基于全局对比度的随机游走显著性视觉注意模型

模拟人类视觉的显著性视觉注意机制还没有形成统一的模型,依据对人类视觉的分析可知显眼、紧凑和对比度高的目标更加吸引人眼的注意,提出一种基于全局对比度结合随机游走的显著目标视觉注意算法,并将视觉显著性检测问题化为马尔科夫随机游走问题.首先计算输入图像的颜色和方向的全局对比度形成特征向量,利用向量间的距离确定图表示的边权重,从而构造随机游走模型的转移矩阵.同时通过全连通图随机游走和k_regular图随机游走提取图像的全局特性和局部特性,并将二者相结合得到显著图,从而确定显著目标.在国际上现有公开测试集上进行仿真实验,并与其它显著性视觉注意检测方法进行对比,结果表明,方法检测结果更加准确、合理,证明算法切实可行.     

基于全局对比度的随机游走显著性视觉注意模型北大核心

模拟人类视觉的显著性视觉注意机制还没有形成统一的模型,依据对人类视觉的分析可知显眼、紧凑和对比度高的目标更加吸引人眼的注意,提出一种基于全局对比度结合随机游走的显著目标视觉注意算法,并将视觉显著性检测问题化为马尔科夫随机游走问题.首先计算输入图像的颜色和方向的全局对比度形成特征向量,利用向量间的距离确定图表示的边权重,从而构造随机游走模型的转移矩阵.同时通过全连通图随机游走和k_regular图随机游走提取图像的全局特性和局部特性,并将二者相结合得到显著图,从而确定显著目标.在国际上现有公开测试集上进行仿真实验,并与其它显著性视觉注意检测方法进行对比,结果表明,方法检测结果更加准确、合理,证明算法切实可行.     

基于视觉注意机制和形状特征的交通标志检测方法

交通标志的颜色和形状与周围环境存在强烈的反差,利用这一特点,提出了一种基于视觉注意机制和形状特征的交通标志检测方法.首先进行图像预处理,然后采用自底向上的视觉注意模型,提取图像的颜色、亮度、方向等初级特征,将其线性组合得到显著图,得到交通标志的感兴趣域,实现交通标志的粗检测;在此基础上,根据交通标志的几何特征描述进一步检测感兴趣区域中的图形形状,实现交通标志的细检测.最后分别基于软件和嵌入式平台对算法的检测准确性和稳定性进行验证,结果表明,与已有算法相比,所提方法可以很好的克服光照或复杂环境的干扰,显著提高交通标志检测的准确率,具有一定的优越性.     

基于随机谱聚类的图像分割算法

图像分割就是把感兴趣的区域从背景中分割、提取出来,为了使分割出来的图像特征信息完整,根据图像的灰度值和空间距离构造了一种相似度函数,得到基于图的灰度值的相似度矩阵,将图像分割转化为图论最小割问题,然后运用谱聚类算法进行分割.针对谱聚类算法运行所需的内存空间和运算量大的特点,提出一种考虑概率因素的随机抽样谱聚类算法.在具体实施时,为了减少背景噪声对分割结果的影响,对图像进行了滤波预处理.结果表明,算法稳定性好,相对现有算法,分割效果得到改善.     

手部软组织红外热像图处理与K-L变换分析

首先对手部软组织的红外热像图进行图像处理,包括灰度化、降噪、增强、分割,其中图像分割采用Otsu算法选择适当的阈值,从而将目标图像与背景分割.然后将处理后图像的像素值代入K-L变换,得到样本的特征空间,将所有样本都投影到该特征空间得到投影系数.最后利用欧式距离公式,最终判断手部软组织的病变情况.     

电力巡检中绝缘子缺陷问题研究

针对电力巡检中由于无人机航拍绝缘子图像尺寸大,导致小目标检测难的问题,提出一种结合滑动窗口和主流神经网络的改进算法,对大尺寸图像中绝缘子串珠的分割以及缺陷区域的定位进行研究.在绝缘子串珠分割问题上,采用滑动窗口结合Mask R-CNN神经网络算法进行图像分割;在绝缘子缺陷检测问题上,结合滑动窗口和非极大值抑制算法对Yolo-v4神经网络进行改进,提高绝缘子缺陷区域小目标检测的精度.实验结果表明,分割所得绝缘子串珠掩膜图与标准掩膜图的相似度(Dice系数)为85.68%,与人工标记的绝缘子区域相比达到更高的相似度,绝缘子缺陷定位的平均精度均值(mean AP)为94.88%,与真实数据相比,平均重叠度为86.93%.     

基于三角隶属函数和模糊熵的图像增强方法及其相关应用研究

鉴于图像增强技术在生活应用中的重要性,模糊技术在图像应用中的实用性和广泛性,提出了一种基于三角隶属函数和模糊熵的新的图像增强算法(T-FE增强算法),使用三角函数作为隶属函数,重构参数型对比增强算子,运用模糊熵最大原则选取阈值,计算快速,简单.并且将T-FE算法运用于图像分割,边缘检测.通过实验仿真表明,T-FE算法在进行图像处理时有较好效果.     

基于混合单纯形算法的模糊均值图像分割

针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.     

基于元胞自动机模型的图像分割算法

针对图像处理中的图像分割任务,我们提出了一个基于模糊元胞自动机模型的图像分割算法.将元胞自动机原理中的演化规则换为模糊规则建立模糊元胞自动机模型,使图像中灰度水平介于目标和背景之间的像素得以更好地归类,从而得到较好的图像分割结果.     

基于小波变换的Laws纹理测度在植被分割中的应用

针对从全色航空影像中进行植被区域提取的随机性和复杂性,阐述了一种基于小波的Laws纹理测度进行植被提取的新算法,它的特点是先用小波变换将图像变换到不同的尺度层上,然后再在多尺度层上提取Laws纹理测度,形成植被区域的特征.与传统的植被提取方法比,它用到了不同频率上纹理的Laws信息,从而更准确的刻画了植被区域的纹理特征,试验结果表明:基于小波变换的Laws纹理测度对植被有较好的分割效果.     

Saliency detection based on superpixels clustering and stereo disparity

Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity (SDC) is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art colorbased saliency detection methods.     

Fast color transfer from multiple images

Color transfer between images uses the statistics information of image effectively.We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding.A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image.The user can specify the correspondences of local region using scribes,which more accurately transfers the target color to the source image while smoothly preserving the boundaries,and exhibits more natural output results.Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image.Moreover,our algorithm does not require to choose the same color style and image size between source and target images.We propose the sub-sampling to reduce the computational load.Comparing with other approaches,our algorithm is much better in color blending in the input data.Our approach preserves the other color details in the source image.Various experimental results show that our approach specifies the correspondences of local color region in source and target images.And it expresses the intention of users and generates more actual and natural results of visual effect.     

Affine Invariant Detection: Edge Maps, Anisotropic Diffusion, and Active Contours

In this paper we undertake a systematic investigation of affine invariant object detection and image denoising. Edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. In this case, affine invariant maps are derived as a weighted difference of images at different scales. We then introduce the affine gradient as an affine invariant differential function of lowest possible order with qualitative behavior similar to the Euclidean gradient magnitude. These edge detectors are the basis for the extension of the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. The active contours are obtained as a gradient flow in a conformally Euclidean space defined by the image on which the object is to be detected. That is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance, the weight being given by functions of the affine edge detectors. The gradient path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology. Based on the same theory of affine invariant gradient flows we show that the affine geometric heat flow is minimizing, in an affine invariant form, the area enclosed by the curve.     

A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others,these results show that our proposed model and algorithms are effective.     

Weighted fusion graphs: Merging properties and watersheds

This paper deals with the mathematical properties of watersheds in weighted graphs linked to region merging methods, as used in image analysis.In a graph, a cleft (or a binary watershed) is a set of vertices that cannot be reduced, by point removal, without changing the number of regions (connected components) of its complement. To obtain a watershed adapted to morphological region merging, it has been shown that one has to use the topological thinnings introduced by M. Couprie and G. Bertrand. Unfortunately, topological thinnings do not always produce thin clefts.Therefore, we introduce a new transformation on vertex weighted graphs, called C-watershed, that always produces a cleft. We present the class of perfect fusion graphs, for which any two neighboring regions can be merged, while preserving all other regions, by removing from the cleft the points adjacent to both. An important theorem of this paper states that, on these graphs, the C-watersheds are topological thinnings and the corresponding divides are thin clefts. We propose a linear-time immersion-like algorithm to compute C-watersheds on perfect fusion graphs, whereas, in general, a linear-time topological thinning algorithm does not exist. Furthermore, we prove that this algorithm is monotone in the sense that the vertices are processed in increasing order of weight. Finally, we derive some characterizations of perfect fusion graphs based on the thinness properties of both C-watersheds and topological watersheds.     

Image encryption using chaotic coupled map lattices with time-varying delays

In this paper, a novel image encryption scheme using coupled map lattices (CML) with time delay is proposed. By employing discretized tent map to shuffle the positions of image pixels and then using delayed coupled map lattices (DCML) to confuse the relationship between the plain-image and the cipher-image, image encryption algorithms with permutation-diffusion structure are introduced in detail. In the process of generating keystream, the time-varying delay is also embedded in our proposed scheme to enhance the security. Theoretical analysis and computer experiments confirm that the new algorithm possesses high security for practical image encryption.     

Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach

The problem of finding a rigid body transformation, which aligns a set of data points with a given surface, using a robust M-estimation technique is considered. A refined iterative closest point (ICP) algorithm is described where a minimization problem of point-to-plane distances with a proposed constraint is solved in each iteration to find an updating transformation. The constraint is derived from a sum of weighted squared point-to-point distances and forms a natural trust region, which ensures convergence. Only a minor number of additional computations are required to use it. Two alternative trust regions are introduced and analyzed. Finally, numerical results for some test problems are presented. It is obvious from these results that there is a significant advantage, with respect to convergence rate of accuracy, to use the proposed trust region approach in comparison with using point-to-point distance minimization as well as using point-to-plane distance minimization and a Newton- type update without any step size control.     

A segmentation procedure of the pervious area of the aorta artery from CT images without contrast medium

In this paper, we develop an algorithm for the segmentation of the pervious lumen of the aorta artery in computed tomography (CT) images without contrast medium, a challenging task due to the closeness gray levels of the different zones to segment. The novel approach of the proposed procedure mainly resides in enhancing the resolution of the image by the application of the algorithm deduced from the mathematical theory of sampling Kantorovich operators. After the application of suitable digital image processing techniques, the pervious zone of the artery can be distinguished from the occluded one. Numerical tests have been performed using 233 CT images, and suitable numerical errors have been computed and introduced ex novo to evaluate the performance of the proposed method. The above procedure is completely automatic in all its parts after the initial region of interest (ROI) selection. The main advantages of this approach relies in the potential possibility of performing diagnosis concerning vascular pathologies even for patients with severe kidney diseases or allergic problems, for which CT images with contrast medium cannot be achieved.     

Region merging with topological control

This paper presents a region merging process controlled by topological features on regions in three-dimensional (3D) images. Betti numbers, a well-known topological invariant, are used as criteria. Classical and incremental algorithms to compute the Betti numbers using information represented by the topological map of an image are provided. The region merging algorithm, which merges any number of connected components of regions together, is explained. A topological control of the merging process is implemented using Betti numbers to control the topology of an evolving 3D image partition. The interest in incremental approaches of the computation of Betti numbers is established by providing a processing time comparison. A visual example showing the result of the algorithm and the impact of topological control is also given.     

Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO)

Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. One of the most important applications of segmentation is for facial surgical planning. Thresholding method is so common in image segmentation, because it is simple, noise robustness and accurate. In this paper, we recognize and segment the area of lips using optimal thresholding based on bacterial foraging optimization. New color space (IHLS) is introduced in this paper, that it has good performance in facial image segmentation. In order to evaluate the performance of the proposed algorithm, we use three methods to measure accuracy. The proposed algorithm has less computational complexity and error and it is also efficient.