基于模拟退火算法的递归自动阈值分割方法

针对目标图像灰度对比度差的现象,以及对目标对象检测实时性的要求,并考虑到传统的Otsu分割方法在分割图像质量较差以及目标区域小时准确性差的缺点,提出了一种基于模拟退火算法的递归Otsu分割方法。在图像直方图呈双峰的情况下能够准确地找到分割阈值。在成像模糊、光照度较差的情况下此方法仍然可以获得较高的准确度。该方法在保证了检测质量的同时并没有导致运算时间的大幅度提升,有效地保证了处理的实时性。实际应用表明该方法切实可行。     

光电图像光照不均蜕化的校正及阈值分割方法*

分析了CCD获取的图像光照不均蜕化产生原因.针对光照不均蜕化的印刷电路板图像的具体情况,用同态滤波器在频域空间进行滤波而增强图像,从而改善图像的目标与背景对比度.讨论了最大类间方差法,给出了其优点.为了更好的分割图像,在此基础上,考虑两类间距与各类内聚性对图像分割的不同影响,提出了一种改进的最大类间方差法.最后对光照不均蜕化的PCB光电图像用不同方法进行了实验,结果表明改进方法能更好地分割图像.     

一种基于荧光信号的海面厚油膜评估方法

油膜厚度是海面溢油污染评估分析的一个重要指标,激光诱导荧光(LIF)技术是目前最有效的海面溢油探测技术之一,基于LIF探测技术的油膜厚度反演算法当下仅有适用于薄油膜(≤10~20 μm)的评估方法,而对于较厚油膜(>20 μm)的评估目前尚无有效的反演算法。鉴于此,提出一种基于LIF技术适用于较厚油膜的反演算法,该算法采用油膜荧光信号反演油膜厚度,推导了油膜厚度反演公式,并给出了基于该反演算法的油膜厚度评估方法。首先采用最大类间方差算法(Otsu)选取合适的荧光光谱波段,然后根据选取波段内每个波长的光谱数据反演油膜厚度,最后采用反演油膜厚度的平均值作为油膜厚度评估结果。研究了该算法的适用范围,给出了该算法有效评估范围最大值与测量相对误差的关系,并结合消光系数给出了在多种测量误差条件下不同消光系数油品有效评估范围的最大值。通过实验对本文方法进行了验证,选用原油和白油的混合油(1∶50)作为实验油品,以波长为405 nm的激光作为激发光源,采集波长范围为420～750 nm,采集了海水背景荧光和拉曼散射光光谱、实验油品的荧光特征光谱和多种不同厚度的较厚油膜的荧光光谱。采用Otsu算法选取420～476 nm波段评估油膜厚度,在实验油品油膜厚度≤800 μm时,该算法对油膜厚度的评估具有较高的精度,平均误差为10.5%;在油膜厚度>800 μm时,平均误差为28.8%,评估误差较大且随油膜厚度的增加快速变大,该实验结果与利用测量相对误差和消光系数的分析结果一致。实验结果表明,该方法可以实现对海面较厚油膜厚度的有效评估,并可以根据测量相对误差和消光系数判断评估结果的有效性。     

Research on identifying the order of fringe pattern traces using angular scan and zone search method

A method for automatically identifying the order of fringe pattern traces is presented. It uses the simplified Otsu algorithm for obtaining the threshold, the angular scan in the range of 45~ for searching the trace positions, and the zone search technique for identifying different traces. Experimental results show that the proposed method may reliably obtain the order of fringe pattern traces orientating from almost 45° to 90°.     

Study of improved Otsu algorithm and its ration evaluation analysis for PCB photoelectric image segmentation

The algorithm of maximum variance between clusters (traditional Otsu algorithm) is discussed, and its advantage is given also. In order to segment the PCB photoelectric image better, on the basis of the traditional Otsu algorithm, considering the different influence of image segmentation about the factors of the distance between target and background as well as each kind of cohesion, an improved Otsu algorithm is proposed, and its basic principle and segmentation advantages are analyzed in detail. In order to evaluate these segmentation results impersonally by using different algorithms, the quantitative criteria of gray-level contrast and district interior uniformity are adopted to evaluate these segmentation results impersonally. Finally, the different segmentation experiment contrasts of PCB photoelectric image between our algorithm and other algorithms is executed, the results of experiment indicate that our algorithm has relatively better segmentation quality.     

A measurement method for distinguishing the real contact area of rough surfaces of transparent solids using improved Otsu technique

An experimental method of measuring the real contact area of transparent blocks based on the principle of total internal reflection is presented,intending to support the investigation of friction characteristics,heat conduction,and energy dissipation at the contact interface.A laser sheet illuminates the contact interface,and the transmitted laser sheet is projected onto a screen.Then the contact information is acquired from the screen by a camera.An improved Otsu method is proposed to process the data of experimental images.It can compute the threshold of the overall image and filter out all the pixels one by one.Through analyzing the experimental results,we describe the relationship between the real contact area and the positive pressure during a continuous loading process,at different loading rates,with the polymethyl methacrylate(PMMA)material.A hysteresis phenomenon in the relationship between the real contact area and the positive pressure is found and explained.     

Tank segmentation of infrared images with complex background for the homing anti-tank missile

In an effort to achieve fast and effective tank segmentation of infrared images under complex background for the homing anti-tank missile, the threshold of the maximum between-class variance method (i.e., the Otsu method) is experimentally analyzed, and the working mechanism of the Otsu method is revealed. Subsequently, a fast and effective method for tank segmentation under complex background is proposed based on the Otsu method by constraining the image background pixels and gray levels. Firstly, with the prior information of the tank, derive the equation to calculate the number of pixels of tank according to optical imaging principle, and then use the calculated tank size to constrain the image background pixels. Secondly, employ the golden section to restrict the background gray levels. Finally, use the Otsu method to implement the segmentation of the tank. Experimental results demonstrate that the proposed method can get as an ideal result as the manual segmentation with less running time.     

Fast incremental algorithm for speeding up the computation of binarization

Binarization is an important basic operation in image processing community. Based on the thresholded value, the gray image can be segmented into a binary image, usually consisting of background and foreground. Given the histogram of input gray image, based on minimizing the within-variance (or maximizing the between-variance), the Otsu method can obtain a satisfactory binary image. In this paper, we first transfer the within-variance criterion into a new mathematical formulation, which is very suitable to be implemented in a fast incremental way, and it leads to the same thresholded value. Following our proposed incremental computation scheme, an efficient heap- and quantization-based (HQ-based) data structure is presented to realize its implementation. Under eight real gray images, experimental results show that our proposed HQ-based incremental algorithm for binarization has 36% execution-time improvement ratio in average when compared to the Otsu method. Besides this significant speedup, our proposed HQ-based incremental algorithm can also be applied to speed up the Kittler and Illingworth method for binarization.     

Two-dimensional Otsu's thresholding segmentation method based on grid box filter

Otsu algorithm, an automatic thresholding method, is widely used in classic image segmentation applications. In this paper, a novel two-dimensional (2D) Otsu thresholding algorithm based on local grid box filter is proposed. In our method, firstly by utilizing the coarse-to-fine idea, the 2D histogram is divided into regions by grid technique, and each region is used as a point to form a new 2D histogram, to which 2D Otsu thresholding algorithm and an improved particle swarm optimization (PSO) algorithm are applied to get the region number of the new 2D histogram threshold. Then on the result region, the mean of the 2D histogram is computed base on box filter, and the two algorithms are applied again to obtain the final threshold for the original image. Experimental results on real data show that the proposed algorithm gets better segmentation results than the traditional recursion Otsu algorithm. It significantly reduces the time of segmentation process and simultaneously has the higher segmentation accuracy.     

基于改进的GAC模型图像分割算法

针对图像分割中经典GAC模型无法准确分割拓扑结构变化的凹陷目标、容易穿越深度凹陷的弱边缘轮廓和无法准确分割含噪声目标的问题,通过利用图像灰色关联度、类间方差和经典GAC模型构造新能量函数,提出了一种改进的GAC模型,该模型可增强经典GAC模型的边界检测能力,减少分割时间。仿真实验验证了改进的GAC模型的正确性和有效性。