Consistent depth maps estimation from binocular stereo video sequence

In the paper, an approach is proposed for the problem of consistency in depth maps estimation from binocular stereo video sequence. The consistent method includes temporal consistency and spatial consistency to eliminate the flickering artifacts and smooth inaccuracy in depth recovery. So the improved global stereo matching based on graph cut and energy optimization is implemented. In temporal domain, the penalty function with coherence factor is introduced for temporal consistency, and the factor is determined by Lucas-Kanade optical flow weighted histogram similarity constraint (LKWHSC). In spatial domain, the joint bilateral truncated absolute difference (JBTAD) is proposed for segmentation smoothing. The method can smooth naturally and uniformly in low-gradient region and avoid over-smoothing as well as keep edge sharpness in high-gradient discontinuities to realize spatial consistency. The experimental results show that the algorithm can obtain better spatial and temporal consistent depth maps compared with the existing algorithms.     

Automatic outer surface extraction of femoral head in CT images

Computer-aided hip surgery planning and implant design applications require accurate segmentation of femoral head and proximal acetabulum. An accurate outer surface extraction of femoral head using marching cubes algorithm remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we present an automatic and fast approach for segmentation of femoral head and proximal acetabulum which leads to accurate and compact representation of femoral head using marching cubes algorithm. At first, valley-emphasized images are constructed from original images so that valleys stand out in high relief. Otsu’s multiple thresholding technique is applied to seperate the images into bone and non-bone classes. Region growing method and threedimensional (3D) morphological operations are performed to fill holes in the bone. In the reclassification process, the bone regions are further segmented, and the boundaries of the bone regions are further refined based on Bayes decision rule. Finally, marching cubes algorithm is applied to reconstruct a 3D model and extract the outer surface of femoral head and proximal acetabulum. Experimental results show that this method is an accurate segmentation technique for femoral head and proximal acetabulum and it can be applied as a tool in medical practice.     

Modified gray level difference-based thresholding segmentation and its application in X-Ray welding image

Thresholding is a popular image segmentation method that often requires as a preliminary and indispensable stage in the computer aided image process, particularly in the analysis of X-ray welding images. In this paper, a modified gray level difference-based transition region extraction and thresholding algorithm is presented for segmentation of the images that have been corrupted by intensity inhomogeneities or noise. Classical gray level difference algorithm is improved by selective output of the result of the maximum or the minimum of the gray level with the pixels in the surrounding, and multi-structuring of neighborhood window is used to represent the essence of transition region. The proposed algorithm could robustly measure the gray level changes, and accurately extract transition region of an image. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm.     

重大件运输的货物分配与航速联合优化

为了研究半潜船运输过程中满载与空载燃油消耗不同对重大件货物运输和最优航速的影响,建立了货物分配与航速联合优化模型,并改进了递归平滑算法RSA(recursive smoothing algorithm),基于插入法和改进的递归平滑算法IRSA(improved recursive smoothing algorithm),提出了全局搜索算法求解该模型.用插入法解决货物分配问题,用IRSA求解满载和空载的最优航速.算法应用结果表明:与RSA相比,运用全局搜索算法使运输成本降低了240.3万元;与划分为6和9个时间段的离散到达时间方法相比,运用全局搜索算法计算时间分别缩短了71.1和767.3 s.      

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脉冲耦合神经网络（PCNN）具有良好的图像分割特性,但神经网络参数的选取对分割效果有较大影响,如何自适应地选择网络参数是脉冲耦合神经网络应用研究的重要内容.本文首次从脉冲耦合神经网络的耦合特性出发,结合神经计算原理及图像局部区域的灰度特性,提出了脉冲耦合神经网络耦合参数的优化算法.首先利用Hebb学习规则对脉冲耦合神经网络模型的链接权值矩阵进行更新,然后利用图像局部区域的均方差自适应确定神经元链接强度系数,最后将优化的PCNN模型应用于运动车辆图像分割.通过耦合参数的优化,增强了神经元之间的耦合强度,与传统PCNN的车辆分割结果相比,较好地避免了过分割和欠分割现象,提高了运动车辆图像中车牌区域的分割质量,为后续车辆特征的提取奠定了良好的基础.     

基于多层前馈神经网络的车牌提取方法

文中提出一种采用三层前馈神经网络提取车牌的方法。该方法是一种基于纹理分析的图像分割算法,适合于彩色及灰度图像。实验表明,该方法能准确地提取图像中的车牌。     

Unsupervised color-texture image segmentation

The measure J in J value segmentation （JSEG） fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.     

MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

Imagesegmentationplaysanimportantrolein imageanalysisaswellasinhigh levelimageinter pretationandunderstandingsuchasrobotvision, objectrecognition,andmedicalimaging.Numerous segmentationmethodshavebeendeveloped.How ever,medicalimagesareoftencorruptedbyno…     

Hierarchical Non-linear Image Registration Integrating Deformable Segmentation

IntroductionSegmentation and registration are two impor-tant problems in the field of medical image analy-sis. Traditionally, solutions have been developedfor each of these two problems in relative isolationfrom the other, but with increasing dependence onthe existence of a solution for the other. For exam-ple, model-based segmentation methods[1,2]need toregister individual data sets to a common frame ofreference, so that statistics about the models canbe gathered to guide the evolution of the…     

基于头脑风暴优化的PCNN路面裂缝分割算法

为提升裂缝检测的分割精度和鲁棒性,基于头脑风暴优化（brainstorming optimization,BSO）和脉冲耦合神经网络（pulse coupled neural network,PCNN）,提出了一种路面裂缝图像分割算法（BSO-PCNN）. 该算法采用最大熵准则作为BSO算法的适应度函数,并依据适应度值决定参与次轮迭代的个体;BSO具有强收敛性,可快速确定最优个体解;结合图像特征,获得PCNN模型的最优参数,将其代入PCNN模型实现对裂缝图像的分割. 试验结果表明:算法可在20次迭代内取得不同类型路面裂缝图像的最大适应值,从而确定最佳分割参数;与Sobel边缘检测算法、PCNN图像分割算法、基于最大熵的遗传算法（genetic algorithm based on the maximun entropy of the histogram,GA-KSW）、基于遗传算法参数优化的PCNN分割算法（genetic algorithm based on the pulse coupled neural network,GA-PCNN）相比,BSO-PCNN算法取得了0.9924的区域一致性与0.0900的区域对比度.      

Image series segmentation and improved MC algorithm

A semiautomatic segmentation method based on active contour is proposed for computed tomography （CT） image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial contour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes （MC） algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by ,experimental results.     

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

Introduction　　 Automating medical image registration is be-coming an increasingly important goal. It formsthe basis of image processing techniques to com-bine information from the different modalities,which are now frequently acquired for many pa-tients. Such combined representations of patientimages have been shown to assistin the interpreta-tion of the complementary information provided bythe modalities[1,2 ] .　　 A variety of imaging modalities gives insightto different parameters and in…     

基于多层B样条插值的干涉合成孔径声纳配准偏移量插值算法

针对干涉合成孔径声纳对配准精度要求较高而局部配准偏移量相差比较大的问题,提出采用多层B样条曲面插值实现配准偏移量的插值,既保证配准偏移量的插值精度,又能保证拟合曲面的光滑性。采用仿真数据和实际干涉合成孔径声纳数据验证了算法的性能,结果表明,提出的插值算法能有效提高插值精度和图像的相干系数,且能达到实时处理要求。     

融合Eye Map信息的疲劳检测方法的研究

为降低人眼检测过程的计算量,提出一种改进的融合EyeM ap信息的快速过滤算法。利用基于肤色信息的图像分割,得到感兴趣的人脸区域,缩小了搜索范围,减小了检测的错误接受率;然后使用Haar-Like特征进行级联AdaBoost人眼定位检测,得到潜在的人眼区域,使用EyeMap的快速算法获得准确地人眼区域,再进行二值化处理,计算眼睛的轴向外接矩形以估计出眼睑的闭合程度,结合PERCLOS评判标准进行疲劳状态分析。实验证明：改进算法能快速实时有效地识别驾驶员疲劳时的眼部状态。     

基于Kittler最小误差分割算法的小视场星图分割

小视场星图易受光照不均和噪声影响，常用阈值分割算法存在处理效果不佳或效率较低的不足. 针对星图灰度的高斯分布特征，基于贝叶斯最小误差理论，提出利用Kittler最小误差分割算法处理小视场星图. 以视频测量机器人为测量平台，以 "优度法"、区域一致性、区域对比度和时间复杂度为评价指标，对比了常用的阈值分割算法和一维最大熵法，验证了Kittler算法在确保星图良好分割的同时，星图处理效率可以提高70%左右. 基于半仿真星图和真实星图的室内试验表明，Kittler算法可以准确提取星点质心坐标，水平和垂直方向均方根误差分别为0.025像素和0.019像素；采用该算法的野外天文定位实测表明，经纬度内符合平均精度分别优于0.015 s和0.22″，外符合精度分别优于0.025 s和0.35″，可以满足一等天文测量的精度要求.      

Lesion segmentation and identification of breast tumor on dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI） can show subtle lesion morphol- ogy, improve the display of lesion definitions, and objectively reflect the blood supply of breast tumors; it can also reflect different strengthening patterns of normal tissues and lesion areas after medical tracer injection. DCE-MRI has become an important basis for the clinical diagnosis of breast cancer. To DCE-MRI data acquired from several hospitals across multiple provinces, a series of in-silico computational methods were applied for lesion segmentation and identification of breast tumor in this paper. The image segmentation methods include Otsu segmentation of subtraction images, signal-interference-ratio segmentation method and an improved variational level set method, each has its own application scope. After that, the distribution of benign and malignant in lesion region is iden- tified based on three-time-point theory. From the experiment, the analysis of DCE-MRI data of breast tumor can show the distribution of benign and malignant in lesion region, provide a great help for clinicians to diagnose breast cancer more expediently and lay a basis for medical diagnosis and treatment planning.     

遗传算法在图像分割中的应用

图像分割是图像分析的预处理阶段,被认为是计算机视觉中的一个瓶颈.基于扩展的Otsu最优阈值图像分割方法,提出用一种改进遗传算法进行图像分割的方法,并给出了遗传算法中参数的设定.仿真结果表明,改进算法的计算速度不仅明显优于传统的Otsu方法,而且算法的分割效果也很好.     

Automatic Airway Deletion in Pulmonary Segmentation

A method of removing the airway from pulmonary segmentation image was proposed. This method firstly segments the image into several separate regions based on the optimum threshold and morphological operator,and then each region is labeled and noted with its mean grayscale. Therefore, most of the non-lung regions can be removed according to the tissue‘s Hounsfield units (HU) and the imaging modality. Fifially, the airway region is recognized and deleted automatically through using the priori information of its HU and size. This proposed method is tested using several clinical images, yielding satisfying results.     

车牌自动识别系统设计与实现

车牌自动识别分为图像预处理、车牌定位、字符分割、字符识别四步。利用形态学变换对图像进行滤波聚类,HOUGH变换对车牌图像进行水平校正,BP神经网络的方法进行字符识别,最后基于DELPHI7．0环境下设计开发了车牌自动识别系统。     

结合区域分割和Wishart分类器的机场跑道区域快速检测方法

提出了一种结合区域分割和Wishart分类器的极化合成孔径雷达图像机场跑道区域快速检测方法; 利用简单线性迭代聚类算法分割极化合成孔径雷达图像, 并将分割得到的超像素作为后续分类处理的基本单元; 采用一种优化后的距离度量方式给超像素分配类别标签, 解决了传统Wishart距离度量因子冗余运算量大的问题; 分析了机场跑道区域像素的极化散射特性, 利用机场跑道区域的弱散射特性从分类结果中提取感兴趣区域; 利用机场跑道的结构特征筛选辨识感兴趣区域, 进而确定机场跑道区域的准确位置; 利用极化合成孔径雷达实测数据测试了算法的有效性, 并与传统基于像素的检测结果进行对比。试验结果表明: 该算法在复杂大场景下能够快速有效检测出机场跑道区域, 检测出的跑道轮廓清晰, 结构比较完整; 采用简单线性迭代聚类算法预处理图像极大地降低了后续处理的复杂性; 针对墨西哥湾试验数据, Wishart分类器处理单元个数分别是Freeman+Wishart算法和FCM+Wishart算法的1.0%和2.4%, 整个检测过程耗时分别为Freeman+Wishart算法和FCM+Wishart算法的9.9%和27.1%;针对大岛试验数据, Wishart分类器处理单元个数分别是Freeman+Wishart算法和FCM+Wishart算法的1.0%和2.6%, 整个检测过程耗时分别为Freeman+Wishart算法和FCM+Wishart算法的14.0%和31.8%。可见, 所提检测方法的实时性能优于基于像素的检测方法。