海量小差异图像群中有效分类方法研究与仿真

对海量小差异图像群进行有效分类,能够极大地满足用户对于图像搜索及管理需求.传统的海量小差异图像分类的方法需要限定大量的分类约束条件,存在分类效率低,无效分类多的弊端.为此,提出基于模糊贴近度改进的海量小差异图像分类方法.利用SIFT算法对采集的海量小差异图像群进行特征提取分析,为进行图像进一步分类提供数据支持.把模糊贴近度的概念引入到分类算法中,对海量小差异图像群进行分类模型构建,求取最优解,完成对海量小差异图像群的最优分类.实验结果表明,运用改进分类算法对海量小差异图像进行分类,能够提高图像分类的效率和分类的准确率.

海量微小差异图像群中智能边缘检测方法仿真

由于海量图像群中会同时含有物体边缘、阴影以及噪声问题,采用常规图像边缘检测方法很难从噪声或是微小差异特征中区分出精确边缘,检测出的边缘存在细节损失严重、边缘不连续以及噪声去除不完全等问题。提出一种结合Canny算子、非下采样Contourlet变换以及模糊C均值聚类方法的图像边缘检测方法。将海量微小差异图像乘性噪声转换为加性噪声形式,结合Canny算子计算差异图像群中边缘方向。根据海量微小差异图像边缘丰富、区域平滑的特点,以及差异图像在灰度上的差异,结合区域生长完成灰度差异的分割。通过非下采样Contourlet变换将图像群中图像分解为低频分量和高频分量,提取边缘信息,采用模糊C均值聚类方法对边缘进行聚类获得低频边缘图像,对于边缘细节信息较多的微小差异图像高频分量各个子带,依据模极大值检测边缘,减少图像为边缘,丰富微小差异图像细节,通过对海量微小差异图像群中低频分量和高频分量进行融合获得完整的图像边缘。实验结果表明,上述方法对高斯噪声具有较好的抑制能力,具有良好的图像边缘定位精度和干扰鲁棒性。

菌群细胞图像分离算法研究

颗粒图像分析中聚堆目标的分离对目标的计数及特征的提取非常重要.现有分离算法都要求聚堆目标粘连处凹陷比较明显,并且/或者存在灰度局部最小边缘.菌群聚堆细胞大小不一、聚堆形态各异,多数聚堆细胞并不具备上述条件.文中提出了一种基于聚堆区域轮廓跟踪、删除的分离算法,依据跟踪“虫”在跟踪过程中遇到已跟踪过轮廓点的情况判断分离的进行.实验结果表明算法对菌群聚堆细胞分离的成功率要高于现有算法10%以上.

图形图像课程群教学改革探索与实践

针对现阶段大学本科图形图像课程群教学过程中,课程体系设计不合理、教材内容陈旧、教学方法枯燥并与评价机制脱节和优势教学资源利用不充分的实际问题,结合多年教学实践经验,从课程体系优化、教学模式、评价机制和发挥资源优势方面探讨本科图形图像课程群的教学方法,提出以此方法激发学生的学习兴趣,因材施教,提高学生自主学习和分析解决问题的能力并对学生成绩进行综合评价。

图形图像课程群教学体系构建探索——以南京中医药大学医学信息工程专业为例

文章针对图形图像技术相关课程部分教学内容重复、现有教学模式缺乏前沿科技敏感度等不足,探讨通过优化图形图像相关课程的知识体系,构建符合专业培养目标与方案要求的图形图像课程群教学体系。专业教师在分析课程群内课程模块功能角色的基础上,纵向梳理课程知识点,横向建立课程间的联系,设计更贴近学生创新能力培养要求的图形图像课程群递进式实验项目及个性化学习评价机制,为中医院校交叉专业人才的培养打下坚实的基础,培养中医药现代化发展综合性人才。

群图像的同伦判定问题(英文)

对有限的群呈示P=〈x;r〉表出的群G,证明了当且仅当群G的字问题可解时,P的图像的同伦问题是可解的.

面向社群图像的显著区域检测方法

网络技术和社交网站的发展带来了社群图像的飞速增长。海量的社群图像成为了非常重要的图像类型。本文关注社群图像的显著区域检测问题,提出基于深度特征的显著区域检测方法。针对社群图像带有标签的特点,在系统框架中,本文采取两条提取线:基于CNN特征的显著性计算和基于标签的语义计算,二者的结果进行融合。最后,通过全连接的条件随机场模型对融合的显著图进行空间一致性优化。此外,为了验证面向社群图像的显著区域检测方法的性能,针对目前没有面向社群图像的带有标签信息的显著性数据集,基于NUS-WIDE数据集,本文构建了一个图像结构丰富的社群图像数据集。大量的实验证明了所提方法的有效性。

微粒群组算法的数码图像修复研究

数码图像的修复是数码图像处理的后期制作技术,主要是使用一定的计算机科学算法针对缺损的数码图像进行修改,或者从数码图像中去除或者淡化一部分,以达到自己的目的。本文具体讲述使用基于微粒群组算法的数码图像修复方法,利用区域采样技术确定初始微粒,然后利用微粒群组算法搜索得到最佳匹配块,最后得到数码图像的修复效果。

传统故事装饰画的图像学解读

由于传统人物故事画上所描绘的物体和事件同现代社会相隔甚远,许多画意濒于失传,近似画谜。为了解开这些图像之谜,我们必须尽力搜集相关资料,尽可能重构这些图像产生的历史原境,只有依靠最接近历史原境的信息进行推理,才可能比较合理地解读画面。本讲座结合文博工作者的读图实例,讨论如何利用图像的原境信息进行推理,从而能够比较正确地解读传统人物故事画。

基于传感器的GPU集群功耗收集监控系统

图像处理器(GPU)集群因其高性能的特性而被广泛应用,但随着GPU规模的增大,其高功耗问题会降低系统的可靠性。为此,提出一种GPU集群功耗收集系统,并设计基于ZigBee无线传感器网络的GPU集群功耗收集监控网络,同时构建收集通信协议和数据库存储系统,通过运行该系统可有效避免通信冲突。实验结果表明,该监控系统可以精确地测量集群中各个GPU的功耗,系统测量误差和丢包率分别低于1%和0.005%。

Clustering-driven watershed adaptive segmentation of bubble image

In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy c means(FCM) algorithm. Through the attributes of size and shape pattern spectrum,the optimal morphological structuring element was determined.According to the optimal parameters,some image noises were removed with an improved area opening and closing by reconstruction operation,which consist of image regional markers,and the bubbles were finely separated from each other by watershed transform.The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum,and the froth image is segmented accurately.Compared with other froth image segmentation method,the proposed method achieves much high accuracy,based on which,the bubble size and shape features are extracted effectively.

An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation

A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrichartificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation.With comprehensive learning,the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area,which provides a proper balance between exploration and exploitation.The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.Furthermore,we applied the MABC algorithm to image segmentation problem.Experimental results verify the effectiveness of the proposed algorithm.

CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING

Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.

协同策略下的卫星数字正射影像图制作

高分辨率对地观测卫星快速发展,卫星影像已经成为人类获取地球空间信息的重要数据源,卫星数字正射影像图是对卫星影像进行一系列处理后生成的数字测绘产品,具有影像直观、信息量丰富、细节表达清楚、像点坐标可量测等特点,在国民经济和军事等各个领域都有广泛的应用价值。本文针对海量卫星数字正射影像图的制作需求,首先,分析了地球成像加速器、集群图像处理系统、特征控制引擎的技术特性及三者协同处理基础;然后,提出了一种跨平台协同策略下的高效全流程卫星数字正射影像图制作方法,该方法以卫星原始影像、数字高程模型、参考影像为输入,通过地球成像加速器、集群图像处理系统、特征控制引擎的协同处理,快速完成几何纠正、匀色处理、镶嵌裁切等主要流程后生成数字正射影像图;最后,利用WorldView-Ⅱ高分辨率多光谱卫星影像和全色卫星影像进行试验。结果表明,该方法可高效制作符合精度要求的卫星数字正射影像图产品。

Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

The San Juan Islands Thrust System： New Perspectives from LIDAR and Sonar Imagery

New LIDAR （Light Detection and Ranging） and sonar imagery have revealed remarkable geomorphic details never seen before and not visible by any other means. Numerous faults and other geologic structures are plainly visible on LIDAR and sonar images. Many previously unknown faults criss-cross the islands and large fault scarps are visible on sonar imagery along the margins of the larger islands. Sonar images of sea floor morphology show many submerged faults as long linear scarps with relief up to 300m （1,000 fl）, some of which visibly truncate geologic structures. The San Juan Lopez fault, the largest fault in the islands, extends for at least 65 km （40 mi） from Stuart Island to Rosario strait with a scarp up to 330m （1,000 it） high. Since 1975, the basic structural framework of the San Juan Islands has been considered to consist of five stacked thrust faults, the Rosario, Orcas, Haro, Lopez, and Buck Bay faults, constituting the San Juan Thrust （Nappe） System that has shuffled together far distant terranes. However, the new LIDAR and sonar imagery shows that most of the mapped extent of these postulated faults are actually segments of high angle, dipslip faults and are not thrust faults at all. Thus, the San Juan Thrust （Nappe） System does not exist. The age of these faults is not accurately known and more than one period of high angle faulting may have occurred. Faults shown on L1DAR images of the surface of the islands appear as visible gashes, etched out by erosion of fault zones with few fault scarps. However, the sea floor faults have bold relief and high scarps. A late Pleistocene moraine lies undisturbed across the San Juan Lopez fault.

Single Image Super-Resolution by Clustered Sparse Representation and Adaptive Patch Aggregation

A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.

Research on Image Segmentation Algorithm based on Fuzzy C-mean Clustering

This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.