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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. 相似文献
136.
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. 相似文献
137.
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. 相似文献
138.
提出了一种新的图像分割算法-三角函数算子,它是一种基于光学干涉原理的快速图像分割方法,并分析了其硬件实现装置。在该算子的运用中需要将输入图像的强度信息转化为相应的相位信息。定义了不同的强度-相位转换方式,例如线性方式、对数函数方式、正切函数方式、反正切函数方式等。通过数值计算,研究了不同转化方式对三角函数算子分割效果的影响。结果显示,不同的转换方式及其参数,都直接影响该算子的分割效果和边缘类型分辨能力。分析表明,在对数S形函数方式下,不但能够检测阶梯状和脉冲状等类型的边缘,还能够检测出屋顶状边缘。 相似文献
139.
曲线的透视不变性分割在基于二次曲线的三维重建中非常重要。首先给出了连续曲线零曲率点和角点的提取法,在数字图像中曲经是不连续的,曲率的变化受噪声的干扰很大。在这种情况下为了检测出零曲率点和角点,对曲线采用了高斯滤波然后再提到零曲率点和角点。实验证明这种方法效果优于直接求曲率法。 相似文献
140.