Experimental study of a textural transition in liquid crystals under shear stress induced by surface friction

Results of a study of the optical response of liquid crystals with helical supramolecular structure to surfacefriction-induced shear stress in aerodynamic experiments are reported. The effect of re-orientation of molecules from confocal (light-scattering) to planar (selectively reflecting light) texture is considered. The textural transition is examined by the spectral and colorimetric methods. Dependences of the reflection spectra of the formed planar texture on time and on the mean shear-stress level were obtained. It is shown that the dependence of the texture transition time on the shear-stress level can be approximated, in a logarithmic scale, with a linear dependence; this allows using the effect for flow diagnostics.     

MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.     

Saliency detection via textural contrast

We present a new approach for visual saliency detection from various natural images. It is inspired by our careful observation that the human visual system (HVS) responds sensitively and quickly to high textural contrast, derived from the discriminative directional pattern from its surroundings as well as the noticeable luminance difference, for understanding a given scene. By formulating such textural contrast within a multiscale framework, we construct a more reliable saliency map even without color information when compared to most previous approaches still suffering from the complex and cluttered background. The proposed method has been extensively tested on a wide range of natural images, and experimental results show that the proposed scheme is effective in detecting visual saliency compared to various state-of-the-art methods.     

Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images

In this study, a pattern recognition system has been developed for the discrimination of multiple sclerosis (MS) from cerebral microangiopathy (CM) lesions based on computer-assisted texture analysis of magnetic resonance images. Twenty-three textural features were calculated from MS and CM regions of interest, delineated by experienced radiologists on fluid attenuated inversion recovery images and obtained from 11 patients diagnosed with clinically definite MS and from 18 patients diagnosed with clinically definite CM. The probabilistic neural network classifier was used to construct the proposed pattern recognition system and the generalization of the system to unseen data was evaluated using an external cross validation process. According to the findings of the present study, statistically significant differences exist in the values of the textural features between CM and MS: MS regions were darker, of higher contrast, less homogeneous and rougher as compared to CM.     

Simultaneous species and sex identification of silkworm pupae using hyperspectral imaging technology

To obtain high-quality raw silk and improve the economic values of sericulture industry, sex needs to be discriminated first before cross-breeding. Much work has been reported about sex identification. However, to realize automatic separation of silkworm pupae, the species also needs to be classified, which no research has ever explored. Hence, this paper studied the feasibility of visible and near-infrared hyperspectral imaging technology to identify the species and sex of silkworm pupae. 288 hyperspectral images of silkworm pupae were collected and the average spectra were extracted from the region of interest, around the tail region of silkworm pupae. Successive projection algorithm was served as a variable selection method to choose the optimal wavelengths from the full spectra. At the same time, principal component analysis was used to choose the characteristic images. Then, the gray-level co-occurrence matrix was implemented on the first three principal component images (accounted for 99.05% of the total variances) to extract 48 textural features. Partial least squares discriminant analysis and support vector machine models were built, respectively, based on the spectral data, textural data and fusion data that included spectral and textural data, in which the support vector machine model based on the fusion data, gave the best species and sex identification result with an accuracy of 95.83%. It demonstrated that the hyperspectral imaging technology could be a new and nondestructive method to replace the manual work.     

Focusing crystal mirrors for X-ray diagnostics of laser-produced plasma

The paper describes an investigation of the characteristics of spherical focusing Bragg-reflection mirrors. The integral reflectivity of bent crystals at Bragg angles 80–90°, the width of the spectrum of the reflected x-rays, and the spatial resolution of the images formed by the focusing crystals are considered. Results are presented of the calculated integral reflectivity of bent silicon and quartz crystals of different orientation and in different orders of reflection. The feasibility is demonstrated of laser-plasma diagnostics using the images obtained with the aid of a multichannel x-ray microscope based on focusing crystals. Possible schemes of active diagnostics of a dense laser plasma using a monochromatic transilluminating x-ray beam are considered.Laser Plasma Laboratory, Lebedev Physics Institute. Translated from FIAN Preprint No. 143, Lebedev Physics Institute of the Academy of Sciences of the USSR, Moscow, 1989.     

高光谱成像技术对鲜枣内外部品质检测的研究

外部缺陷以及内部可溶性固形物的含量对提升鲜枣的采后附加值和鲜枣后续生产加工具有重要的意义,因此,为了实现同时对鲜枣内外部品质进行快速、准确识别,利用高光谱成像技术(450-1,000 nm)对壶瓶枣的“自然损伤”和可溶性固形物含量同时进行检测研究。首先,对光谱数据进行主成分分析(PCA)得到前7个主成分光谱值,对图像数据采用灰度共生矩阵(GLCM)提取到7项图像纹理指标(对比度、相关性、能量、同质性、方差、均值、熵)。然后,分别使用光谱主成分值、图像纹理特征值、以及主成分与纹理特征融合值建立偏最小二乘支持向量机(LS-SVM)模型对壶瓶枣的外部缺陷(“自然损伤”)和内部品质(可溶性固形物含量)进行检测研究。结果表明：使用主成分与纹理特征融合值建立的LS-SVM模型可作为通用模型同时对壶瓶枣内外部品质进行检测研究,其“自然损伤”判别正确率为92.5%,可溶性固形物预测集的预测相关系数(R_p)和预测均方根误差(RMSEP)分别达到了0.944和0.495。表明,采用高光谱成像技术可以建立通用模型同时对壶瓶枣的内外部品质进行检测,该研究为壶瓶枣的无损检测提供了理论参考。     

Polarization-correlation analysis of anisotropic structures in bone tissue for the diagnostics of pathological changes

A method for the correlation analysis of polarization-filtered laser images of bone tissue is considered. Its ability to visualize the bone-tissue multifractal network in its normal and pathological states is analyzed. A set of criteria for the optical diagnostics of osteoporosis is determined.     

Geant4在惯性约束聚变内爆物理中的应用

基于Geant4建立了内爆物理实验多种诊断设备的全三维仿真模型,包括中子成像系统、用于硬X射线诊断的滤波应光谱仪、楔形滤片质子能谱仪、化学气相沉积金刚石探测器(CVD)等。根据Geant4仿真模拟的数据,建立了合适的解谱及图像还原算法,优化了系统设计,从理论上预估了诊断设备的诊断能力。此外仿真数据亦用于设计实验方案,建立诊断设备的标定方法等。     

基于高光谱成像技术应用光谱及纹理特征识别柑橘黄龙病

讨论了基于高光谱成像技术光谱及纹理特征在识别早期柑橘黄龙病中的应用。使用一套近地高光谱成像系统采集了176枚柑橘叶片的高光谱图像作为实验样品,其中健康叶片60枚,黄龙病叶片60枚,缺锌叶片56枚。手工选取每幅叶片高光谱图像的病斑位置作为样品感兴趣区域(regions of interest, ROI),计算其平均光谱反射率,并以此作为样品的反射光谱,光谱范围为396～1 010 nm。样品光谱分别经过主成分分析(PCA)及连续投影算法(SPA)进行数据降维,再结合最小二乘支持向量机(LS-SVM)分类器建立分类模型。相比原始光谱,由PCA选取的前四个主成分及SPA选取的一组最佳波长组合(630.4,679.4,749.4和899.9 nm)建立的模型拥有更好的分类识别能力,其对三类柑橘叶片平均预测准确率分别为89.7%和87.4%。同时,从被选四个波长的每幅灰度图像中提取6个灰度直方图的纹理特征以及9个灰度共生矩阵的纹理特征再次构建分类模型。经SPA优选的10个纹理特征值进一步提高了分类效果,对三类柑橘叶片的识别正确率达到了100%,93.3%和92.9%。实验结果表明,同时包含光谱信息及空间纹理信息的高光谱图像在柑橘黄龙病的识别中显示了很大的潜力。     

基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法

为了保持高岛分辨率合成孔径雷达(SAR)图像中的纹理结构,提出了一种基于BivaShrink模型的Contourlet 域SAR图像相干斑噪声抑制算法.联合当前层和父层的Contourlet系数,通过计算局部方差一致性范数和区域能量比,自适应地确定方差估计区域的形状和大小.从而对原始图像方差进行最优估计.实验结果表明,算法在噪声的去除和结构信息等细节的保持上均不同程度的优于小波BivaShrink去噪算法和Contourlet 阈值去噪算法,主观效果和数值指标都有较好改进.     

Correlation processing and wavelet analysis of polarization images of biological tissues

Methods of polarization filtering, correlation processing, and wavelet analysis of coherent images of physiologically normal and necrotically changed (myocardium infarct) muscular tissues are considered. A technique of early optical diagnostics of the appearance of these biological objects and the course of their degenerative-dystrophic changes is proposed.     

Thermal feature extraction of servers in a datacenter using thermal image registration

Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.     

辐射法制备Pd掺杂碳气凝胶粉末

利用辐射还原法,在100, 200, 500 kGy辐射剂量下制备了金属Pd掺杂的碳气凝胶粉末。X射线衍射（XRD）,扫描电子显微镜（SEM）和能谱测试证实了辐射法成功地制备出Pd掺杂的碳气凝胶粉末复合物。SEM照片表明,还原生成的金属Pd相对均匀地分布在所有碳气凝胶颗粒表面。N2吸附数据分析表明:掺入金属Pd后,碳气凝胶粉末比表面积、平均孔径和总孔体积都显著减小。由于被还原金属大多沉积在碳气凝胶粉末表面,不同辐射剂量下制得的掺杂碳气凝胶粉末的比表面积等多孔特征数据相差不大。     

一种基于中性集和均值漂移的彩色遥感图像非监督建筑物提取方法

建筑物的纹理和光谱信息的多样性一直是自动化识别的瓶颈。针对此问题,提出了一种彩色遥感图像建筑物提取方法,该算法结合中性集和均值漂移,对转换到中性集空间的影像进行均值漂移分割,生成以影像中主要地物类型为核心的光谱类别图像,提取建筑物。通过中性集空间的增强及分割,克服了传统均值漂移分割稳定性低、光谱不连续及信息混杂的缺陷,避免了地物识别前提取连通区等操作。实验证明,提出的算法可以简捷、完整、准确、稳定地提取建筑物,满足高分辨率遥感影像建筑物的提取要求。     

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards automatic detection and interpretation of disease symptoms on processed US images. First, in this paper, there is presented a new heuristic method of filtering gallbladder contours from images. A major stage in this filtration is to segment and section off areas occupied by the said organ. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours, based on rank filtration, as well as on the analysis of line profile sections on tested organs. The second part concerns detecting the most important lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. The methodology of computer analysis of US gallbladder images presented here is clearly utilitarian in nature and after standardising can be used as a technique for supporting the diagnostics of selected gallbladder disorders using the images of this organ.     

数字式X射线辐射扫描成像系统及其应用

数字化图像具有非常突出的优点和广泛的应用,而Ｘ射线成像在医学诊断和工业无损检测等领域占据非常重要的地位,因此Ｘ射线成像的数字化必将成为一种趋势。文章介绍了数字式Ｘ射线辐射扫描成像的特点、原理及其应用前景。采用气体电离室探测器阵列完成了３２路数字式Ｘ射线辐射扫描成像装置系统,其空间分辨率达到０．５ｍｍ,并且获得了比较满意的Ｘ射线图像效果。     

Shadowgrams of a dense micro-capillary plasma obtained with atable-top soft X-ray laser

A sequence of high resolution shadowgrams that map the evolution of the plasma of a 380 μm diameter microcapillary discharge was obtained using a very compact 46.9 nm laser. These images are the first plasma diagnostics data obtained using a table-top soft X-ray laser     

Imaging of flames and cold flows in air by diffraction from a laser-induced grating

Nonresonant laser-induced gratings are created in gases employing the second-harmonic output of a Nd: YAG laser in a degenerate four-wave mixing beam geometry. The diffraction efficiency of the gratings has been investigated as a function of laser intensity and gas pressure. Single-shot images of a helium flow in ambient air illustrate that diffraction of light from a laser-induced grating has the potential for remote, two-dimensional diagnostics of gas mixing processes. In addition, this coherent technique is used to image a sooty flame.     

Flow measurements based on Rayleigh scattering and Fabry-Perot interferometer

Flow diagnostics based on molecular Rayleigh scattering are discussed along with the results of a feasibility study to non-intrusively measure flow properties in a small supersonic wind tunnel. The technique uses an injection seeded, frequency doubled Nd:YAG laser tuned to an absorption band of iodine. The molecular Rayleigh scattered light is filtered with an iodine cell to block light at the laser frequency. The Doppler-shifted Rayleigh scattered light that passes through the iodine cell is analyzed with a planar mirror Fabry-Perot interferometer used in a static imaging mode. An intensified CCD camera is used to record the images. The images are analyzed at several subregions, where the flow properties are determined. Each image is obtained with a single laser pulse, giving instantaneous measurements.