A new inversion method for reconstruction of plasmaspheric He^(+)density from EUV images

The Computer Tomography(CT)method is used for remote sensing the Earth’s plasmasphere.One challenge for image reconstruction is insufficient projection data,mainly caused by limited projection angles.In this study,we apply the Algebraic Reconstruction Technique(ART)and the minimization of the image Total Variation(TV)method,with a combination of priori knowledge of north–south symmetry,to reconstruct plasmaspheric He+density from simulated EUV images.The results demonstrate that incorporating priori assumption can be particularly useful when the projection data is insufficient.This method has good performance even with a projection angle of less than 150 degrees.The method of our study is expected to have applications in the Soft X-ray Imager(SXI)reconstruction for the Solar wind–Magnetosphere–Ionosphere Link Explorer(SMILE)mission.

Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.

THE CALCULATION OF THE DENSITY FIELD FROM AXISYMMETRIC SCHLIEREN INTERFEROGRAMS BY THE IMAGE PROCESSING TECHNIQUES

The schlieren interferograms used to be analyzed in a qualitative way. In this paper, by means of the powerful computational ability and the large memory of computer; the image processing method is investigated for the digitalization of an axisymmetric schlieren interferogram and the determination of the density field. This method includes the 2-D low-pass filtering, the thinning of interferometric fringes, the extraction of physical information and the numerical integration of the density field. The image processing results show that the accuracy of the quantitative analysis of the schlieren interferogram can be improved and a lot of time can be saved in dealing with optical experimental results. Therefore, the algorithm used here is useful and efficient.

Automatic Determination of Yarn Hairiness Length Based on Image Processing and Analysis Algorithm

A new algorithm is proposed to determine the actual length and the number of the protruding fibres of yarn based on a combination of image acquisition technology. First, a yarn hairiness image is obtained by the series of image processing procedures that include grayscale transformation,skew correction,yarn binary image acquisition and yarn core binary image obtaining. Then,the hairiness is realized in single pixel width by the usage of thinning algorithm. Finally, a baseline of yarn core margin is obtained,and pixels that match 8-neighbor template correctly are found by row scanning in a certain area. From this,these pixels are judged and the real crossover points of yarn core margin and hairiness,i. e.,the starting points of hairiness,are gained. The real length of the protruding fibres is calculated by tracking hairiness from the starting point constantly.

LAND-COVER DENSITY-BASED APPROACH TO URBAN LAND USE MAPPING USING HIGH-RESOLUTION IMAGERY

Nowadays, remote sensing imagery, especially with its high spatialresolution, has become an indispensable tool to provide timely up-gradation of urban land use andland cover information, which is a prerequisite for proper urban planning and management. Thepossible method described in the present paper to obtain urban land use types is based on theprinciple that land use can be derived from the land cover existing in a neighborhood. Here, movingwindow is used to represent the spatial pattern of land cover within a neighborhood and seven windowsizes (61mx61m, 68mx68m, 75mx75m, 87mx87m, 99mx99m, 110mx110m and 121mxl21m) are applied todetermining the most proper window size. Then, the unsupervised method of ISODATA is employed toclassify the layered land cover density maps obtained by the moving window. The results of accuracyevaluation show that the window size of 99mx99m is proper to infer urban land use categories and theproposed method has produced a land use map with a total accuracy of 85%.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable.In this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures.Three datasets of volunteers from two clinical trials were included.Breast MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique.Two whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were developed.Manual segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual segmentation.The mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a reference.The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and ICC.The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),respectively.For test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation methods.In conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD.Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducible BD measurements.

AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS

In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.

A Novel Approach to Calculating Energy Density from Food Images Reduces Analysis Time and Cost

Traditional methods of self-reported food intake are characterized by limitations such as underreporting, high participant burden, and high cost. With the development of automated devices to capture food images and monitor food intake, an accurate and efficient method to estimate energy intake is needed. This study aimed to develop an accurate and time efficient method for estimating energy intake from food images by defining a simple and less burdensome way of estimating energy density (ED). Four experimental methods, exchange, food score-long, food score-short, and meal, were developed to estimate ED based on nutrient composition, water content, and relative proportion of foods in images, using different approaches. Three trained nutritionists analyzed 29 food images for ED using each method. All four experimental methods were compared to the full visual method in which a nutritionist estimated the portion size of each food consumed from dietary intake images and conducted data entry and analysis software. All experimental methods overestimated ED compared to the FVM but the meal method exhibited the closest agreement, lowest variance for ED, and significantly decreased analysis time by an average of 53 s/meal (p = 0.03). The meal method was used for full-scale validation by analyzing 213 food images against weighed food records. The meal method reduced analysis time by 69% (120 s;p ≤ 0.0001) and over-estimated ED by an average of 1.56 ± 3.17 J/g (p < 0.0001) compared to the FVM and 1.67 ± 3.09 J/g (p < 0.0001) compared to the WFR. The meal method is a novel and quick approach to calculate ED from dietary intake images.

New Contour Detection Model Working on Gray-Scale Image of Blended Yarn Cross-Section

The traditional Contour Tracing algorithm works on the binary image. It is developed that a new model called Facula Diffusion which can work directly on gray-scaled images according to the principle of human vision. The diffusion operation is controlled by four factors including approximation, closing, length-limiting, and hit-rate. Based on this model, three shape indices, i. e., dimension index, abnormity index, and fluctuation index, were put forward to describe the shape of objects. The rule of shape indices selection was discussed subsequently. Finally, the fibers in polyester/cotton blended yam are classified and the blending ratio is determined.

SVM for density estimation and application to medical image segmentation

A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

Adaptive Hybrid Bivariate Double Density Discrete and Complex Wavelet for Image Denoising

Image denoising is an important step in eliminating any noise impact in any image transmission process. Recently we presented two approaches for Bivariate based image denoising. They were Double Density Discrete Wavelet Transform (DD DWT) and Double Density Dual Tree Complex Wavelet Transform (DD CWT). In both techniques we decomposed noisy images with either DD DWT or DD CWT decompositions and then applied the Bivariate based denoising technique for noise removal. In this paper we propose an adaptive hybrid technique for Bivariate based image denoising that is based on the synthesis of DD-DWT bands or DD-CWT bands but with different weights, to deliver enhanced image features with less denoising impact especially around image edges, which is the most effected by noisy transmission channels. This proposed technique has been also enhanced by edge sharpening and Eigen analysis, as two separate stages. Simulation result comparisons have been performed between the proposed hybrid band adaptive DD-DWT and DD-CWT technique and the two primary techniques DD-DWT, DD- CWT, as well as other superior literature techniques such the original bivariate denoising technique with both original Complex Wavelet Transform and Double Density decompositions. This work in specific compares between Double Density DWT and Double Density CWT decompositions, proposes new filter design that suits each of them and proposes a hybrid technique between as will be shown.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Parametric shape prior model used in image segmentation

Due to the frequency of occlusion, cluttering and lowcontrast edges, gray intensity based active contour models oftenfail to segment meaningful objects. Prior shape information is usuallyutilized to segment desirable objects. A parametric shape priormodel is proposed. Firstly, principal component analysis is employedto train object shape and transformation is added to shaperepresentation. Then the energy function is constructed througha combination of shape prior energy, gray intensity energy andshape constraint energy of the kernel density function. The objectboundary extraction process is converted into the parameters solvingprocess of object shape. Besides, two new shape prior energyfunctions are defined when desirable objects are occluded by otherobjects or some parts of them are missing. Finally, an alternatingdecent iteration solving scheme is proposed for numerical implementation.Experiments on synthetic and real images demonstratethe robustness and accuracy of the proposed method.

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.

A New Method of Semantic Feature Extraction for Medical Images Data

In order to overcome the disadvantages of color, shape and texture-based features definition for medical images, this paper defines a new kind of semantic feature and its extraction algorithm. We firstly use kernel density estimation statistical model to describe the complicated medical image data, secondly, define some typical representative pixels of images as feature and finally, take hill-climbing strategy of Artificial Intelligence to extract those semantic features. Results of a content-based medial image retrieve system show that our semantic features have better distinguishing ability than those color, shape and texture-based features and can improve the ratios of recall and precision of this system smartly.

Online Investigation of Optical Properties and Craze Propagation of Drawn Polypropylene Fiber by Using Interferometric Image Analysis

Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, and then craze propagation was monitored as a function of drawing speed and test temperature. The effect of craze dimension on their propagation velocity was taken into account. Three-dimensional birefringence profile for crazed polypropylene fibre has been demonstrated to investigate the birefringence of crazed fibre at different test times for fixed drawing speed value. Also the mean birefringence values of crazed polypropylene fibres were calculated and the results showed that, these values increased with the areal craze density. Video images were used to calculate the craze velocity. Optical micrographs and microinterferograms were presented for demonstrations.

Measuring Structural Parameters of Knitted Fabrics by Digital Image Processing Techniques

In the knitting industry the measurements of the stitch density and the stitch length are usually done manually, which may lead to lower efficiency and less definition and also bring subjective ideas into the test results. In order to improve the effect we can measure with Digital Image Processing Techniques. A piece of sample is scanned into computer and changed into a digital image, which is processed with media filtering. To acquire the power spectrum, the image in the spatial domain is converted into the frequency domain. Picking up the characteristic points describing the stitch density and the stitch length separately in the power spectra and reconstructing them, the values of the stitch density and the stitch length could be calculated. When measuring the stitch length, we should establish a geometric model of the stitch based en the digital image processing, which provides a method to transform the stitch length in the two-dimensien space into the three-dimensien space and to measure the value of the stitch length more accurately. This method also provides a new way to measure the stitch length without damaging the fabric.

Irregular and regular LDPC codes with high spectrum efficiency modulation in image transmission over fading channel

If the degree distribution is chosen carefully, the irregular low-density parity-check (LDPC) codes can outperform the regular ones. An image transmission system is proposed by combining regular and irregular LDPC codes with 16QAM/64QAM modulation to improve both efficiency and reliability. Simulaton results show that LDPC codes are good coding schemes over fading channel in image communication with lower system complexity. More over, irregular codes can obtain a code gain of about 0.7 dB compared with regular ones when BER is 10 -4. So the irregular LDPC codes are more suitable for image transmission than the regular codes.

植物叶脉标本简单制作方法及基于Image J软件的叶脉图像分析

采用5%氢氧化钾沸水浴对12种植物叶片进行处理,结合超声波清洗使叶肉及表皮脱离,经漂白、染色制成叶脉标本。使用数码相机及显微镜对标本进行成像,利用Image J软件MINA工具宏的分析步骤对图像进行分析,测定12种植物叶脉的密度、分支密度、交点密度、端点密度,并通过手动追踪验证测量结果的准确性。结果表明,该方法可快速获得清晰完整的叶脉网络结构图,并且可以运用Image J软件MINA工具宏的分析步骤对叶脉进行分析。与传统的氢氧化钠组织透明法比较,该方法处理时间短,叶脉网络结构完整清晰,用Image J软件可准确测量叶脉密度。

Modeling yarn-level geometry from a single micro-image

Different types of cloth show distinctive appearances owing to their unique yarn-level geometrical details.Despite its importance in applications such as cloth rendering and simulation,capturing yarn-level geometry is nontrivial and requires special hardware,e.g.,computed tomography scanners,for conventional methods.In this paper,we propose a novel method that can produce the yarn-level geometry of real cloth using a single micro-image,captured by a consumer digital camera with a macro lens.Given a single input image,our method estimates the large-scale yarn geometry by image shading,and the fine-scale fiber details can be recovered via the proposed fiber tracing and generation algorithms.Experimental results indicate that our method can capture the detailed yarn-level geometry of a wide range of cloth and reproduce plausible cloth appearances.