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81.
为了满足月面巡视探测器的自主导航要求。提出一种新的基于先验知识的特征点提取算法。首先,根据先验知识对原始图像进行预分割成危险区域和安全区域,然后在危险区域里面进行特征点的选取工作,对特征点用于以后的匹配和目标定位,进而用于视觉系统的导航工作。与传统算法相比,提取方法可以有效减少计算量,使选取特征点更加准确,提高后续匹配阶段的处理速度和匹配精度。在模拟试验场的双目视觉照片中,通过实验取得较好的效果。 相似文献
82.
83.
基于区域GAC模型的二值化水平集图像分割算法 总被引:2,自引:1,他引:1
针对测地线主动轮廓(GAC)模型进行了改进,提出了一种基于区域的GAC模型.通过构造基于区域统计信息的符号压力函数取代边界停止函数,有效解决了弱边界目标或离散状边界目标的分割问题.该模型采用二值化水平集方法实现,避免了传统实现方法水平集函数需要重新初始化为符号距离函数,从而导致稳定性差、计算量大、实现复杂等缺点.对不同类型图像的试验结果表明:该算法迭代收敛速度比GAC模型传统实现方法明显加快,且可有效防止边界泄漏,分割效果优于传统GAC模型与C-V模型. 相似文献
84.
85.
在分析了中国书画印章图像特点的基础上,针对基于内容的书画作品图像检索领域中存在的"语义鸿沟"问题,提出了一种自动提取中国书画作品中印章图像的方法,并设计实现了该算法.通过对提取效果进行分析,证明该方法有较高提取率,这对书画作品中图像语义和特定图像鉴别的研究有重要意义. 相似文献
86.
基于改进的最大类间方差算法的图像分割研究 总被引:2,自引:0,他引:2
每种图像分割方法都只利用了图像信息中的部分特征,必然带有局限性,因此只能针对各种实际应用领域的需求来适当选择所需的方法.比较了几种阈值分割和边缘检测算法,着重研究了最大类阃方差算法,并对其进行改进.针对不同的图像进行了仿真,对实验结果进行了分析、研究、比较.结果表明,改进的Otsu算法能有效地提高图像分割的质量. 相似文献
87.
88.
Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets. 相似文献
89.
In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the
outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure
for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications.
In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data.
Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure
deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed
objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure
in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian
Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs
consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity
of a shape.
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献
Francesco de PasqualeEmail: |
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献
90.
Ilias Maglogiannis Demosthenes Vouyioukas Chris Aggelopoulos 《Personal and Ubiquitous Computing》2009,13(1):95-101
This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact
that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is
consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation
and skin detection. A set of several colored images with human faces have been considered as the training set. A second module
is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye
and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring
the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals
for further research. 相似文献