基于张量的图象边缘检测及滤波

边缘检测是数字图象处理中一种重要的处理手段，目前普遍采用的方法是基于直角坐标系下的Laplacian算子，取其零交叉位置来检测。而现实中，往往可见到许多曲面上的图象，比如柱面贴图，球面贴图，动态景物的数字化效果以及三维医学图象，结合深度信息采用基于微分几何曲线坐标系下的算子进行图象边缘检测及滤波是一种新的尝试，从结果可以看到，处理效果明显优于普通的方法。     

基于边缘提取的分形图象编码方法

为了提高图象边缘提取时分类的准确性,在利用四叉树进行自适应图象分割的同时,将Laplacian算子作用于原始图象,然后从得到的边缘图像中,除去给定阈值的特定灰度层的图像后,即得到一种去除冗余,并突出边缘特征的图象,再将它应用于Domain块分类之中,可使分类结果更准确和更具客观性,计算机仿真解码实验结果表明,与其他同类的自动分形方法相比,该方法在编码速度、压缩比和恢复图象质量等方面均有显著提高。     

结合Canny算子的图像二值化方法

用Robert、Sobel、Prewitt、Laplacian、LoG和Canny算子,对图像进行了边缘检测实验,分析了边缘检测的结果,得出Canny边缘检测算子,是图像边缘检测的一种十分有效的方法。然后,根据图像的Canny算子边缘信息,将最佳全局阈值法与局部阈值自适应法在边缘信息的基础上融合起来,对图像进行了二值化处理。实验表明此算法在二值化图像时,能很好的保留图像的边缘信息。     

基于Laplacian算子的图像增强

使用Laplacian算子检测图像的边缘纹理等细节信息,然后以适当比例线性叠加原始图像和细节信息,从而完成图像增强。不同增强方法的比较试验表明,基于Laplacian算子的图像增强方法既能增强图像的高频分量,又能保持图像的低频分量,是图像增强的有效方法。     

Extracting image orientation feature by using integration operator

This paper presents an orientation operator to extract image local orientation features. We show that a proper employment of image integration leads to an unbiased orientation estimate, based on which an orientation operator is proposed. The resulting discrete operator has flexibility in the scale selection as the scale change does not violate the bias minimization criteria. An analytical formula is developed to compare orientation biases of various discrete operators. The proposed operator shows lower bias than eight well-known gradient operators. Experiments further demonstrate higher orientation accuracy of the proposed operator than these gradient operators.     

基于分数阶偏微分的图像边缘检测新模型

现有的图像边缘检测方法效果不太理想,得到的图像边缘可能模糊。针对此问题,将分数阶微分理论和现有的Laplacian算子方法相结合应用于图像边缘检测,提出了一种基于分数阶偏微分的图像边缘检测新模型。实验结果表明,相比现有的整数阶微分边缘检测方法,该模型不仅能较好地检测出图像的边缘特征,而且对噪声具有一定的抑制作用,尤其对于纹理细节丰富的图像而言,能够检测出更多的纹理细节信息,是一种比较有效的边缘检测方法。     

基于Gabor滤波器的图像边缘检测算法的研究

采用Canny算子进行边缘检测时,需人工设定高低2个阈值,对不同的图像采用相同的阈值,边缘检测效果差异很大。这一点限制了Canny算子在实际中的应用。针对这一问题,本文提出了一种基于Gabor奇部滤波器的边缘检测方法,在不同的尺度下分别采用非极大值抑制,然后利用Canny算子在高低阈值图像中查找边缘点的方法对图像进行边缘检测,无需人为的设定任何参数。实验结果表明这种算法不但克服了Canny算法中人为设定阈值的缺陷,而且能有效地去除了图像中的伪边缘。     

基于Canny算子的肤色分割在彩图中的人脸检测

结合Canny算子提取图像的边界,利用边界信息可以实现大块连通区域的自然分割,同时使用颜色插补的方法来代替传统的金字塔采样,让模板自动匹配经上述分割后的各个待测区域。实验结果证实该方法对于包含部分遮挡、肤色干扰等较复杂环境下的图片的检测率有很大提高。     

基于改进的四叉树Sobel算子图像分割研究

研究图像分割精度提高问题,由于图像噪声的干扰,造成图像模糊。传统的Sobel图像分割方法容易造成图像分割不清晰和对比度不明显。为解决上述问题,应用Sobel算子检测出数字图像真正的边缘,并进行边缘提取;最后运用四叉树算法对去噪图像进行多区域目标分割,并对分割的不同区域进行合并,有效地解决了因局部图像的叠加和分割不准确的难题,并且通过仿真实验显示,本文提出的改进型算法,也就是利用四叉树Sobel算子,能够有效地去除图像分割所带来的去除噪声,大大地提高升图像边缘的清晰度,是一种有效地图像分割方法。     

Laplacian colormaps: a framework for structure‐preserving color transformations

Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color‐blind people. Simple color transformations often result in information loss and ambiguities, and one wishes to find an image‐specific transformation that would preserve as much as possible the structure of the original image in the target color space. In this paper, we propose Laplacian colormaps, a generic framework for structure‐preserving color transformations between images. We use the image Laplacian to capture the structural information, and show that if the color transformation between two images preserves the structure, the respective Laplacians have similar eigenvectors, or in other words, are approximately jointly diagonalizable. Employing the relation between joint diagonalizability and commutativity of matrices, we use Laplacians commutativity as a criterion of color mapping quality and minimize it w.r.t. the parameters of a color transformation to achieve optimal structure preservation. We show numerous applications of our approach, including color‐to‐gray conversion, gamut mapping, multispectral image fusion, and image optimization for color deficient viewers.     

用不变矩和边界方向进行形状检索

基于形状的图像检索一直以来是图像内容检索的一个难点问题，而目前采用周长、面积、边角率等描述形状的方法不能使形状检索达到理想的效果．本文提出了一种新的针对图像形状的检索方法．首先，用Canny算子对图像进行平滑处理，提取图像边界方向直方图特征、其次，用不变矩来描述图像形状的区域特征，不变矩特征不受图像的缩放、平移和旋转的影响．最后，为了克服不变矩只关心对象区域，而对图像边界忽视的缺点，提出了不变矩与边界方向特征相结合的方法，使得检索取得更好的效果．本文通过对医学图像的形状检索实验，给出了实验结果和结论．     

基于三角模融合算子的指纹图像分割方法

针对指纹图像提出了一种基于三角模融合算子的图像分割算法。该方法使用方向性和对比度两个信息分别作为两个分类器的特征，并求出各分类器的判决信度，利用三角模融合算子将两个分类器的结果进行融合判决。该方法具有较高的稳健性和精确度。最后给出的实验结果验证了算法的有效性。     

基于SIFT的图像匹配算法

直接用SIFT算法对较模糊图像进行关键点提取时,提取的关键点个数较少且进行下一步匹配时错误匹配较多。提出一种基于SIFT特征的匹配算法,首先利用拉普拉斯算子对图像进行锐化处理,使其边缘得到突出,然后利用SIFT算法进行关键点提取,最后利用双向匹配算法进行图像匹配。实验结果表明,利用本文算法进行匹配比直接用SIFT算法进行匹配时的匹配点数更多,匹配效果更好。     

基于Laplacian金字塔和小波变换的医学CT图像增强算法

对医学图像进行增强可提高信息的利用率。传统的图像增强方法应用于医学图像时处理效果一般,存在诸多问题,如在增强图像的同时使图像的细节丢失,减弱了图像中目标的边缘信息,降低了图像的对比度。针对上述问题,提出一种基于小波变换和Laplacian金字塔分解的图像增强算法。首先,对原医学图像进行小波变换分解,得到处理结果;然后,对原医学图像进行Laplacian金字塔分解,得到医学图像的高频信息;最后,利用小波变换的结果和Laplacian金字塔分解的结果进行重构,得到增强后的图像。实验结果表明,该方法的增强效果明显优于传统的图像增强算法,对医学图像具有较好的增强效果,同时能更好地抵抗噪声。     

基于迭代重加权的非刚性图像配准

非刚性图像配准问题是当今重要的研究课题. 本文提出一类基于能量最小化方法的非刚性图像配准模型, 其中包括单模态和多模态两个模型. 在单模态模型中,正则项采用迭代重加权的L2范数度量, 一方面克服了迭代收敛不同步的问题, 另一方面使新模型既能保持图像的边缘几何结构, 又能避免块效应的产生. 在多模态模型中, 不同模态的图像被转化为同一模态进行处理, 提高了配准的效率. 在模型求解方面, 利用算子分裂和交替最小化的方法, 将原问题转化为阈值和加性算子分裂的迭代格式进行求解. 数值实验表明, 本文的方法对含噪以及变形较大的图像都能实现较好的配准.     

Improved anti-forensics of JPEG compression

The comblike histogram of DCT coefficients on each subband and the blocking artifacts among adjacent blocks are the two main fingerprints of the image that was once compressed by JPEG. Stamm and Liu proposed an anti-forensics method for removing these fingerprints by dithering the DCT coefficients and adding noise into the pixels. However, some defects emerge inside the anti-forensically processed images. First, the noise distributions are abnormal in the resulting images; and second, the quality of the processed image is poor compared with the original image. To fill these gaps, this paper proposes an improved anti-forensics method for JPEG compression. After analyzing the noise distribution, we propose a denoising algorithm to remove the grainy noise caused by image dithering, and a deblocking algorithm to combat Fan and Queiroz's forensics method against blocking artifacts. With the proposed anti-forensics method, fingerprints of the comblike histograms and the blocking artifacts are removed, noise distribution abnormality is avoided, and the quality of the processed image is improved.     

Segmentation and adaptive assimilation for detail-preserving display of high-dynamic range images

Realistic display of high-dynamic range images is a difficult problem. Previous methods for high-dynamic range image display suffer from halo artifacts or are computationally expensive. We present a novel method for computing local adaptation luminance that can be used with several different visual adaptation-based tone-reproduction operators for displaying visually accurate high-dynamic range images. The method uses fast image segmentation, grouping, and graph operations to generate local adaptation luminance. Results on several images show excellent dynamic range compression, while preserving detail without the presence of halo artifacts. With adaptive assimilation, the method can be configured to bring out a high-dynamic range appearance in the display image. The method is efficient in terms of processor and memory use.     

A Riemannian approach to graph embedding

In this paper, we make use of the relationship between the Laplace-Beltrami operator and the graph Laplacian, for the purposes of embedding a graph onto a Riemannian manifold. To embark on this study, we review some of the basics of Riemannian geometry and explain the relationship between the Laplace-Beltrami operator and the graph Laplacian. Using the properties of Jacobi fields, we show how to compute an edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes. For the particular case of a constant sectional curvature surface, we use the Kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points. We use the resulting edge-weight matrix to embed the nodes of the graph onto a Riemannian manifold. To do this, we develop a method that can be used to perform double centring on the Laplacian matrix computed from the edge-weights. The embedding coordinates are given by the eigenvectors of the centred Laplacian. With the set of embedding coordinates at hand, a number of graph manipulation tasks can be performed. In this paper, we are primarily interested in graph-matching. We recast the graph-matching problem as that of aligning pairs of manifolds subject to a geometric transformation. We show that this transformation is Pro-crustean in nature. We illustrate the utility of the method on image matching using the COIL database.     

Author's Reply

We present evidence that the Laplacian zero-crossing operator does not use neighborhood information as effectively as the second directional derivative edge operator. We show that the use of a Gaussian smoother with standard deviation 5.0 for the Laplacian of a Gaussian edge operator with a neighborhood size of 50 × 50 both misses and misplaces edges on an aerial image of a mobile home park. Contrary to Grimson and Hildreth's results, our results of the Laplacian edge detector on a noisy test checkerboard image are also not as good as the second directional derivative edge operator. We conclude by discussing a number of open issues on edge operator evaluation.