货车枕簧丢失故障动态图像识别方法

针对货车运行故障动态图像检测,提出无故障目标识别工作模式,解决货车枕簧丢失故障的自动识别问题。利用Haar特征提取枕簧特征信息,基于AdaBoost算法选取特征并构建层叠分类器,等比缩放搜索窗口检测货车图像,最终分选出无故障的枕簧图像,从而大大地减少了待识别图像的数量,显著地提高了人工识别效率。实验表明,该算法使用的特征简单,搜索策略高效,不受枕簧位置、缩放和旋转的影响,抗噪能力强,对分辨率低、局部遮挡、光照不足或过度曝光等质量较差的图像仍具有很强的适应性,所提出的方案能够满足全天候条件下的货车枕簧目标识别,为货车故障动态图像检测的工程化应用奠定了基础。     

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.     

基于数学形态学和匹配滤波的视网膜血管图像增强方法

针对视网膜图像成像过程的特点,提出了一种新的视网膜图像血管信息增强方法。由于存在光照不均匀的情况,采用同态滤波的方法设计了一种滤波器,对光照不均匀的情况进行了有效的校正;使用数学形态学中的Top Hat变换,对背景信息进行抑制,使得血管信息得到增强;最后进一步采用匹配滤波的方法,探测血管信息,使得血管信息的亮度得到提高,增强了与背景的对比度,血管信息具有明显的可视性。实验结果表明了提出的方法的有效性,对临床疾病的诊断及治疗具有较大的参考价值。     

Face recognition based on PCA image reconstruction and LDA

Face recognition has become a research hotspot in the field of pattern recognition and artificial intelligence. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two traditional methods in pattern recognition. In this paper, we propose a novel method based on PCA image reconstruction and LDA for face recognition. First, the inner-classes covariance matrix for feature extraction is used as generating matrix and then eigenvectors from each person is obtained, then we obtain the reconstructed images. Moreover, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The simulation experiments illustrate the effectivity of this method on the ORL face database.     

多特征融合的交通标志检测与分类研究

针对国内交通标志的属性特征,提出了一种基于颜色和形状的交通标志分类算法。采用RGB模型分量值互减的方法对自然背景下的交通标志图像进行分割,提取出感兴趣区域,对标志区域进行形态学处理、边缘检测,提取标志外层轮廓。利用Hough变换识别交通标志线性特征。在识别交通标志颜色和几何形状的基础上,利用交通标志的分类知识实现交通标志的快速分类。实验结果表明,该方法容易实现,满足实时性要求,并能达到较好的分类效果。     

基于数学形态学的快速糖尿病视网膜病变自动检测方法

基于眼底视网膜图像的糖尿病视网膜病变(糖网)自动检测不仅可使得实施大规模糖网筛查成为可能,也可为糖网早期诊断、及时治疗以及人眼视觉科学研究提供重要依据。为此,提出了基于数学形态学的糖网病灶自动检测算法:首先利用数学形态学结合阈值分割快速提取出视盘,在此基础上得到病灶候选区域;然后利用形态学重建等获取精确的病灶轮廓,从而实现病灶的准确检测。实验结果表明,该算法能够快速,有效地检测出眼底视网膜图像中的糖网病灶。     

Kernel-aligned multi-view canonical correlation analysis for image recognition

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.     

An automatic image analysis system for the evaluation of dielectric track detectors

The automatic image analysis system for scanning solid state nuclear track detectors is designed by utilizing a microdensitometer. The control of the entire system as well as the data analysis are accomplished with the IBM PC computer equipped with a FrameGrabber. The system counts the number of tracks and measures their geometrical parameters. The usage of the system is illustrated by counting rhombic tracks in mica detectors which were used for the investigation of the fusion–fission reaction ¹²C+ ²⁰⁸Pb.     

Cooling analysis of welded materials for crack detection using infrared thermography

Infrared thermography offers a wide range of possibilities for the detection of flaws in welding, being the main difference among them the thermal excitation of the material. This paper analyzes the application of an inexpensive and versatile thermographic test to the detection of subsurface cracks in welding. The procedure begins with the thermal excitation of the material, following with the monitoring of the cooling process with IRT (InfraRed Thermography). The result is a sequence of frames that enables the extraction of thermal data, useful for the study of the cooling tendencies in the defect and the non-defect zone. Then, each image is subjected to a contour lines algorithm towards the definition of the morphology of the detected defects. This combination of data acquisition and processing allows the differentiation between two types of cracks: toe crack and subsuperficial crack, as defined in the quality standards.     

基于软X射线谱学显微双能衬度图像的元素空间分布研究

上海光源软X射线谱学显微实验站(BL08U)可实现30nm空间分辨的元素探测功能.实验中,对样品内要标识元素的吸收边附近两个能量(E1和E2,且E1E2)分别进行扫描成像,然后将这两幅吸收图上对应像素点的光密度相比,得到的相比双能衬度图像可有效地显示该元素的空间分布信息.相对于另一种常用的相减双能衬度图像分析法,这种方法对元素空间分布的显示更为灵敏、精确,判断空间点是否含有要标识元素的阈值的定义更为简单,物理意义更为清晰.通过对聚丙烯腈预氧化纤维截面氧元素分布的测量结果的分析计算,比较了这两种分析方法的差异,验证了相比双能衬度法的有效性.该方法为快速标识样品中的微量元素提供了一种很好的新型分析手段.     

粒子场全息分析中的自动判焦与自动阈值选择方法

本文提出一种新的自动判焦与自动阈值选择方法,并在由IBM/PC-XT计算和PCVISION plus图象板组成的系统上实现。通过对实际粒子场全息图的分析,表明该方法效果良好,是一种更一般的、适应性更广的方法。     

Mode shapes analysis of a cracked beam and its application for crack detection

In this paper, mode shapes of a cracked beam with a rectangular cross section beam are analysed using finite element method. The 3D beam element is applied for this finite element analysis. The influence of the coupling mechanism between horizontal bending and vertical bending vibrations due to the crack on the mode shapes is investigated. Due to the coupling mechanism the mode shapes of a beam change from plane curves to space curves. Thus, the existence of the crack can be detected based on the mode shapes: when the mode shapes are space curves there is a crack in the beam. Also, when there is a crack, the mode shapes have distortions or sharp changes at the crack position. Thus, the position of the crack can be determined as a position at which the mode shapes exhibit such distortions or sharp changes. While in previous studies using 2D beam element, distortions in the mode shapes caused by a small crack could not be detected, these distortions in the case using the 3D beam element can be amplified and inspected clearly by using the projections of the mode shapes on appropriate planes. The quantitative analysis is also implemented to relate the size and position of the crack with the observed coupled modes. These results can be applied for crack detection of a beam. In this paper, the stiffness matrix of a cracked element obtained from fracture mechanics is presented and numerical simulations of three case studies are provided.     

高精度自动贴片机视觉对准系统及其图像处理

阐述了高精度自动贴片机视觉对准系统的构成和原理。介绍了利用模式识别理论和图像的不变矩实现定位标志存在性判断的原理及算法和定位标志对准的原理及相关的图像处理算法,以及其中的点模式匹配算法。试验结果表明,定位标志存在性判断算法可以有效地区分不同的定位标志和判断定位标志是否在视场之内;定位标志对准算法在输入图像旋转、平移、定位标志被部分遮挡时,能精确地得到定位标志的位置偏差。     

一种基于小波分析的唐卡图像划痕检测

唐卡图像在其保存和搬运过程中较易出现细微的划痕,针对所出现的垂直(水平)划痕进行了自动检测算法研究。垂直(水平)划痕实质上属于特殊的边缘,首先利用该特性采用小波模极大值描述图像中目标的多尺度边界;然后再通过投影变换放大划痕中心的亮度极值特性,并基于多尺度突变点检测找到划痕的位置;最终采用标注联通分量、设置高度和宽度约束得到划痕的掩膜。实验结果表明,利用该方法取得了较好的检测效果。     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

基于时空信息的红外成像系统在探测识别目标时所面临的问题及分析

具体分析了新型激光调制气溶胶红外诱饵对红外成像导引头的干扰。分析了EKV导引头在超远距离点目标的探测识别中,在应用基本识别方法识别时的失效性。归纳出了基于传统的空间和时间信息的红外成像系统在探测识别真假目标时所面临的问题,并对光谱探测识别的未来发展方向进行了初步探讨。     

利用几何特性及神经网络进行人脸探测技术的研究

在人脸识别过程中 ,首先也是最重要的一个环节是人脸探测 ,因为一旦从图像中定位并提取到了人脸 ,那么下一步的人脸识别工作就变得非常容易。眼睛是人脸图像中最容易探测的部位 ,而且通过探测双眼来发现人脸最符合人的视觉习惯。提出了一种基于几何特征分析和人工神经网络的由粗到细的两级人脸探测方法。在第一级中 ,眼睛和脸是通过测量眼睛的尺寸和眼睛与脸的位置关系探测到的 ,第一级的输出是一个尺寸归一化的人脸 ,但偶尔也伴随着一个或多个因对复杂背景中与眼睛类似的物体的误判而得到的非人脸图像 ;第二级神经网络正是用来过滤掉第一级中被误判的人脸。实验表明 ,这种由粗到细的两级人脸探测系统具有很高的稳定性和探测正确率     

A general strategy for anisotropic diffusion in MR image denoising and enhancement

Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.     

Crack-like defect detection and sizing from image segmentation through co-occurrence matrix analysis

The inspection of austenitic welds used in the nuclear industry using ultrasound poses problems in interpretation: strong grain noise makes the detection of the crack top and the crack bottom difficult. Since corresponding echoes enable defect sizing, defect sizing also becomes difficult. The formation of two-dimensional images (BSCAN) and their processing enable an increase in the effectiveness of testing. This paper presents a new segmentation method, based on the analysis of the co-occurrence matrix. A threshold is automatically calculated by the extremum of a curve, called the Average Grey-level Variance Measure. This curve is an analysis of the distribution of the matrix coefficients. Examples of segmentation improvement applied to BSCAN images of artificial defects are presented.     

Sensitive and automated detection of iron-oxide-labeled cells using phase image cross-correlation analysis

Superparamagnetic iron oxide (SPIO) nanoparticles are increasingly being used to noninvasively track cells, target specific molecules and monitor gene expression in vivo. Contrast changes that are subtle relative to intrinsic sources of contrast present a significant detection challenge. Here, we describe a postprocessing algorithm, called Phase map cross-correlation Detection and Quantification (PDQ), with the purpose of automating identification and quantification of localized accumulations of SPIO agents. The method is designed to sacrifice little flexibility - it works on previously acquired data and allows the use of conventional high-SNR pulse sequences with no extra scan time. We first investigated the theoretical detection limits of PDQ using a simulated dipole field. This method was then applied to three-dimensional (3D) MRI data sets of agarose gel containing isolated dipoles and ex vivo transplanted allogenic rat hearts infiltrated by numerous iron-oxide-labeled macrophages as a result of organ rejection. A simulated dipole field showed this method to be robust in very low signal-to-noise ratio images. Analysis of agarose gel and allogenic rat heart shows that this method can automatically identify and count dipoles while visualizing their biodistribution in 3D renderings. In the heart, this information was used to calculate a quantitative index that may indicate its degree of cellular infiltration.