分层六角网格中物体边缘检测的算法

本文提出了在分层六角网格中基于像素灰度实现边缘检测的边缘点确定算法和边缘构造算法。文中首先介绍了六角网格结构的优势和1：3面积比、1：4面积比两类六角网格金字塔,而后给出了相应的实验论证。实验结果显示应用六角网格金字塔的算法对图像数据中的缝隙与噪声具有鲁棒性,1：4面积比六角金字塔在边缘检测中优于1：3面积比金字塔。     

A maximum likelihood filter using non-local information for despeckling of ultrasound images

This work presents a new ultrasound image despeckling method based on the maximum likelihood principle that effectively exploits non-local information for estimating noise-free pixels. First, a new maximum likelihood filter is proposed which uses neighborhood information to despeckle images. For this purpose, the general speckle model is used in the log-likelihood function and despeckled pixels are obtained by maximizing this function. Second, the proposed filter is developed to use non-local information such that the distribution of each noisy pixel is weighted according to the statistical distance between the patch of the noisy pixel and that of the target pixel. Because it is optimally designed for ultrasound images, the Pearson distance is used to measure the statistical distance between the patches. A series of experiments are conducted on three different ultrasound images and one synthetic image. Subjective evaluations show that the proposed method is able to preserve edges and structural details of the image and objective evaluations using equivalent number of looks, natural image quality evaluator, peak signal-to-noise ratio, mean preservation, and structural similarity confirm that the proposed method can achieve superior performance.     

An endmember-based distance for content based hyperspectral image retrieval

We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.     

M估计在医学图像配准中的应用

图像的刚性配准或者匹配是图像处理中的热点和难点问题。由于图像的不连续性和成像设置中噪声的影响,传统的最小二乘方法（Least Squares Method）不能很好地解决这一难题。采用M估计算子（M-estimation operator）代替最小二乘算子,并结合由粗到精的多分辨率策略,提出了一种新的基于M估计的医学图像多分辨率配准算法,实验结果表明该方法对配准的速度、精度及鲁棒性都有很大的提高。     

基于边缘特征和Keren算法的图像配准

为获得高精确度、高稳定度的岩心配准图像,提出利用Sobel算子提取图像边缘并结合keren算法的图像配准方法。该方法首先提取参考图像和待配准图像边缘,接着对边缘图像进行高斯金字塔分解,最后,利用keren改进算法实现由粗到细的岩心图像配准。实验结果表明该算法与Keren算法相比,在小角度下都有较高的配准精度,大角度旋转情况下该方法绝对误差明显降低,且在大角度旋转下平移参数和角度参数分别可获得0.1个像素以下和0.1度以下的绝对误差精度,提高了岩心配准精度。     

一种多传感器图像的彩色融合系统

针对像素及多传感器图像的融合问题,设计了一种多传感器图像的彩色融合系统。该系统首先采用Laplacian金字塔变换算法得到灰度融合图像,然后产生共有图像、差异图像和细节增强图像,最后将灰度融合图像、细节增强图像,分别送入RGB通道得到彩色融合图像。实验结果表明,本系统可以有效地对多传感器图像进行彩色融合。根据熵和颜色丰富度两个客观评价指标,本系统的彩色融合图像更清晰,更容易识别目标,包含更多的细节信息,图像信息也更加丰富。     

一种基于多尺度小波分解的图象镶嵌技术

为了获得一种具有艺术视觉效果的镶嵌图象,提出了一种基于多尺度小波分解的图象镶嵌技术,该技术首先对原始图象的各子块区域和图象库中的每一幅图象进行多尺度小波分解;然后逐层计算各图象小波分解系数的标准方差和它们之间的相似距离,并据此从图象库中选取与原始图象中的各子块区域最佳匹配的贴图,再将其镶嵌到原始图象中的对应区域;最后对贴图进行逐像素的颜色校正,使贴图的颜色与原始图象尽可能一致.同时,根据人眼观察某个区域时往往通过取整或将细节取平均来得到一个总体强度效果这一视觉特性,使最终的镶嵌图象具有在近处看到的是各个贴图的内容,而在远处观看则是原始图象的总体轮廓的视觉效果.利用计算机来自动地生成这种镶嵌图象的实验结果表明,该方法是有效的.     

空间频率图像的智能融合

提出了一种智能地融合同一场景的多幅图像为一幅图像的方法,与原图像相比,产生的图像包含较少的噪声和更多的信息。首先对原始图像除去斑点噪声;然后,运用直方图均衡化表示图像的细节和最大化图像信息内容;第三步,运用金字塔将图像分解为子图像,利用图像的密度、空间频率等特征,寻找需要融合的图像的部分;第四步,对这些子图像进行配准,为融合做准备;第五步,对需要融合的子图像,计算每一幅的空间频率,对空间频率不同的配准子图像,将结合它们周围子图像的空间频率信息,给出这个子图像的频率(一般情况下,频率值选择一个具有最高值的像素);第六步,为了得到更多细节,用模糊插值方法扩大这幅子图像。     

基于空间模式聚类最大熵图像分割算法研究

研究图像分割优化问题,在分割图像中,提取信息受到各种因素影响,分割效果不理想。针对图像分割计算复杂,造成图像分割分辨率低,清晰度不高。同时,当图像中的信息量非常大时,图像分割非常耗时。为了有效地分割图像,提出了一种基于空间模式聚类和最大熵算法原理相结合的图像分割方法。首先对图像采用最大熵算法进行图像分割,为每个熵区域定义特征量。根据不同的特征量计算相似区域之间的欧氏距离和空间距离,从而确定像素聚类中心的距离。然后对分割后的图像区域采用基于空间模式聚类方案进行合并,并对图像进行二值化处理。仿真表明与传统图像分割相比,提高了分割效率,分割出的图像边缘效果清晰,证明了算法的可行性和有效性。     

基于正交小波变换的多尺度边缘提取

基于正交二进小波，设计了一个滤波器对图象进行多尺度滤波，得到不同尺度的小波变换。在每种尺度下分别提取图象边缘，而后综合形成图象真正的边缘。该方法不仅能有效地抑制噪声，得到单象素宽、精确的边缘信息，而且能依据边缘的奇异度区分不同的边缘     

同一场景不同曝光图像的配准及HDR图像合成

提出了一种针对同一场景不同曝光的多幅照片图像的配准、恢复光照响应曲线及高动态范围图像合成算法.首先将不同曝光的序列图像中值二值化,利用多尺度思想构造二值图像的金字塔序列并逐级比较,以实现多幅图像间的平移配准和转角配准.从配准后的序列图像中采样,根据最小二乘原理拟合出相机的光照响应曲线,并获得图像中像素值与曝光量之间的映射关系,进而将不同曝光的场景照片图像融合成一幅高动态范围图像.实验结果表明,合成的高动态范围图像效果满意.     

Spatial error concealment for stereoscopic video coding based on pixel matching

In stereoscopic video coding, the interview correlation between the stereo image pair can be used for error concealment. A new spatial error concealment method for stereoscopic video coding based on pixel matching in the decoder is proposed in this paper. The lost macroblocks are recovered by utilizing disparity matching between two-view images on a pixel-by-pixel basis. Firstly, we get the candidate disparity vectors of the four neighboring pixels of the lost pixel by disparity matching in the decoder. Secondly, by calculating the boundary pixel difference, we determine an optimal replacing pixel in the reference image, and then we recover the lost pixel by the optimal pixel in the reference image. Experimental results show that the proposed algorithm performs better comparing to the previous technique.     

足部标记图像中标尺提取与像素测量

测量足部关节受力后的位移大小,进而研究足部关节的运动特性,需要计算足部标记图像中每个标记点的位移。实验采集图像中,黑色标记点和矩形区域分别为探测电极和标尺。实现了足部标记图像中标尺的自动提取与像素测量,根据最小二乘直线拟合思想,提取标尺的边界直线,并求其下方两端点的像素距离,然后利用标尺长度求出每个像素所代表的实际长度;实现了像素距离与标尺长度的自动转换,为足部疾病的临床诊断与治疗提供服务。     

基于模板距离分布的烟支中心点搜索策略

基于采用模板匹配的方法实现烟支识别与计数的思想,研究识别前烟支中心点搜索策略的一种优化方法.首先依据烟支图像的特点,分析烟支模板中心点的距离分布;在此基础上通过在烟支图像中粗粒度标志出可能的烟支边界,考察非边界点到距其最近边界间的距离,将其与模板距离分布相结合,得到烟支图像中像素点的6种距离分布;按照6种距离分布划分各像素点作为烟支中心点的可能性大小及识别优先级;最后按照识别优先级由大至小依次搜索烟支中心点.实验表明,该方法对孤立烟支的中心点定位和连通烟支的中心点定位同样有效,与全局遍历法相比,能提高烟支识别与计数的精确性.     

A Pyramid Approach to Motion Tracking

This paper presents a multiresolution approach to visual motion tracking. In the approach, the foveation mechanism of the human visual system is used to model the multiresolution information perception algorithms of a Transputer-based pyramid visual tracking system. The video images of a moving target are transformed into pyramidal data structures, each of those images consists of multiple image layers with different resolutions by a Gaussian pyramid generation algorithm. The tracking of a moving target over an image sequence is accomplished by performing a foveal search that is based on an iterative intensity pattern correlation along the multiple resolution levels of the Gaussian pyramids of two successive images. Analyses are given as to the efficiency and accuracy of our tracking algorithm, showing that the algorithm is over 160 times faster than conventional mono-resolution tracking methods, with the tracking error within one pixel. To demonstrate the superiority of the multiresolution tracking algorithm in the connection to parallel computation, a scheme for mapping the tracking algorithm into a Transputer-based pyramidal parallel computing structure is proposed in the paper. Experimental results demonstrate good performance of the proposed approach.     

基于图像金字塔的分维融合算法

数据融合是进行目标检测和识别的重要技术之一,但是在融合过程中不可避免地会产生信息损失。如何保留尽可能多的有用信息,关键在于融合算法的选取。由于图像金字塔中不同级的图像之间具有自相似性,而自相似性又是分形分维的基础。于是,根据图像金字塔的特点,提出了一种新的图像融合方法——基于图像金字塔的分维融合算法,将不同源图像分别分解成不同尺度的金字塔图像序列,在相应的尺度上以分形分维作为权系数进行融合,并采用中波红外和长波红外图像作为源图像进行融合实验,实验结果表明该方法是可行的。     

Color Constancy Using the Inter-Reflection from a Reference Nose

This paper introduces a novel camera attachment for measuring the illumination color spatially in the scene. The illumination color is then used to transform color appearance in the image into that under white light.The main idea is that the scene inter-reflection through a reference camera-attached surface Nose can, under some conditions, represent the illumination color directly. The illumination measurement principle relies on the satisfaction of the gray world assumption in a local scene area or the appearance of highlights, from dielectric surfaces. Scene inter-reflections are strongly blurred due to optical dispersion on the nose surface and defocusing of the nose surface image. Blurring smoothes the intense highlights and it thus becomes possible to measure the nose inter-reflection under conditions in which intensity variation in the main image would exceed the sensor dynamic range.We designed a nose surface to reflect a blurred scene version into a small image section, which is interpreted as a spatial illumination image. The nose image is then mapped to the main image for adjusting every pixel color. Experimental results showed that the nose inter-reflection color is a good measure of illumination color when the model assumptions are satisfied. The nose method performance, operating on real images, is presented and compared with the Retinex and the scene-inserted white patch methods.     

局部双目视差回归的目标距离估计

目的 双目视觉是目标距离估计问题的一个很好的解决方案。现有的双目目标距离估计方法存在估计精度较低或数据准备较繁琐的问题,为此需要一个可以兼顾精度和数据准备便利性的双目目标距离估计算法。方法 提出一个基于R-CNN（region convolutional neural network）结构的网络,该网络可以实现同时进行目标检测与目标距离估计。双目图像输入网络后,通过主干网络提取特征,通过双目候选框提取网络以同时得到左右图像中相同目标的包围框,将成对的目标框内的局部特征输入目标视差估计分支以估计目标的距离。为了同时得到左右图像中相同目标的包围框,使用双目候选框提取网络代替原有的候选框提取网络,并提出了双目包围框分支以同时进行双目包围框的回归;为了提升视差估计的精度,借鉴双目视差图估计网络的结构,提出了一个基于组相关和3维卷积的视差估计分支。结果 在KITTI（Karlsruhe Institute of Technology and Toyota Technological Institute）数据集上进行验证实验,与同类算法比较,本文算法平均相对误差值约为3.2%,远小于基于双目视差图估计算法（11.3%）,与基于3维目标检测的算法接近（约为3.9%）。另外,提出的视差估计分支改进对精度有明显的提升效果,平均相对误差值从5.1%下降到3.2%。通过在另外采集并标注的行人监控数据集上进行类似实验,实验结果平均相对误差值约为4.6%,表明本文方法可以有效应用于监控场景。结论 提出的双目目标距离估计网络结合了目标检测与双目视差估计的优势,具有较高的精度。该网络可以有效运用于车载相机及监控场景,并有希望运用于其他安装有双目相机的场景。     

基于多尺度HOG的草图检索

草图检索是图像处理领域中的重要研究内容。提出了一种将高斯金字塔和局部HOG特征融合的特征提取改进方法,并将其用于草图检索。采用高斯金字塔将图像分解到多尺度空间,在所有尺度上进行兴趣点提取,获得基于兴趣点的多尺度HOG特征。利用图像的多尺度HOG特征集生成视觉词典,最终形成与视觉词典相关的特征描述向量,通过相似度匹配实现草图检索。将该算法与单一尺度下的HOG算法及其他几种算法比较,实验结果表明了其可行性和有效性。     

A Wavelet-Based Multiresolution Method to Automatically Register Images

We present a method to automatically register images presenting bothglobal and local deformations. The image registration process isperformed by exploiting a multi-level/multi-image approach wherebyafter having wavelet-transformed the images, the subband images atdifferent levels are used in a non-feature-based way to determine themotion vectors between the reference and the target images. The crudemotion field determined by block matching at the coarsest level ofthe pyramid is successively refined by taking advantage of both theorientation sensitivity of the different subbands and thecontribution of the adjacent levels. The final registered image isobtained by applying the motion field to the lowest level of thepyramid and by inversely transforming it.