基于先验形状的CV模型肝脏CT图像分割

针对目标被部分遮挡或部分信息丢失情况下CV模型不能正确识别的问题,提出一种新的分割算法。首先,利用数学形态学对原肝脏图像进行滤波,并结合其他算法建立肝脏先验形状;然后,采用边缘查找和区域标定等算法,对肝脏先验性状的边缘以及边缘内外区域进行赋值,构建执行效率高的符号函数距离函数,将其通过形状比较函数嵌入到CV模型的能量泛函中,形成新的基于先验形状的CV模型,并将此模型用于分割存在干扰或者被部分遮挡的肝脏CT图像。与CV模型分割结果相比,本文算法能在目标周围存在干扰信息或者被部分遮挡的情形下,成功地正确识别出目标区域。     

一种基于凸集映射的高分辨率图像恢复算法

介绍了一种基于凸集映射(POCS)算法的高分辨率图像恢复方法，通过仿真对比了其与双线性插值方法恢复高分辨率图像的差异，仿真结果表明，该方法有助于改善高分辨率图像的恢复质量。     

从明暗恢复形状问题中的一种分形约束方法探讨

本文提出一种由从明暗恢复自然景物表面三维形状的一种表面约束方法.该方法从传统约束条件的分形本质出发,克服了传统表面恢复方法造成恢复结果过分平滑而失真的缺点,并利用最大后验概率方法对物体表面高度函数进行Gibbs随机采样,从而得到待恢复表面三维形状的一个最佳实现.实验结果表明了本文方法的有效性.     

基于形状先验的建筑物几何参数提取方法

基于单幅高分辨率遥感图像的建筑物几何参数提取结果的准确性,容易受图像背景、图像噪声以及灰度分布相似性的干扰,形成错误的提取结果。针对这一问题,该文提出一种新的基于形状先验的变分水平集提取方法,该方法同时使用图像边缘信息、区域灰度信息以及包含屋顶和侧立面的形状先验信息,实现单幅遥感图像中建筑物的几何参数的提取。实验结果表明,该方法能够更加准确地提取建筑物,最后得到的几何参数比较接近真值,并且由于更加充分地使用了全局形状信息该方法能更好地抵制侧立面的干扰,具有很强的鲁棒性。     

融合颜色与形状特征的图像检索方法

基于颜色或颜色-空间信息的图像检索方法，由于没有考虑图像中所含目标对象的形状特征，检索效果往往不够理想，针对这一不足。文章提出并设计了颜色-梯度方向角二维直方图，将图像的颜色特征与形状特征融合起来进行图像检索。试验结果表明，该方法的检索精度与效率都有明显的提高。     

基于Level Set方法的图像分割

研究了基于水平集的图像分割,提出了一种无需重新初始化,基于边缘信息的变分水平集图像分割算法.该算法消除了影响水平集计算量的重新初始化步骤,加速了轮廓线的演化,提高了算法的鲁棒性,同时使得初始化方法更加灵活.     

基于形状约束和局部演化的二值水平集运动目标分割

针对水平集分割模型运算效率较低且易出现过分割的现象,结合视频中运动目标分割的应用背景,该文提出一种将运动目标检测作为先验形状约束和曲线局部演化方法相结合的二值水平集分割模型。该模型提出将运动目标检测的区域作为先验形状信息对水平集分割进行约束,并使用二值函数替换传统水平集函数提高运算效率,同时融入曲线的局部演化方法解决二值水平集模型缺乏曲线演化渐进性的问题。实验结果表明,该文方法在分割准确性、鲁棒性和运算效率等方面与相关模型相比均有不同程度的提高。     

基于改进先验形状CV模型的目标分割

由于空间目标姿态变化较大,且其灰度与地球背景差异较小,传统CV（Chan and Vese）模型难以获得理想的分割结果。针对目标被部分遮挡或部分信息丢失情况下CV模型不能正确识别问题,Chan和Zhu在CV模型基础上引入先验能量项,构建的先验形状模型只具有旋转、缩放和平移不变性。本文提出了一种先验形状约束的变分水平集改进模型,用于分割星空及复杂地球背景下的空间目标。在保持先验形状模型具有旋转、缩放和平移不变性的基础上,本文改进的变分水平集模型增加了X、Y方向拉伸以及剪切不变约束能量项,增强了先验形状对目标变化的自适应性。实验结果表明本文方法对复杂背景下姿态变化较大的空间目标,具有更好的分割效果。      

基于显著性区域检测和水平集的图像快速分割算法

为了实现含有复杂背景和弱边界图像的快速准确分割,传统的水平集常采用重新初始化的方法,但是这种方法存在计算量大、分割不准确等问题。因此,结合显著性区域,该文提出一种基于边缘信息与区域局部信息结合的变水平集图像快速分割方法。首先用元胞自动机模型检测出图像的显著性区域,得到图像的初始化边界曲线。然后,采用改进的距离正规化水平集演化(Distance Regularized Level Set Evolution, DRLSE)模型把图像的局部信息结合到变分能量方程中,用改进的能量方程去指导曲线的演化。实验结果表明,与DRLSE模型相比,提出的算法平均消耗的时间只需要前者的2.76%,且具有较高的分割准确性。     

基于进化规划的医学图像恢复方法研究

图像模糊对图像中特定目标的检测以及特征的提取等都会造成影响,采用图像恢复可以改善图像的质量.本文提出了一种基于进化规划的图像恢复方法,该方法把图像的可行解用个体表示,由多个个体构成种群,通过种群的进化而获得质量较优的图像.这种方法可以应用于医学图像的处理.首先分析了图像恢复的原理,然后给出了方法的详细实现步骤和相关的实验结果.     

Reconstruction of MR images from data acquired on a general nonregular grid by pseudoinverse calculation

A minimum-norm least-squares image-reconstruction method for the reconstruction of magnetic resonance images from non-Cartesian sampled data is proposed. The method is based on a general formalism for continuous-to-discrete mapping and pseudoinverse calculation. It does not involve any regridding or interpolation of the data and therefore the methodology differs fundamentally from existing regridding-based methods. Moreover, the method uses a continuous representation of objects in the image domain instead of a discretized representation. Simulations and experiments show the possibilities of the method in both radial and spiral imaging. Simulations revealed that minimum-norm least-squares image reconstruction can result in a drastic decrease of artifacts compared with regridding-based reconstruction. Besides, both in vivo and phantom experiments showed that minimum-norm least-squares image reconstruction leads to contrast improvement and increased signal-to-noise ratio compared with image reconstruction based on regridding. As an appendix, an analytical calculation of the raw data corresponding to the well-known Shepp and Logan software head phantom is presented.     

Digital recovery of biomedical signals from binary images

Given the vast amount of historical clinical data to be incorporated from old hospital information systems into new emerging digital storing standards, digital recovery of paper-written one-dimensional biomedical signals is a relevant application. Signal recovery from noisy, black and white, grid paper printout recordings, is a real situation that has received little attention in the literature. In this paper we propose an integral, automatic approach, based on digital image processing principles, and implemented in four stages: (1) orientation correction of the scanned image, using the eigenvector decomposition of the foreground pixel coordinates, hence reducing the computational cost of subsequent Hough Transform; (2) grid detection, using the Discrete Cosine Transform on horizontal and vertical histogram projections; (3) signal waveform identification, using morphological operators; (4) conversion from the waveform in the image plane to the one-dimensional biomedical signal. Time synchronization between the digitized gold standard and the recovered signals, which is essential for performance evaluation, is addressed by using of contrast filters to extract fiducial points on both signals, which are then fitted to a regression curve. Results with black and white paper printout recordings of intracardiac signals show that proposed approach is capable of automatically recovering biomedical signals from noisy images.     

Phase detector for data-clock recovery circuit

A new type of phase detector which can be used in data-clock recovery circuits is reported. The main feature of the proposed phase detector is that it is free front data dependent jitter, which is the main problem with the Hogge phase detector     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.     

原子力显微镜在液相条件下的成像分析

本研究利用原子力显微镜(atomic force microscope,AFM)分别对Al2O3纳米模板、云母片及数字光盘在固相及液相务件下进行形貌扫描和云母片上的力 - 距离曲线测量.实验结果分析表明液相条件可以降低针尖的展宽效应,使得形貌成像更能够显示出物体的细微结构.在液相条件下,由于样品表面消除了静电效应和液体的阻滞力结果,力 - 距离曲线显示了较好的力均一性.这些结果为AFM实验技术研究生理条件下的生物样品的形态学及生物分子之间的相互作用力提供了实验可行性.     

Focused image recovery from two defocused images recorded withdifferent camera settings

Two new methods are presented for recovering the focused image of an object from only two blurred images recorded with different camera parameter settings. The camera parameters include lens position, focal length, and aperture diameter. First a blur parameter sigma is estimated using one of our proposed depth-from-defocus methods. Then one of the two blurred images is deconvolved to recover the focused image. The first method is based on a spatial domain convolution/deconvolution transform. This method requires only the knowledge of sigma of the camera's point spread function (PSF). It does not require information about the actual form of the camera's PSF. The second method, in contrast to the first, requires full knowledge of the form of the PSF. As part of the second method, we present a calibration procedure for estimating the camera's PSF for different values of the blur parameter sigma. In the second method, the focused image is obtained through deconvolution in the Fourier domain using a Wiener filter. For both methods, the results of experiments on actual defocused images recorded by a CCD camera are given. The first method requires much less computation than the second method. The first method gives satisfactory results for up to medium levels of blur and the second method gives good results for up to relatively high levels of blur.     

A statistical and geometrical edge detector for SAR images

A constant-false-alarm-rate (CFAR) edge detector based on the ratio between pixel values is described. The probability distribution of the image obtained by applying the edge detector is derived. Hence, the decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood. For a better and finer detection, the edge detector operates along the four usual directions over windows of increasing sizes. A test performed, for a given direction, on a radar image of an agricultural scene shows good agreement with the theoretical study. The operator is compared with the CFAR edge detectors suitable for radar images     

A coupled statistical model for face shape recovery from brightness images.

We focus on the problem of developing a coupled statistical model that can be used to recover facial shape from brightness images of faces. We study three alternative representations for facial shape. These are the surface height function, the surface gradient, and a Fourier basis representation. We jointly capture variations in intensity and the surface shape representations using a coupled statistical model. The model is constructed by performing principal components analysis on sets of parameters describing the contents of the intensity images and the facial shape representations. By fitting the coupled model to intensity data, facial shape is implicitly recovered from the shape parameters. Experiments show that the coupled model is able to generate accurate shape from out-of-training-sample intensity images.     

线激光、遮挡轮廓结合的雕塑形体3D扫描方法研究

随着数字化技术的逐步普及,越来越多的形体通过3D扫描设备输入计算机,然后通过一系列操作转化为CAD模型.但由于价格、适用性、易用性等条件影响,现有的3D扫描设备不能满足许多用户的需求.本文集成线激光法扫描发与遮挡轮廓重构法,实现了一套简便、易用的3D光学扫描系统,充分利用不同特性的形体空间信息,生成可靠的数字模型.文中详细介绍了相关原理、系统集成方法,并提出了一种基于分片能量泛函的数据融合方法.最后给出了一个典型扫描实例,验证了本文方法的有效性.     

Skew correction of business card images acquired in PDA

An efficient algorithm for rotational skew correction of business card images acquired in a PDA (personal digital assistant) camera is presented. The proposed method is composed of four parts: block adaptive binarisation (BAB), stripe generation, skew angle calculation and image rotation. In BAB, an input image is binarised block by block so as to lessen the effects of irregular illumination and shadow over the input image. In stripe generation, character string clusters are generated merging adjacent characters and their strings, and then only clusters useful for skew angle calculation are output as stripes. In skew angle calculation, the direction angles of the stripes are calculated using their central moments and then the skew angle of the input image is determined averaging the direction angles. In image rotation, the input image is rotated by the skew angle. Experimental results show that the proposed method yields root mean square error of 0.44/spl deg/ for test images of several types of business cards acquired by a PDA under various surrounding conditions.