基于热图像的种蛋气室变化监测算法

种蛋气室的大小是监测种蛋孵化过程的重要指标之一。根据种蛋的热力学结构,种蛋在孵化过程中,包裹气室部分蛋壳会与其他部分蛋壳产生温差,从而可通过热红外图像进行观察。针对在种蛋孵化过程中,人工照蛋检测气室效率低的问题,探索设计了一种基于热图像的种蛋气室变化俯视监测算法。监测种蛋气室热图像的算法主要包括种蛋目标检测,种蛋图像分割和种蛋气室面积计算3个部分,其中种蛋的目标检测采用Faster-RCNN算法实现;种蛋图像分割采用BP神经网络算法实现;种蛋气室面积是在种蛋图像分割的基础上进行计算。使用孵化5天及以上的种蛋作为研究对象,并拍取种蛋的热图像进行试验。试验结果表明：种蛋热图像的目标检测的平均精度（mAP）为99.85%,拥有较好的检测效果。使用BP网络对种蛋进行图像分割。BP神经网络经过调参后,其网络最佳的结构为三层隐藏层,每个隐藏层拥有1 000个神经元,最优初始学习率为0.000 1,最优最大迭代次数为500。以F1-measure作为分割效果的评价指标,BP神经网络的图像分割总体结果为87.02%,Otsu算法的总体结果为65.25%。其中只有一个蛋的情况下,BP神经网络的分割结果为87.17%,Otsu算法的结果为68.86%。存在其他种蛋的干扰条件下,BP神经网络的分割结果为86.94%,Otsu算法的结果为61.64%,BP神经网络的分割效果优于Otsu分割算法,BP神经网络拥有更强的抗干扰能力。最后提取了孵化5～19 d种蛋的气室变化,通过观察种蛋气室大小曲线来监测种蛋的孵化情况,可看出随着天数的增加,气室有着明显变大的趋势。人工测量法与热红外测量法比较结果说明两者相关性为0.934 3,拥有较好的相关性。基于热图像的种蛋气室变化监测算法可在实际生产中实现种蛋的识别与气室大小的快速监测,为实现监测种蛋孵化的自动化提供了技术参考。     

基于模糊逻辑的红外相机模拟器定量化仿真度评价框架

针对红外相机模拟器仿真度量化评价体系的现状,提出了一种基于模糊逻辑和模糊集思想的全新的理论框架.该框架是一种客观的、多尺度的评价体系,涉及到模糊逻辑、曲线相似度评价、图像质量评估以及信息论等多个交叉领域.和现有的单一尺度的评价方法相比,这种多尺度的仿真度评价分析系统综合考虑了成像系统的主要性能特性参量和系统输出图像的质量,具有更好的任务适应性,可以方便地量化和比较具有相同或相似需求的相机模拟器的仿真度结果.     

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.     

Orientationally polarized dependence of image contrast in doped lithium niobate crystals

In lithium niobate crystals doped with photorefractive iron and copper admixtures when lightening objects with broadband incoherent radiation one can observe typical orientation and polarized dependences of image contrast. The record does not realize at orientation of light bands in the image parallel to the polar axis of the crystal.     

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Most of the techniques developed for infrared (IR) image enhancement (IE) depend heavily on the scene, environmental conditions, and the properties of the imaging system. So, with a set of predefined scenario properties, a content-based IR-IE technique can be developed for better situational awareness. This study proposes an adaptive IR-IE technique based on clustering of wavelet coefficients of an image for sea surveillance systems. Discrete wavelet transform (DWT) of an image is computed and feature vectors are constructed from subband images. Clustering operation is applied to group similar feature vectors that belong to different scene components such as target or background. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices which are used to multiply wavelet coefficients for the enhancement of the original image. Enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.     

Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules

Infrared polarization and intensity imagery provide complementary and discriminative information in image understanding and interpretation. In this paper, a novel fusion method is proposed by effectively merging the information with various combination rules. It makes use of both low-frequency and high-frequency images components from support value transform (SVT), and applies fuzzy logic in the combination process. Images (both infrared polarization and intensity images) to be fused are firstly decomposed into low-frequency component images and support value image sequences by the SVT. Then the low-frequency component images are combined using a fuzzy combination rule blending three sub-combination methods of (1) region feature maximum, (2) region feature weighting average, and (3) pixel value maximum; and the support value image sequences are merged using a fuzzy combination rule fusing two sub-combination methods of (1) pixel energy maximum and (2) region feature weighting. With the variables of two newly defined features, i.e. the low-frequency difference feature for low-frequency component images and the support-value difference feature for support value image sequences, trapezoidal membership functions are proposed and developed in tuning the fuzzy fusion process. Finally the fused image is obtained by inverse SVT operations. Experimental results of visual inspection and quantitative evaluation both indicate the superiority of the proposed method to its counterparts in image fusion of infrared polarization and intensity images.     

Non-contact evaluation of the resonant frequency of a microstructure using ultrasonic wave

This paper presents a novel non-contact method for evaluating the resonant frequency of a microstructure, Firstly, the microstructure under test is excited by ultrasonic waves. This excitation method does not impose any undefined load on the specimen like the electrostatic excitation and also this is the first actual use of ultrasonic wave for exciting a microstructure in the literature. Secondly, the amplitudes of the microstructure are determined by image edge detection using a Mexican hat wavelet transform on the vibrating images of the microstructure. The vibrating images are captured by a CCD camera when the microstructure is vibrated by ultrasonic waves at a series of discrete high frequencies （〉30 kHz）. Upon processing the vibrating images, the amplitudes at various excitation frequencies are obtained and an amplitude-frequency spectrum is obtained from which the resonant frequency is subsequently evaluated. A micro silicon structure consisting of a perforated plate （192 × 192 μm） and two cantilever beams （76 × 43 μm） which is about 4 μm thickness is tested. Since laser interferometry is not required, thermal effects on a test object can be avoided. Hence, the setup is relatively simple. Results show that the proposed method is a simple and effective approach for evaluating the dynamic characteristics of microstructures.     

“天光一号”平滑化角多路系统的建立

 采用诱导空间非相干（FEISI）的平滑方法结合传递傅里叶面的技术，建立了一套平滑化六束角多路系统。该系统输出总能量158 J，能量稳定度约4％，输出脉冲宽度25 ns，靶面有效焦斑直径400 μm，焦斑均匀性1.6％，激光功率密度3.7×10¹² W/cm²。     

噪音对光学图象浑沌相位列阵加密和解密的影响

本文提出了一种用浑沌相位列阵对光学图象加密和解密的方法,并重点分析了加法高斯白噪音对光学图象浑沌相位列阵加密和解密的影响.通过计算机模拟发现加密后的图象与原图象相比,抗振幅噪音的能力增强,而抗相位噪音的能力下降.在一定信噪比范围内,相位噪音是影响解密图象质量的主要因素.     

Improving motion estimation by accounting for local image distortion

Cardiac elastography is a useful diagnostic technique for detection of heart function abnormalities, based on analysis of echocardiograms. The analysis of the regional heart motion allows assessing the extent of myocardial ischemia and infarction. In this paper, a new two-stage algorithm for cardiac motion estimation is proposed, where the data is taken from a sequence of 2D echocardiograms. The method combines the advantages of block-matching and optical flow techniques. The first stage employs a standard block-matching algorithm (sum of absolute differences) to provide a displacement estimate with accuracy of up to one pixel. At the second stage, this estimate is corrected by estimating the parameters of a local image transform within a test window. The parameters of the image transform are estimated in the least-square sense. In order to account for typical heart motions, like contraction/expansion, translation and rotation, a local affine model is assumed within the test window. The accuracy of the new algorithm is evaluated using a sequence of 500 grayscale B-mode images, which are generated as distorted, but known copies of an original ROI, taken from a real echocardiogram. The accuracy of the motion estimation is expressed in terms of errors: maximum absolute error, root-mean-square error, average error and standard deviation. The errors of the proposed algorithm are compared with these of the known block-matching technique with cross-correlation and interpolation in the sub-pixel space. Statistical analysis of the errors shows that the proposed algorithm provides more accurate estimates of the heart motion than the cross-correlation technique with interpolation in the sub-pixel space.