基于量子遗传算法的水下图像增强

随着海洋工程以及海洋科学研究的发展,水下图像增强越来越受到大家的关注。针对水下图像噪声大、对比度低、且边缘和纹理模糊等特点,采用了一种量子遗传算法,在小波域对图像进行非线性增强。实验表明,该算法能较好的实现水下图像增强。     

基于WebGL的医学图像三维可视化研究

现今医学图像往往与大型的硬件设备和复杂的软件联系在一起, 然而随着互联网技术的发展, 越来越多互联网应用的出现改变了人们对传统本地软件的依赖, 现今医学图像在互联网领域才刚刚起步, 提出了一种在浏览器中实现医学图像的三维可视化的方法, 能够通过成熟的本地医学图像平台(比如3DSlicer)获取医学图像数据, 结合HTML5以及WebGL(Web Graphics Library)来实现医学图像的三维可视化.     

A training-free nose tip detection method from face range images

Nose tip detection in range images is a specific facial feature detection problem that is highly important for 3D face recognition. In this paper, we propose a nose tip detection method that has the following three characteristics. First, it does not require training and does not rely on any particular model. Second, it can deal with both frontal and non-frontal poses. Finally, it is quite fast, requiring only seconds to process an image of 100-200 pixels (in both x and y dimensions) with a MATLAB implementation. A complexity analysis shows that most of the computations involved in the proposed algorithm are simple. Thus, if implemented in hardware (such as a GPU implementation), the proposed method should be able to work in real time. We tested the proposed method extensively on synthetic image data rendered by a 3D head model and real data using FRGC v2.0 data set. Experimental results show that the proposed method is robust to many scenarios that are encountered in common face recognition applications (e.g., surveillance). A high detection rate of 99.43% was obtained on FRGC v2.0 data set. Furthermore, the proposed method can be used to coarsely estimate the roll, yaw, and pitch angles of the face pose.     

A framework with modified fast FCM for brain MR images segmentation

Intensity inhomogeneity, noise and partial volume (PV) effect render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the effects listed above. In this paper, a framework with modified fast fuzzy c-means for brain MR images segmentation is proposed to take all these effects into account simultaneously and improve the accuracy of image segmentations. Firstly, we propose a new automated method to determine the initial values of the centroids. Secondly, an adaptive method to incorporate the local spatial continuity is proposed to overcome the noise effectively and prevent the edge from blurring. The intensity inhomogeneity is estimated by a linear combination of a set of basis functions. Meanwhile, a regularization term is added to reduce the iteration steps and accelerate the algorithm. The weights of the regularization terms are all automatically computed to avoid the manually tuned parameter. Synthetic and real MR images are used to test the proposed framework. Improved performance of the proposed algorithm is observed where the intensity inhomogeneity, noise and PV effect are commonly encountered. The experimental results show that the proposed method has stronger anti-noise property and higher segmentation precision than other reported FCM-based techniques.     

Image representation using accurate orthogonal Gegenbauer moments

Image representation by using polynomial moments is an interesting theme. In this paper, image representation by using orthogonal Gegenbauer function is presented. A novel method for accurate and fast computation of orthogonal Gegenbauer moments is proposed. The accurate values of Gegenbauer moments are obtained by mathematically integrating Gegenbauer polynomials multiplied by their weight functions over the digital image pixels. A novel recurrence formula is derived for the kernel generation. The proposed method removes the numerical approximation errors involved in conventional method. A fast algorithm is proposed to accelerate the moment’s computations. A comparison with the conventional method is performed. The obtained results explain the efficiency and the superiority of the proposed method.     

Development of an image-based model for capillary vasculature of retina

The paper presents a method of development of a detailed network model to represent retinal capillary vasculature. The capillary model is a circular mesh consisting of concentric rings with an increasing diameter. Each of the rings has uniformly distributed bifurcation nodes to represent capillary vessels. The model is customized using the data that has been measured from confocal microscopic images of a mouse retina. The capillary model developed can be connected to networks of larger vessels of the vasculature such as arterial and venous networks to form a complete model of the retinal network. A method to automate such interface connections between capillary and other vascular networks using connecting vessels (i.e., pre-capillary and post-capillary) is also presented in the paper. Such a detailed image-based capillary model together with the arterial and venular networks can be used for various circulation simulations to obtain accurate information on hemodynamic quantities such as the spatial distribution of pressure and flow in the vasculature for both physiological and pathological conditions. The method presented for the development of the capillary model can also be adopted for vasculatures of other organs.     

一种基于InSb磁敏电阻器的接近开关

介绍了锑化铟(InSb)磁阻式接近开关的工作原理,设计了信号处理电路,并对其动态性能进行了测试.实验结果表明:径向间距为4 mm时,接近开关动作距离大于8 mm,回差距离小于0.06 mm,重复定位精度优于0.04mm,可适用于工作频率0～5 kHz范围内的铁磁性物质检测.该接近开关灵敏度高,开关动态特性较好,且结构简...     

一种新的指纹图像合成分割法

在研究和分析传统方差法和最大类间方差法的基础上,提出了一种传统方差法和最大类间方差法相结合的分块处理分割算法。该算法首先求出整个指纹图像的方差,然后计算每一子块的方差。若方差小于整个图像的方差则用方差法对图像进行分割,否则用最大类间方差法进行分割。最后再对图像进行平滑处理。实验结果表明,相对于传统分割方法,该方法无需根据经验选取阈值,能较准确地分割出前景和背景,抗噪能力强,对高对比度和低对比度的图像均具有很好的分割效果。     

基于特征点的多光谱遥感图像配准

提出一种基于特征点的多光谱遥感图像配准算法。首先在图像上建立二级规则网格,根据信息嫡值及特征分布均匀性准则选取特征网格;然后利用Forstner算子在特征网格中提取特征点,针对多光谱图像的特点,利用基于相关性原理的粗匹配和改进的基于空间距离约束的精匹配确立特征点的对应关系;最后通过仿射变换得到配准后的图像,并用均方根误差评价配准效果。实验结果表明,该方法计算速度快,且能够达到亚像素级配准精度。     

边界重叠图象的一种快速拼接算法

针对边界部分有重叠的图象,提出了一种基于网络的快速对准算法,并通过平滑因子对图象实现了无缝拼接。