改进的遥感图像多种信息熵阈值分割算法

给出了一种新的图像信息量的表述方法,提出了五种基于信息熵的科学分割遥感图像的改进方法。用MATLAB对这些算法进行了实验仿真,并进行了算法性能优化。得出了各种不同算法的最佳阈值和耗时,并对它们进行了比较和分析。实验数据表明,新算法较以往的算法在分割效果和速度上都有明显的改善和提高。     

基于蚁群算法的二维最大熵分割算法

由于二维最大熵分割法不仅考虑了像素的灰度信息,而且还充分利用了像素的空间邻域信息,因此能够取得较好的分割效果.但是,该方法的计算量巨大,不利于红外图像的快速处理.蚁群算法于20世纪90年代初提出,是受到蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于随机搜索算法.该算法已经成功应用于旅行商等离散问题.将蚁群算法应用于二维最大熵法,提出了基于蚁群算法的二维最大熵分割算法.与传统的穷尽搜索法相比,求解速度提高了60倍左右.仿真实验表明,该方法快速、简单、有效.     

基于二维直方图与FCM相结合的图像快速分割方法

基于二维直方图的模糊聚类分割方法,可以有效地抑制噪声。但是FCM(模糊C均值)算法用于图像聚类时最大的缺点是运算开销太大,进而限制了该算法在图像分割中的应用。通过构造合理的二维直方图,并筛选出符合规定条件的元素作为聚类样本,再结合FCM算法进行图像分割。实验结果表明该方法具有与基于一维直方图的模糊聚类分割方法速度相近,但却比其分割精度高很多的良好特点。     

一种快速有效的交互式医学序列图像分割方法

提出了一种结合Live-Wire算法、种子填充以及轮廓插值的序列医学图像分割算法。该方法首先将病灶组织形状变化较大的断层图像作为关键层,然后综合应用Live-Wire算法和种子填充对关键层中病灶区域进行分割,最后利用轮廓插值技术对中间的过渡图像进行自动分割。实验表明这种算法能快速有效地从序列医学图像中分割出感兴趣的物体。     

二维Fisher准则函数在区域生长算法中的应用

本文针对传统区域生长算法中“种子”和“相似性准则”不能自适应获得的缺点,将二维Fisher准则函数算法[1]应用到区域生长算法中,根据图像特征,利用二维阈值,自动确定“种子”和“相似性准则”。实验表明,该算法不仅利用了二维Fisher准则函数算法的优点,而且也使区域生长算法具有自适应性,对荧光磁粉图像具有较好的分割效果。     

Unsupervised Bayesian image segmentation using orthogonal series

This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.     

用无需选取参数的Unit-linking PCNN进行自动图像分割

脉冲耦合神经网络（PCNN-Pulse Coupled Neural Network）是一种有生物学依据的人工神经网络，它可有效地用于图像分割。基于PCNN的图像分割效果取决于PCNN中各参数的选择。然而，图像分割时，各种不同的图像对应的PCNN参数是不同的，而PCNN参数的选择是困难的。本文提出了一种基于Unit-linking PCNN的图像分割新方法，解决了PCNN图像分割参数选择的难题。用本文提出的新方法可有效地自动分割各种图像，而无需考虑PCNN参数的选择，这对于PCNN的理论研究和实际应用有重要的意义。     

Satellite imagery based adaptive background models and shadow suppression

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be “hand-tuned” based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling.     

基于CAMSHIFT算法和形态学的人手动态分割

动态图像分割是计算机视觉分析中一个重要和困难的任务。现讨论了一种基于CAM-SHIFT算法和形态学的动态跟踪分割。首先把采集到的图像由RGB色彩空间转换到HSV色彩空间,因为肤色对HSV色彩空间的H色调具有恒常性。其后在HSV颜色空间建立肤色直方图模型,确定肤色概率度,运用CAMSHIFT算法对人手动态跟踪。然后运用形态学重构闭运算去除空洞和噪声。实验证明该方法能够有效的实现人手的动态分割。     

An Approach for Segmentation of Airborne Laser Point Clouds Utilizing Scan‐Line Characteristics

In this study, we suggest a new segmentation algorithm for processing airborne laser point cloud data which is more memory efficient and faster than previous approaches. The main principle is the reading of data points along a scan line and their direct classification into homogeneous groups as a single process. The results of our experiments demonstrate that the algorithm runs faster and is more memory efficient than previous approaches. Moreover, the segmentation accuracy is generally acceptable.