coupled PDEs for image analysis applications. We show examples for object tracking and segmentation of electronic microscopy     

面向不平衡分类的IDP-SMOTE重采样算法

传统的分类算法在对不平衡数据进行分类时,容易导致少数类被错分。为了提高少数类样本的分类准确度,提出了一种基于改进密度峰值聚类的采样算法IDP-SMOTE。首先,采用Box-Cox变换和σ准则对密度峰值聚类算法进行改进,实现了聚类中心和离群点的自动判别;然后,将改进的密度峰值聚类算法与SMOTE升采样算法相结合,去除噪声数据,并基于少数类样本的局部密度和邻近距离,在子类的范围内合成采样数据。该算法有效避免了升采样导致的边界模糊,改善了类内不平衡及边界样本难以学习的问题,同时实现了自动聚类和重采样,防止了人为因素干扰。通过实验对比,验证了提出算法的有效性和自适应性。     

有效重采样的颜色编辑扩散

目的 颜色编辑扩散是图像处理领域中的一个重要研究内容。以往方法中,为了取得期望的编辑效果,要求交互选定的样本颜色尽可能丰富。为了降低输入条件,提出一种新的有效重采样的颜色编辑扩散技术。方法 首先对图像做超像素分割并提取每个超像素中心作为重采样样本。遵循超像素中心非边缘像素,只服从一种编辑模式的原则,计算其与每种编辑模式的相似度,如果与某种编辑模式相似度绝对占优,则该样本的编辑模式直接确定。对于待定样本,依照相邻相似样本服从相同编辑模式的思想来判断其归属。然后将重采样样本的颜色编辑遵循相似颜色编辑后仍相似的原则采用增量的方式扩散到整幅图像。扩散时每个像素自适应地选择样本的数量,以避免相似度低样本的干扰。结果 与以往的方法进行比较,该方法对输入条件的敏感度较低,且在相同的相对简单的输入条件下能取得更好的视觉效果,可视化的梯度显示细节保持也更理想。结论 该方法能有效简化用户交互,将稀疏的画笔像素的扩散转化为颜色信息相对丰富的超像素中心样本的扩散,且生成的图像能忠于编辑颜色及较好地保持图像细节。     

Coupled parametric active contours

We propose an extension of parametric active contours designed to track nonoccluding objects transiently touching each other, a task where both parametric and single level set-based methods usually fail. Our technique minimizes a cost functional that depends on all contours simultaneously and includes a penalty for contour overlaps. This scheme allows us to take advantage of known constraints on object topology, namely, that objects cannot merge. The coupled contours preserve the identity of previously isolated objects during and after a contact event, thus allowing segmentation and tracking to proceed as desired.     

Fast polygonal approximation of digitized curves

We describe a new technique for fast “scan-along” computation of piecewise linear approximations of digital curves in 2-space. Our method is derived from earlier work on the theory of minimum-perimeter polygonal approximations of digitized closed curves.We demonstrate the specialization of this technique to the case where the error is measured as the largest Hausdorff-Euclidean distance between the approximation and the given digitized curve. We illustrate the application of this procedure to the boundaries of the images of a lung and a rib in chest radiographs.     

Finding stable salient contours

Methods for generating maximally stable extremal regions are generalized to make intensity trees. Such trees may be computed quickly, but they are large so there is a need to select useful nodes within the tree. Methods for simplifying the tree are developed and it is shown that standard confidence tests may be applied to regions identified as parent and child nodes in the tree. These tests provide a principled way to edit the tree and hence control its size. One of the algorithms for simplifying trees is able to reduce the tree size by at least 90% while retaining important nodes. Furthermore the tree can be parsed to identify salient contours which are presented as generalisations of maximally stable extremal regions.     

Bootstrap quantile estimation via importance resampling

We propose an adaptive importance resampling algorithm for estimating bootstrap quantiles of general statistics. The algorithm is especially useful in estimating extreme quantiles and can be easily used to construct bootstrap confidence intervals. Empirical results on real and simulated data sets show that the proposed algorithm is not only superior to the uniform resampling approach, but may also provide more than an order of magnitude of computational efficiency gains.     

Adaptive potential active contours

In this paper potential active contours are presented as a new method of image segmentation. The concept of potential contour is a result of the relationship between active contour techniques and the methods of classifiers’ construction. The proposed method can be extended by the adaptation mechanism that allows changing the available class of the shapes dynamically. An original contribution is also the method of evaluation of segmentation results and methodology used for the parameters selection. The described method is illustrated by two examples.     

Parametric interpolation of digitized log segments

The correlation of lithostratigraphic units by mathematical and statistical methods is complicated by stratigraphic thickness variation between sections. To account for thickness differences, the shorter of the two series being compared can be stretched and resampled to obtain a curve segment of the same shape but longer and with the original sample interval.This operation can be performed in the time domain by transforming the series of points in homogeneous space and resampling the curve by manipulating the parameters of a B-spline curve approximation.The technique of stretching and interpolating a time series in this manner has several advantages to Fourier transformed frequency-domain techniques. With homogeneous transformations the time series need not be normalized and amplitude as well as length manipulations can be easily performed. The B-spline curve approximation has the property that interpolated points will have variance less than or equal to that of the original curve points. This smoothing capability plus its nonglobal nature make it ideal for curve interpolation.     

Interactive smoothing of digitized point data

A new approach to the problem of smoothing digitized points is presented. The smoothness of the input data is made visible by displaying the first and second differences. Local corrections are performed on the first- and second-difference difference curves. The improved original data points are then computed by integration.     

Image resampling detection based on texture classification

Multimedia Tools and Applications - This study presents a method for resampling detection. By combining texture analysis with resampling detection, the task of resampling detection is considered as...     

Splatting of non rectilinear volumes through stochastic resampling

The paper extends the conventional splatting algorithm for volume rendering non rectilinear grids. A stochastic sampling technique called Poisson sphere/ellipsoid is employed to adaptively resample a non rectilinear grid with a set of randomly distributed points whose energy support extents are well approximated by spheres or ellipsoids. Then volume rendered images can be generated by splatting the scalar values at the new sample points with filter kernels corresponding to these spheres and ellipsoids. Experiments have been carried out to investigate the image quality as well as the time/space efficiency of the new approach, and the results suggest that our approach can be regarded as an alternative for existing fast volume rendering techniques of non rectilinear grids     

一种改进重采样的粒子滤波算法

针对粒子滤波重采样过程中存在的粒子多样性丧失问题,提出一种改进重采样的粒子滤波算法。按照局部重采样算法对粒子进行分类,中等权值的粒子保持不变,大、小两种权值的粒子采用Thompson-Taylor算法进行随机线性组合产生新粒子。实验结果表明,该算法能在降低计算复杂度的同时不丧失粒子多样性,提高了滤波性能。     

Wide-beam SAR autofocus based on blind resampling

Long synthetic aperture time and large instantaneous beam can turn airborne synthetic aperture radar(SAR) autofocus into a wide-beam autofocus problem; i.e., the motion error is both range-and azimuth-spatial variant. A typical two-step Mo Co method cannot process wide-beam airborne SAR data.Moreover, traditional wide-beam SAR Mo Co algorithms, such as sub-aperture topography and aperturedependent(SATA), precise topography-and aperture-dependent(PTA), and frequency division(FD), are highly depen...     

电罗经数字化转换器的研制

对电罗经信号转换为符合NMEA0183航海标准的数字通信信号的转换器进行了设计和开发,重点对转换器的总体方案、ATmega48单片机资源配置、解调电路和电源电路进行了设计;分析了转换器内部的数据流,在此基础上对转换器的主程序进行详细设计。