基于白光干涉无重叠拼接的微结构表征方法

针对白光干涉技术(WLI)应用于大范围测量时图像重叠拼接会引入误差并降低测量效率这一缺欠,本文基于纳米测量机(NMM)提出了一种无重叠拼接的白光干涉测试方法。利用NMM的高精度定位能力,扩展了普通白光干涉仪的测量范围,实现了对微结构近4倍于CCD视场的大范围测量,验证了无重叠图像拼接的可实现性。     

狭缝光栅自由立体显示器的立体图像串扰度

为了表征狭缝光栅自由立体显示器存在的左右眼视差图像的混叠程度,提出了立体图像串扰度C的概念。根据狭缝光栅自由立体显示器的结构和工作原理,应用几何光学知识,分析得出了立体图像串扰度C的计算公式,并给出了一个具体的狭缝光栅自由立体显示器的计算结果。通过观看实验,证明了所定义的立体图像串扰度C可以定量描述观看者在立体可视区域看到的立体图像的串扰程度。     

基于掩模图像的磁环表面缺陷提取研究

磁环表面缺陷图像具有对比度低、纹理背景复杂 、缺陷种类多和亮度不均匀等特点,提出了一种 基于掩模图像的磁环表面缺陷提取方法。首先,通过分析磁环表面图像中不同缺陷区域与正 常区域的灰度 差别,将磁环表面缺陷划分为两类,第1类为缺陷灰度值与正常区域差别大易分割,第2类为 缺陷灰度 值与正常区域灰度值差别小不易分割;其次,根据两类表面缺陷的成像特点以及与背景的 关系,利用掩 模技术分别设计了相应的缺陷提取方法;最后,在不同的光照、规格、缺陷类型及大小等方 面,利用开发 的样机进行了大量的在线实验。实验结果表明,本文缺陷提取算法稳定性好,鲁棒性强,能 够准确、快速 地提取出磁环表面图像各区域的缺陷,表面缺陷检测的准确率为95.3%。     

Deeply fusing multi-model quality-aware features for sophisticated human activity understanding

Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently. Nowadays, human action recognition is still a tough challenge due to intra-class and inter-class, environment and temporal-level differences of the same action. Algorithms based on the single visual channel cannot achieve satisfactory performance. Thus, in this paper, we propose a novel action recognition framework towards sophisticated activity understanding, focusing on intelligently combining multimodel quality-related action features. Specifically, we first design a multi-channel feature fusion (MCFF) algorithm to capture visual appearance, motion and acoustic patterns from each video frame, where image-level labels are characterized by choosing high quality multimodel features. Subsequently, we design an adaptive key frame selection algorithm that can be applied to characterize human action from human action video stream. Thereafter, we engineer a multimodel feature based on an auxiliary human action retrieval system to achieve sophisticated activity understanding. Extensive experimental evaluations have demonstrated that the effectiveness and robustness of our proposed method.     

Deep multi query image retrieval

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy.     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

基于剪切波域改进Gamma校正的医学图像增强算法

为了解决医学图像在采集和传输过程中引入噪 声和干扰导致图像质量恶化从而严重影响医学诊断的问题,提出 一种基于剪切波(shearlet)域改进Gamma校正的图像增强方法。首先,通过剪切波变换,把 图像分解成高频 部分和低频部分;其次,用改进的Gamma校正处理剪切波分解后的低频部分以调整图像的整 体对比 度,采用改进的自适应阈值函数对高频部分进行去噪;最后,把剪切波反变换的重构图 像进行模糊对比 增强,以突出图像的细节信息。实验结果表明,本文算法的峰值信噪比(PSNR )、结构相似度(SSIM)和 绝对均值差(MAE)优于其他对比算法,尤其是PSNR的提升更加明显。这些 客观指标说明,本 文算法不仅能有效地抑制噪声,而且能明显改善增强对比度。从主观方面观察,本文算法与 其他算法相比,能获得更好的视觉效果。     

基于CT图像的肺实质分割技术研究

基于CT图像的肺实质分割不仅仅是后续图像处理最基础和最重要的技术,而且是一个典型的亟待解决的问题。基于CT图像的精确的肺实质分割是数学分析、计算机辅助分析和治疗的前提。本文明确了肺实质分割的概念及本质,以及肺实质分割在临床治疗中的重大意义。重点介绍了阈值法、区域生长法、主动轮廓模型和遗传算法,分析了它们的本质以及优缺点,并且列举大量实例说明。最后指出了基于CT图像的肺实质分割方法的发展趋势与面临的挑战。     

叶簇穿透雷达系统设计综述

叶簇穿透(FOPEN)雷达是一种用于发现和表征密集叶簇下人为目标及表征叶簇自身的技术手段。这种雷达已经在军事监视和民用地理空间成像方面得到应用。本次讲座分为三大部分:第一部分讲述FOPEN雷达的早期历史,主要讲述战场监视及叶簇穿透雷达的早期试验。其中,某些雷达技术的发展意义重大,支撑探测密集叶簇下面固定和移动目标的能力。这些技术中最重要的部分就是众所周知的相干雷达与数字处理技术。同样重要的是雷达穿透叶簇的量化及其散射损耗影响。第二部分重点讨论了几种军民用FOPEN合成孔径雷达系统及其研究成果。在对每种雷达系统概述过程中还通过举例说明SAR图像及其固定目标探测能力。这一部分还给出了量化极化分集在探测和表征人为和自然目标时的好处。在目标描述与降低虚警方面,极化技术的优势明显。最后讨论了超宽带和超宽角成像技术。第三部分讨论的是多模超宽带雷达最新研究及SAR和动目标显示(MTI)FOPEN系统的设计。重点是设计这些超宽带(UWB)系统的优势和困难,以及实际电磁环境下的工作情况。讲座最后两部分阐述了文献中出现的一些用于未来多模式工作的新技术:探测可辨别低速运动目标的需求;双基地SAR与固定GMTI照射波形的协同工作。     

运用压缩传感理论的交通视频图像处理

目前图像的编解码都是运用传统的香农定理进行的,此定理要求采样频率要大于等于原始信号最高频率的两倍。在进行视频图像处理时,就产生了需要相当大的存储空间来存储获得的数据的问题。鉴于此,提出了运用压缩传感理论进行编解码的方法,它可在相同的压缩率下产生更少的压缩数据,进而可以节约存储空间和传输时间。首先进行了传统的信号获取和处理与压缩传感理论的比较,再对压缩传感理论进行了阐述,最后把压缩传感理论应用于交通视频图像处理中,获得了较好的效果,这也是压缩传感理论的一次尝试性的应用。