一种精确检测细胞真实边缘的算法

分析了传统边缘检测算法在细胞边缘检测和形态分析中存在的缺陷。提出了最佳阈值分割和轮廓提取相结合的细胞边缘检测新方法。通过理论分析和实验,证明该方法能有效抑制噪声干扰的影响,实现细胞真实边缘的精确检测。     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

自然地面背景下红外目标检测算法

为了快速准确地检测出以地面物体为背景的红外序列图像中的目标,结合小波变换的特点及其应用于图像边缘检测的优点,提出了一种基于小波技术的图像预处理算法。该方法利用图像边缘在目标识别中的重要作用,讨论了基于小波变换的图像边缘检测方法,并结合地面背景红外图像自身的特点,大大地提高了单帧的检测概率。     

基于遗传算法的指纹图像分割算法

在指纹技术中,指纹图像分割是图像处理中关键的一步。将遗传算法用于计算指纹图像分割阈值,采用染色体编码的方法并利用赌轮法产生初始化种群,通过选择算子、交叉算子和变异算子的方法实现分割。该方法表明利用遗传算法所得到的阈值进行分割处理能取得较好的效果。     

The equation of motion for the trajectory of a circling particle with an overdamped circle center

Inspired by biological microorganisms swimming in circles in liquid with low Reynolds number, I developed the dynamic theory for computing the helical trajectory of a circling particle with an overdamped circle center. The equation of motion for the circling particle is a hybrid equation of deterministic terms and stochastic terms. Observing the motion of a swimming microorganism, I found the strength of stochastic fluctuations should be much smaller than that governs deterministic dynamics. This dynamic theory predicts a nonlinear transverse motion perpendicular to the direction of external force. Both the living microorganism and artificial circling particle are applicable for an experimental check of this prediction. For the convenience of easy theoretical research, I further derived the probability conservation equations based on this dynamic theory both in two-dimensional and three-dimensional space.     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

一种视频图像规则圆目标的超分辨率位置测量方法

介绍了一种用于视频图像规则圆目标的超分辨率位置测量方法。分析了超分辨率测量的原理 ,并提出和完成了测量方案 ,取得了满意的结果。首先在目标静止时 ,连续采集多帧视频图像 ,通过叠加平均进行降噪 ;其次通过罗伯特微分算子进行边界探测 ,求出目标的大致位置 ;最后通过边界重心拟合法、边界圆拟合法和最小二乘法拟合求出目标的精确位置。理论分析和实验表明 ,完整圆目标时 ,测量精度可达 1/4像素 ;而当目标图像有缺陷时 ,边界圆拟合法和最小二乘法拟合测量误差增加较少 ,边界重心法的测量误差增加较多。在具体情况下 ,算法及参数应该作相应的调整     

基于多尺度分解的超光谱图像异常检测

提出了一种基于多尺度分解的超光谱图像异常检测算法。在目标和背景均未知的前提下,利用光谱和空间两种信息完成对异常目标信号的定位,从而实现超光谱遥感数据中异常目标检测。首先利用非下采样塔式变换对超光谱图像进行分解,将其划分为不同尺度子块;然后依据超光谱图像同一波段不同尺度空间内相邻系数的相关性,采用不同波段各个尺度空间的反锐化掩模方法优化背景数据分布,从而抑制异常数据对背景的干扰;最后利用设计的核RX算子得到异常目标检测结果。为验证方法的有效性,利用真实和模拟的AVIRIS数据进行了实验,并与经典RX算法相比较,实验结果表明,基于非下采样塔式分解的异常检测方法具有更好的检测性能和较低的虚警。     

基于多尺度分解的超光谱图像异常检测

提出了一种基于多尺度分解的超光谱图像异常检测算法。在目标和背景均未知的前提下,利用光谱和空间两种信息完成对异常目标信号的定位,从而实现超光谱遥感数据中异常目标检测。首先利用非下采样塔式变换对超光谱图像进行分解,将其划分为不同尺度子块;然后依据超光谱图像同一波段不同尺度空间内相邻系数的相关性,采用不同波段各个尺度空间的反锐化掩模方法优化背景数据分布,从而抑制异常数据对背景的干扰;最后利用设计的核RX算子得到异常目标检测结果。为验证方法的有效性,利用真实和模拟的AVIRIS数据进行了实验,并与经典RX算法相比较,实验结果表明,基于非下采样塔式分解的异常检测方法具有更好的检测性能和较低的虚警。     

A new level set model for cell image segmentation

In this paper we first determine three phases of cell images:background,cytoplasm and nucleolus according to the general physical characteristics of cell images,and then develop a variational model,based on these characteristics,to segment nucleolus and cytoplasm from their relatively complicated backgrounds.In the meantime,the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model,which can speed up the segmentation and present satisfactory results in cell image processing.     

Wavelet subspace decomposition of thermal infrared images for defect detection in artworks

Health of ancient artworks must be routinely monitored for their adequate preservation. Faults in these artworks may develop over time and must be identified as precisely as possible. The classical acoustic testing techniques, being invasive, risk causing permanent damage during periodic inspections. Infrared thermometry offers a promising solution to map faults in artworks. It involves heating the artwork and recording its thermal response using infrared camera. A novel strategy based on pseudo-random binary excitation principle is used in this work to suppress the risks associated with prolonged heating. The objective of this work is to develop an automatic scheme for detecting faults in the captured images. An efficient scheme based on wavelet based subspace decomposition is developed which favors identification of, the otherwise invisible, weaker faults. Two major problems addressed in this work are the selection of the optimal wavelet basis and the subspace level selection. A novel criterion based on regional mutual information is proposed for the latter. The approach is successfully tested on a laboratory based sample as well as real artworks. A new contrast enhancement metric is developed to demonstrate the quantitative efficiency of the algorithm. The algorithm is successfully deployed for both laboratory based and real artworks.     

Multilayered electrochemical cell for NOx decomposition at moderate temperatures

A new multilayered electrochemical cell for NO_x decomposition was prepared. The cell demonstrated good characteristics at moderate temperatures (350–450 °C) and possessed much higher selectivity with respect to NO_x decomposition than previous one. It was found that the “crossover” process from high selective regime to the normal regime depends on the chemical composition of the catalytic electrode.     

Recognition algorithm of internal defect images of thermal battery北大核心CSCD

针对目前热电池内部装配缺陷检测效率低、准确度不高的问题,研究了一种可精准分割内部电池堆图像并能够准确识别缺陷种类的方法。首先采用水平、垂直积分投影法对目标电池堆边缘特征进行提取,利用局部自适应对比度增强算法对局部不清晰部分进行细节纹理增强;然后研究了缺陷结构的灰度特性,计算提取出缺陷特征参数;最后使用BP（back propagation）神经网络和CART（classification and regression tree）决策树对特征参数分类识别,并根据分类准确度进行权重分配,将加权融合后的结果作为检测的最终判据。实验结果表明:该方法对2 000个样本的检测准确度达98.9%,为热电池的X射线缺陷检测提供了有效的途径。     

基于均值漂移的遥感图像海陆边界分割算法

光学遥感图像海陆边界分割是海洋近岸目标检测和识别的重要技术基础。由于光学遥感图像分辨率的提高,海陆边界分割存在背景复杂、干扰多等问题。为解决复杂背景下高清遥感图像海陆边界分割问题,提出了一种新的海陆边界分割算法。该方法包含三个部分:使用均值漂移算法将遥感图像处理成若干同质区域;采用一种新的基于扫描线的方法选择海水区域种子像素点,利用区域增长算法填充海水区域;通过连通区域分析的方法分离陆地部分,得到海陆分割结果。实验证明,该方法能够对于复杂背景下的光学遥感图像实现准确、稳定的海陆分割,算法具有较强的鲁棒性和准确性。     

A Clustering Approach for the Separation of Touching Edges in Particle Images

The occurrence of touching objects in images of particulate systems is very common especially in the absence of dispersion methods during image acquisition. The separation of these touching particles is essential before accurate estimation of particle size and shape can be achieved from these images. In the current work, clustering approaches based on the fuzzy C‐means algorithm are employed to identify the touching particle regions. Firstly, clustering in the multidimensional space of image features, e.g., standard deviation, gradient and range calculated in a certain neighborhood of each pixel, is performed to trap the touching regions. Then, in a novel proposed method, the clustering of pixel intensity itself into two fuzzy clusters is performed and a feature, referred to as the ‘Fuzzy Range', is calculated for each pixel from its membership values in both clusters and is presented as a distinguishing feature of the touching regions. Both approaches are compared and the superiority of the latter method in terms of the non‐necessity of neighborhood based calculations and minimum disfiguration is elucidated. The separation methods presented herein do not make any assumption about the shape of the particle as is undertaken in many methods reported elsewhere. The technique is proven to minimize greatly the deleterious effects of over‐segmentation, as is the case with traditional watershed segmentation techniques, and consequently, it results in a superior performance.     

Toward automatic evaluation of defect detectability in infrared images of composites and honeycomb structures

Non-destructive testing (NDT) refers to inspection methods employed to assess a material specimen without impairing its future usefulness. An important type of these methods is infrared (IR) for NDT (IRNDT), which employs the heat emitted by bodies/objects to rapidly and noninvasively inspect wide surfaces and to find specific defects such as delaminations, cracks, voids, and discontinuities in materials. Current advancements in sensor technology for IRNDT generate great amounts of image sequences. These data require further processing to determine the integrity of objects. Processing techniques for IRNDT data implicitly looks for defect visibility enhancement. Commonly, IRNDT community employs signal to noise ratio (SNR) to measure defect visibility. Nonetheless, current applications of SNR are local, thereby overseeing spatial information, and depend on a-priori knowledge of defect’s location. In this paper, we present a general framework to assess defect detectability based on SNR maps derived from processed IR images. The joint use of image segmentation procedures along with algorithms for filling regions of interest (ROI) estimates a reference background to compute SNR maps. Our main contributions are: (i) a method to compute SNR maps that takes into account spatial variation and are independent of a-priori knowledge of defect location in the sample, (ii) spatial background analysis in processed images, and (iii) semi-automatic calculation of segmentation algorithm parameters. We test our approach in carbon fiber and honeycomb samples with complex geometries and defects with different sizes and depths.     

A 3D shape retrieval method for orthogonal fringe projection based on a combination of variational image decomposition and variational mode decomposition

The orthogonal fringe projection technique has as wide as long practical application nowadays. In this paper, we propose a 3D shape retrieval method for orthogonal composite fringe projection based on a combination of variational image decomposition (VID) and variational mode decomposition (VMD). We propose a new image decomposition model to extract the orthogonal fringe. Then we introduce the VMD method to separate the horizontal and vertical fringe from the orthogonal fringe. Lastly, the 3D shape information is obtained by the differential 3D shape retrieval method (D3D). We test the proposed method on a simulated pattern and two actual objects with edges or abrupt changes in height, and compare with the recent, related and advanced differential 3D shape retrieval method (D3D) in terms of both quantitative evaluation and visual quality. The experimental results have demonstrated the validity of the proposed method.     

Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images

Interaction between bacteria and protozoa is an increasing area of interest, however there are a few systems that allow extensive observation of the interactions. A semi-automated approach is proposed to analyse a large amount of experimental data and avoid a time demanding manual object classification. We examined a surface system consisting of non nutrient agar with a uniform bacterial lawn that extended over the agar surface, and a spatially localised central population of amoebae. Location and identification of protozoa and quantification of bacteria population are performed by the employment of image analysis techniques in a series of spatial images. The quantitative tools are based on intensity thresholding, or on probabilistic models. To accelerate organism identification, correct classification errors and attain quantitative details of all objects a custom written Graphical User Interfaces has also been developed.     

SAR图像中机场跑道自动检测方法研究

跑道检测通常由边缘检测和霍夫变换2个步骤组成.由于SAR图像中存在大量斑点噪声,使得边缘检测中存在大量虚假边缘,增加了霍夫变换的时间,降低了跑道检测的准确度,提出一种基于区域分割和距离变换的SAR图像中机场跑道自动检测的新方法.采用基于统计信息的方法增强跑道一背景的对比度,然后用二维直方图阈值分割方法分割图像,再通过距离变换对跑道一背景二值图进行处理,得到跑道中心线的大致分布,最后采用局部空间霍夫变换得到跑道参数.实验结果表明:该方法能够可靠检测出跑道,运算速度满足实时性要求.     

基于数学形态学的表面原子熔融相的 STM图像识别算法

高温扫描隧道显微镜(HT-STM)可以实时原位地捕捉到表面原子的熔化相变过程.在这一原位变温实验中,快速可靠地识别出每帧STM图像中的熔融相十分关键.传统的手工统计方法存在效率低下、随意性大等问题.我们发展出一套基于数学形态学的算法,来自动快速地识别.与人工方法相比,该算法消除了人为主观误差,使确定的边界更加准确、光滑,处理效率提高了266倍.