一种基于DA-GMRF的无监督图像分割方法

提出一种基于间断自适应高斯马尔可夫随机场(DA-GMRF)模型的无监督图像分割方法.针对MRF模型中的过平滑问题,利用边缘信息构造能量函数,定义了一种DA-GMRF模型.利用灰度直方图势函数自动确定分类数及分割阈值,进行多阈值分割,得到DA-GMRF模型中标记场的初始化,用Metroplis采样器算法进行标记场的优化,实现了图像的无监督分割.实验结果表明了该方法的有效性.     

一种改进的多方位结构元素形态学和互信息量的图像分割算法

提出了一种改进的基于多结构元素形态学与互信息量相结合的图像分割算法。算法首先利用相邻像素差分运算标记出图像中存在梯度变化的像素点,然后对这些像素点进行多结构元素形态学运算,确定一个初始阈值;然后以互信息量为目标函数,在小范围内计算分割图像与原图像的互信息量,以确定最优阈值。通过对大量路面破损图像进行的实验表明,该改进算法所得到的分割图像目标边缘特征保持完好,具有很强的抗噪能力,且处理速度很快。     

手背静脉图像二值化阈值算法

因为传统分割算法对图像细节特征信息不敏感,分割效果不理想,提出了一种确定阈值的改进方法。该算法在阈值图像法的基础上,采用迭代计算平均值的方法,动态地获得每个像素点位置的最佳阈值,以获取一幅与原图像大小相同的阈值图像,利用该阈值图像对原静脉图像进行二值化处理,增强了图像的局部细节特征信息。仿真实验证明了该改进算法的有效性。     

基于雁群优化的声呐图像快速阈值分割方法

针对声呐图像阈值分割中,直接阈值法分割效果不理想而复杂阈值法运算效率低的问题,提出一种基于雁群优化算法的快速分割方法。该方法结合声呐图像的特点,根据先验知识截取部分灰度-梯度二维直方图,对其进行最大熵分割,通过计算熵值确定最佳阈值,并在寻找最佳阈值的过程中,引入雁群优化算法进行提速。实验结果表明,该方法用于声呐图像分割时,能够得到较理想的分割效果,同优化前相比,分割速度提高了10倍以上。     

基于MATLAB的图像分割算法分析

作为数字图像处理的关键技术,图像分割在图像分析系统中发挥了不可忽视的作用。随着科学技术的不断进步,有很多不同的算法被应用到图像分割技术中,但是最常用的分析方法包括基于阈值的分割方法、基于区域的分割方法、基于特定理论的分割方法以及基于边缘的分割方法。该文主要利用MATLAB软件对基于边缘的分割算法和基于阈值图像的分割算法进行仿真分析。阈值分割技术的关键在于确定阈值,利用Otsu算法能够自动选取阈值,对图像进行分割;在边缘检测算法中,对4种算子(Roberts、Sobel、Prewitt和LOG)的分割结果进行图像分析可以得出,LOG检测算子得出的边缘检测结果最好。     

一种改进的二维最小交叉熵图像分割方法

针对当前二维最小交又熵阈值法存在计算复杂度高等问题,提出了一种改进的二维最小交叉熵阈值分割方法.首先,依据图像的含噪声类型选择邻域模板并建立相应的二维直方图来提高分割效果;然后,对二维最小交又熵公式进行推导和简化处理,利用定义的数组运算推导出新型递推算法,再确定图像及其邻域图像的实际灰度级别范围,并用这种新算法在所求的灰度级别范围内搜索最佳阈值向量来降低计算复杂度;最后,使用关键阈值一对滤波后的图像进行分割达到最佳的分割效果.仿真实验结果表明,与当前的二维最小交又熵阁值分割法相比,本文提出的方法不仅分割性能及抗噪性能更强,而且分割时间大大减少,小于0.05 s.     

DNA遗传算法的改进二维最大熵图像分割

传统的二维最大熵图像分割算法在求解阈值时将二维直方图的噪声和边缘区域近似为零,降低了分割精度。针对这一问题,本文提出了一种基于DNA遗传算法的改进二维最大熵快速图像分割算法。利用梯度-均值灰度直方图得到有用区域,并以改进的二维最大熵作为优化函数,采用DNA计算遗传算法得到二维最优阈值。实验表明该算法对图像分割去噪能力强,分割效果好,以及快速有效处理能力。     

基于遗传算法的干涉图像样的最佳熵研究

将遗传算法用于计算云纹干涉图像的二值化阈值，提出基于改进遗传算法的图像分割方法，采用Otsu公式，找出分割图像最优阈值。通过算法实现表明，利用遗传算法所得到的最佳阚值进行二值化处理，效果非常好。     

基于并行遗传算法的Otsu双阈值医学图像分割

传统遗传算法用于搜索某些函数极值时精确度较低且稳定性较差。针对该问题,提出了一种基于并行遗传算法的Otsu双阈值医学图像分割算法。在该算法中,进化在多个不同的子群中并行进行,避免单种群进化过程中出现的过早收敛现象,提高整个算法的收敛速度。100次阈值计算实验结果表明,提出的分割算法与传统遗传算法相比,不仅能够对图像进行准确的分割,而且具有更强的精确性和稳定性。其收敛速度明显优于基于单种群的遗传算法的Otsu双阈值医学图像分割。     

基于多分辨率分析和分水岭的图像分割方法

提出了一种基于小波多分辨率分析和分水岭算法的图像分割方法.在小波分解后的低分辨率图像上进行分水岭分割,提高了分割的速度;由低分辨率图像返回到高分辨率图像时,采用了一种基于边缘信息的合并函数,避免了边缘信息的丢失,保证了分割的准确性.此外预处理过程中,在梯度图像上基于Rayleigh分布采用阈值处理的方法,有效抑制了高斯噪声对梯度图像的影响,避免了过分割.实验结果证明,本文所提出的基于小波多分辨率分析的图像分水岭分割算法能够很好地兼顾算法的效率和分割的准确性.     

基于比例特征的区域分割算法在药板缺陷检测中的研究

目的 为解决铝塑泡罩药板图像ROI区域定位慢、精度差等问题,本文提出一种基于比例特征的泡罩区域分割算法,该算法可以快速定位并分割泡罩ROI区域,结合图像相关性特征算法对铝塑泡罩药板进行缺陷检测。方法 首先通过工业相机采集药品包装生产线上的药板原始图像,接着使用Blob分析从原始图片中分离出铝塑泡罩主体部分,然后通过仿射变换将图像放置在中心区域,并使用比例特征分割算法对泡罩区域进行分割,最后通过金字塔加速的NCC算法完成缺陷检测。结果 实验结果表明,基于比例特征分割后的图像平均NCC匹配时间为9 ms,在缺陷样本占比20%的实验中误检率为0.167%,漏检率为0.556%。结论 通过比例特征分割出精准的泡罩ROI区域结合改进的NCC算法,在拥有较高准确率的同时大幅减少了缺陷检测时图像匹配的时间,能较好地完成铝塑泡罩药板的缺陷检测任务。     

改进樽海鞘群优化K-means算法的图像分割

目的 针对樽海鞘群算法寻优精度低、易陷入到局部最优,以及K-means算法进行图像分割容易被初始聚类中心干扰等缺点,提出改进樽海鞘群优化K-means算法的图像分割。方法 首先利用Circle映射来对樽海鞘种群进行初始化;其次引入莱维飞行到领导者和追随者位置更新公式中,使得樽海鞘种群的多样性得到提高,克服算法陷入到局部最优。最后,对改进樽海鞘群算法先采用8个基准函数进行性能测试;再将改进樽海鞘群算法优化K-means进行图像分割。结果 改进算法在寻优精度、稳定性、收敛速度以及跳出局部最优的本领得到了提高。同时,改进樽海鞘群优化K-means算法进行图像分割,有效地提高了图像分割质量。结论 改进算法改善了原始樽海鞘群算法的寻优精度低、易陷入到局部最优的缺点,很好地优化了K-means算法对图像进行准确分割,在图像分割领域具有一定的参考意义。     

Airplane extraction and identification by improved PCNN with wavelet transform and modified Zernike moments

At an airport, the information of the number and positions of airplanes is very important for the applications of air navigation. Especially, the information from airplane extraction and identification is significant in both civil and military remote sensing. In this paper, according to the characteristics of airplanes and airport in satellite remote sensing images, a new airplane image segmentation algorithm is proposed based on improved pulse-coupled neural network (PCNN) with wavelet transform, and airplane identification algorithm is carried out by using modified Zernike moments. Firstly, for an original image, a PCNN model is improved and then used to do image segmentation by combining the wavelet transform. Then, in order to reduce the number of irrespective targets in the image and increase the processing speed, the airplanes in the original image are roughly detected on the characteristics of the segmented object contour geometries. Finally, the Zernike moments are modified and then applied to identify the roughly detected airplanes accurately. By comparing to the five traditional image segmentation algorithms for the same airplane images, the testing results show that the improved PCNN image segmentation algorithm can segment and detect airplane regions at an airport accurately at a high recognising rate and with high recognising stability, and it is not affected by the image shadows and rotations.     

High-speed MRF-based segmentation algorithm using pixonal images

Abstract

Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-image segmentation techniques. To evaluate the proposed algorithm and compare it with the last best algorithms, many experiments on standard images were performed. The results indicate that the proposed algorithm is faster than other methods, with the most segmentation accuracy.     

基于Shamir门限方案和DWT的图像盲水印算法

目的 为了提高数字水印的抗剪切等方面的鲁棒性和不可见性,提出一种基于Shamir门限方案和DWT的图像盲水印算法.方法 首先将水印图像利用Arnold变换进行加密处理,然后对置乱后水印图像用Shamir门限方案进行分存,接着将原始图像进行分块,并对每块分别做二层离散小波分解,提取出相应的低频系数,通过叠加的方法将分存过后得到的子水印图像分别嵌入相对应的原始图像分块的低频系数中,最后合成图像,完成水印的嵌入.结果 实验结果显示,文中算法的不可见性较好,峰值信噪比均在49 dB以上,结构相似性接近于1,并且在各种攻击下,水印NC始终大于0.9.结论 文中算法对于剪切、JPEG压缩和常见的噪声干扰等攻击表现出良好的鲁棒性,并且水印的提取过程无需用到原始图像,实现了水印的盲提取,在版权保护方面具有可行性.     

Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame. Finally, the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine, and the recognition results were obtained. The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013     

改进 Otsu 算法在铝塑泡罩药品包装缺陷检测中的应用

目的为了满足药品缺陷检测系统的具体要求,需要有更好的图像分割效果。方法在二维Otsu法的基础上,提出新的阈值分割输出函数,并改进传统算法,使运算量大幅度降低。结果理论分析和仿真实验结果表明,该方法更能顾及药品图像边缘细节,具有好的分割效果,且分割速度也能满足实时检测要求。结论该改进算法用于铝塑泡罩药品包装缺陷检测更具有适用性和实时性。     

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.