一种新的直方图核函数及在图像分类中的应用

基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用,运用支持向量机的关键是设计有效的核函数。为克服传统核函数较少融合先验知识的弱点,该文提出基于数据驱动的核函数构建方法;并结合词包(BOW)模型,设计了一种基于TF-IDF规则的加权二次卡方(Weighted Quadritic Chisquared, WQC)距离的直方图核函数;在计算直方图之间距离时充分考虑到不同量化区间的不同区分性能,从而增强核函数对不同类别的区分能力。在Caltech101/256等多个经典图像数据集上的分类实验结果验证了该文方法的有效性。     

基于三维激光扫描的低照度图像色彩增强方法

为了提高图像画面的清晰度，给用户展示更多细节，提出了基于三维激光扫描的低照度图像色彩增强方法。利用图像直方图分析灰度级，通过直方图处理实现低照度图像对比度增强，使所有灰度区域的均匀布局；通过多维信息算出协方差，利用矩阵构成相关数据，在相邻点实行空间曲面拟合，构建平面图像函数，将连续图像函数变换成离散形式，扫描图像找到相应的行列数，判断点位置获得三维激光图像矩阵。通过实验可知，本方法的图像标准差与实际图像差值仅为0.25,信息熵差值仅为0.1,平均梯差值仅为0.05,表明本方法能够增强低照度图像色彩。     

核加权RX高光谱图像异常检测算法

提出了一种新的基于混合核函数的加权RX算法,用于高光谱图像异常检测.在将原始高光谱数据非线性映射到高维特征空间以挖掘高光谱图像波段间蕴含的非线性信息后,自适应地赋予特征空间RX算子中采样协方差矩阵各光谱向量相应的权值.权值的大小与光谱向量到质心的距离成反比,从而削减了协方差矩阵中异常数据比重,使加权协方差矩阵更好地表征背景数据分布.最后利用核函数性质将高维特征空间的内积运算转化为低维输入空间的核函数计算,并根据高光谱数据特点线性组合新型光谱核函数和径向基核函数以改善算法性能.为验证算法的有效性,利用真实的高光谱数据进行了仿真实验,结果表明该算法优于特征空间的RX算法,能检测到更多的异常目标.     

基于SVM的图像分类算法优化实现

支持向量机(Support Vector Machine,SVM)是以核函数为载体的机器学习方法,集优化、核、最佳推广能力等特点于一身,目前比较广泛的一个应用是数字图像分类,具体的步骤是:先用词袋模型对数字图像特征进行组织,再构造核函数进行训练、学习,然后分类。在整个过程中,对最后分类结果起到关键作用的分别是核函数的构造和分类器核参数的选择,为解决核参数大多依靠经验选取或者大范围网络搜索耗时等问题,引入群智能算法来优化核参数,使得模型性能达到最优。最后选用Caltech 101、Caltech 256中的经典图像数据集做分类实验,以验证其核参数优化方法的有效性。     

基于成对约束的混合核函数KFCM图像分割算法

目前一些基于模糊核聚类的图像分割方法得到了大量研究,但难以有效地解决核方法中的参数合理选择问题,分割结果受到核参数人为主观选择的制约,不能达到分割的自适应性和良好性.通过提出一种基于成对约束的混合高斯核的方法来解决上述问题.将传统的高斯核函数改进为混合核函数,该混合核函数由多个不同核参数的高斯核函数组成,对于该混合核函数采用基于成对约束的类别信息算法求解其中的核参数和权重系数,进而采用该混合核函数对图像进行聚类分割.实验结果表明:该方法成功解决了模糊核聚类中核参数的选择问题,使得聚类更具有自适应性,而且由该混合核参数得到的图像分割结果更为鲁棒和准确.     

凹凸范数比值正则化的快速图像盲去模糊

模糊图像可表示为清晰图像和模糊核函数的卷积,由模糊图像恢复出清晰图像,需要同时估计模糊核和清晰图像,因此是一个病态问题.优化含有先验项的代价函数是求解病态问题最有效方法之一.针对图像盲去模糊问题,本研究提出具有更强稀疏表达能力的凹凸范数比值正则化先验项,在用变量分裂法求解模型时,提出用L1范数保真项更新估计图像,在更新模糊核时,提出使用线性递增权重参数对模糊核按多尺度方法由粗到细逐步估计,当获得模糊核后,利用封闭阈值公式估计清晰图像.该方法能快速得到高质量的清晰图像,实验结果验证了模型的有效性和算法的快速性.     

一种基于稀疏编码的多核学习图像分类方法

 本文提出一种基于稀疏编码的多核学习图像分类方法.传统稀疏编码方法对图像进行分类时,损失了空间信息,本文采用对图像进行空间金字塔多划分方式为特征加入空间信息限制.在利用非线性SVM方法进行图像分类时,空间金字塔的各层分别形成一个核矩阵,本文使用多核学习方法求解各个核矩阵的权重,通过核矩阵的线性组合来获取能够对整个分类集区分能力最强的核矩阵.实验结果表明了本文所提出图像分类方法的有效性和鲁棒性.对Scene Categories场景数据集可以达到83.10%的分类准确率,这是当前该数据集上能达到的最高分类准确率.     

基于二维再生核理论的图像平滑方法

本文给出了应用扩散方程进行图像平滑的再生核方法.基于再生核理论进行图像平滑,由于再生核函数的导数是小波函数,具有良好的局部性,与常规的对扩散方程差分化进行求解相比,用再生核方法求解扩散方程具有精度高,误差传播速度慢,对时间步长不敏感等优点.同时本文给出了用W2空间再生核求解Alvarez模型的方法.实验表明该方法具有图像清晰、图像平滑效果好等优点,本文的求解方法是有效的.     

图像特征提取中领域尺寸和本征维数的自动选择算法

本文首先对图像特征提取过程中的近邻距离进行线 性重构,然后对其在整个流形上的分布进行优化运算,得到一个最优线性重构权为变量的图 像数据局部低维特征的表达函数。最后以该函数稳定性的最小度量构造出适用于图像特征提 取的邻域尺寸和本征维数的自动选择策略。实验表明该方法实现了图像数据本征维数与邻域 尺寸的自动化选择,并具有计算简单、匹配率高且计算复杂性低等特点。     

空间一致性约束谱聚类算法用于图像分割

近来出现的谱聚类算法在模式识别和图像分割中得到了广泛应用.与传统的聚类算法相比,谱聚类算法能在任意形状的样本空间上聚类且收敛于全局最优解.本研究从谱聚类和权核K-均值的等价性出发,基于图像的空间一致特性,提出了一种基于空间约束特性的谱聚类算法.该算法通过对加权核K-均值的目标函数加上空间一致约束项,利用近似逼近将目标函数最小化与谱聚类算法等价起来.仿真实验表明,此算法在图像分割中取得了比原始谱聚类算法更好的分割效果.     

Image interpolation by two-dimensional parametric cubic convolution.

Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] x [-2, 2] with constraints for biaxial symmetry, diagonal (or 90 degrees rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.     

Image downscaling via co-occurrence learning

Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filtering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the filtering operation. The co-occurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The idea is further extended to the case of fractions factor of downscaling. The resulting images retain visually-important details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.     

基于小波核主成分分析的相关向量机高光谱图像分类

相关向量机(RVM)高光谱图像分类是一种较新的高光谱图像分类方法,然而算法本身存在对于高维大样本数据训练时间过长、分类精度不高的问题。针对这些问题,该文提出一种基于新型核主成分分析的RVM分类方法。该方法首先将核函数引入到主成分分析中,然后应用小波核函数代替传统核函数,利用小波核函数的多分辨率分析特点,进一步提高核主成分分析(KPCA)非线性映射能力,最终将新型核主成分分析算法与相关向量机相结合,对高光谱图像进行分类。仿真实验结果表明,将所提出的方法应用于AVIRIS美国印第安纳州实验田高光谱数据预处理后,类内类间距离比降低20%,方差整体增幅较大,最终将处理后的数据应用于相关向量机的高光谱图像分类中,分类精度提升3%~5%。     

基于FPGA的图像缩放技术分析

针对轨道交通调度显示系统中监控视频控制器的实际需求,介绍了双3次插值图像放缩算法和抗混叠滤波器的原理,提出了作为视频控制器核心的图像缩放技术的功能划分,分析了基于现场可编程门阵列(FPGA)的图像缩放技术实现的工作流程,论述了输入缓冲、图像缩放、帧频调整和图像拼接等功能模块的详细功能和实现方案,完成了对该技术实现的仿真分析,比较了该技术实现与传统图像缩放技术的仿真结果。     

Image blind restoration based on degradation representation network

Most deep learning (DL)-based image restoration methods have exploited excellent performance by learning a non-linear mapping function from low quality images to high quality images. However, two major problems restrict the development of the image restoration methods. First, most existing methods based on fixed degradation suffer from significant performance drop when facing the unknown degradation, because of the huge gap between the fixed degradation and the unknown degradation. Second, the unknown-degradation estimation may lead to restoration task failure due to uncertain estimation errors. To handle the unknown degradation in the real application, we introduce a degradation representation network for single image blind restoration (DRN). Different from the methods of estimating pixel space, we use an encoder network to learn abstract representations for estimating different degradation kernels in the representation space. Furthermore, a degradation perception module with flexible adaptability to different degradation kernels is used to restore more structural details. In our experiments, we compare our DRN with several state-of-the-art methods for two image restoration tasks, including image super-resolution (SR) and image denoising. Quantitative results show that our degradation representation network is accurate and efficient for single image restoration.     

A new method for D-dimensional exact deconvolution

Deconvolution is an important problem of signal processing, and conventional approaches, including Fourier methods, have stability problems due to the zeros of the convolution kernel. We present a new method of multidimensional exact deconvolution. This method is always stable, even when the convolution kernel h(n) has zeros on the unit circle, and there exist closed-form solutions for the one-dimensional (1-D) case (D=1). For the multidimensional case (D>1), the proposed method yields stable solutions when det(h)=D. This solution set covers a portion of all possible convolution kernels, including the ones that have zeros on the multidimensional unit circle. This novel time-domain method is based on the fact that the convolution inverse of a first-order kernel can be found exactly in multidimensional space. Convolution inverses for higher order kernels are obtained using this fact and the zeros of the convolution kernel. The presented method is exact, stable, and computationally efficient. Several examples are given in order to show the performance of this method in 1-D and multidimensional cases     

Approximating large convolutions in digital images

Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function. This operation can be quite costly, especially when large kernels are involved. We present an algorithm for computing convolutions of this form, where the kernel of the binary convolution is derived from a convex polygon. Because the kernel is a geometric object, we allow the algorithm some flexibility in how it elects to digitize the convex kernel at each placement, as long as the digitization satisfies certain reasonable requirements. We say that such a convolution is valid. Given this flexibility we show that it is possible to compute binary convolutions more efficiently than would normally be possible for large kernels. Our main result is an algorithm which, given an mxn image and a k-sided convex polygonal kernel K, computes a valid convolution in O(kmn) time. Unlike standard algorithms for computing correlations and convolutions, the running time is independent of the area or perimeter of K, and our techniques do not rely on computing fast Fourier transforms. Our algorithm is based on a novel use of Bresenham's (1965) line-drawing algorithm and prefix-sums to update the convolution incrementally as the kernel is moved from one position to another across the image.     

Automatic Image Annotation Based on Generalized Relevance Models

This paper presents a generalized relevance model for automatic image annotation through learning the correlations between images and annotation keywords. Different from previous relevance models that can only propagate keywords from the training images to the test ones, the proposed model can perform extra keyword propagation among the test images. We also give a convergence analysis of the iterative algorithm inspired by the proposed model. Moreover, to estimate the joint probability of observing an image with possible annotation keywords, we define the inter-image relations through proposing a new spatial Markov kernel based on 2D Markov models. The main advantage of our spatial Markov kernel is that the intra-image context can be exploited for automatic image annotation, which is different from the traditional bag-of-words methods. Experiments on two standard image databases demonstrate that the proposed model outperforms the state-of-the-art annotation models.