A simple 2D composite image analysis technique for the crystal growth study of L‐ascorbic acid

This work was destined for 2D crystal growth studies of L‐ascorbic acid using the composite image analysis technique. Growth experiments on the L‐ascorbic acid crystals were carried out by standard (optical) microscopy, laser diffraction analysis, and composite image analysis. For image analysis, the growth of L‐ascorbic acid crystals was captured as digital 2D RGB images, which were then processed to composite images. After processing, the crystal boundaries emerged as white lines against the black (cancelled) background. The crystal boundaries were well differentiated by peaks in the intensity graphs generated for the composite images. The lengths of crystal boundaries measured from the intensity graphs of composite images were in good agreement (correlation coefficient “r” = 0.99) with the lengths measured by standard microscopy. On the contrary, the lengths measured by laser diffraction were poorly correlated with both techniques. Therefore, the composite image analysis can replace the standard microscopy technique for the crystal growth studies of L‐ascorbic acid.     

Wear particle analysis—utilization of quantitative computer image analysis: A review

This paper provides a general overview of developments and progress in quantitative computer image analysis as applied to wear particle identification/classification technology, over the last two decades. Since many technical disciplines are involved in this ‘infant-stage’ technical area, an attempt is made to put into perspective mechanical failure prediction/diagnosis and prevention through quantitative wear particle morphological analysis. The problems experienced with applying conventional wear particle analysis methods in machinery condition monitoring, notably the employment of wear debris morphological diagnostic systems, revealed that it is not prudent to rely solely on human interpretation in the analysis of ‘filtergram’ slides. This has highlighted the need for improving the provision of ‘intelligent’ objective methods for performing this type of analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image analysis are reported and examined as a basis for establishing improved methods of diagnostic analysis.     

An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates.     

图像处理技术在焊缝射线探伤中的应用

针对机械零件X射线图像的特点,对其缺陷的提取技术进行了研究,提出了基于迭代的阈值构造方法和基于数学形态学的边缘提取算法.针对在工作现场采集的射线探伤底片进行数字成像,结合图像的变换、中值滤波、阈值分割、边缘检测、特征提取、模式识别等算法,通过适当的处理和分析,对压力容器焊接缺陷进行自动检测与识别,最终实现了对焊接缺陷的机器自动识别.实践证明,该方法具有一定的快速性和实用性.     

Improved algorithms for automatic bone histomorphometry on a numerized image analysis system

Quantitative histological methods have proved to be the most effective methods in bone disease research. Faster and more accurate techniques are currently needed. We have developed a simple digitized image analysis system which allows accurate measurements of trabecular bone mass. The algorithm is based on the 'four-connected sets' mathematical theory. Given a numerized image displayed by a CCD camera, the algorithm recognizes all possible four-connected sets and provides area measurements. The first procedure automatically eliminates small, irrelevant profiles (wrinkles, cell nuclei, etc.) while larger profiles are erased interactively. The second procedure similarly erases the artefactual defects within the trabecule (artefactual cracks or empty osteocytic lacunae). The method was shown to be very accurate and time-saving.     

Infrared image system for fire location

This paper describes a new sensor and interpolation technique for localizing a fire in a closed environment or in a highway tunnel. When a smoke detecting device warns for a presence of a fire, two thermopile arrays images are elaborated, to localize the, eventual, fire and to control fire extinguisher jet. This system is a very promising for highway tunnel and also as substitute for common fire sprinkler systems in closed rooms, due to its cost and relatively simplicity of installation.     

Sizing of spray particles using image processing technique

The image processing technique is simple and, in principle, can handle particles with various shapes since it is based on direct visualization. Moreover, a wide measurement area can be covered with appropriate optical arrangement. In the present paper, various techniques of image processing for sizing and counting particles are reviewed and recent developments are introduced. Two major subjects are discussed in detail: identification of particles (i.e., boundary detection and pattern recognition) and determination of in-focus criteria. Finally, an overall procedure for image processing of spray particles is suggested.     

Multiscale image analysis of microcellular solids: application to hybrid silica xerogels

A general methodology is proposed to characterize microcellular solids, the structure of which consists of a three‐dimensional network of filamentary structures. The analysis is based on transmission electron microscopy observation of the filaments individually and of their spatial arrangement. The micrographs are analyzed with grey‐tone digital image analysis techniques, such as opening granulometry and correlation analysis. The methodology is applied to hybrid organic–inorganic low‐density silica solids synthesized by the sol–gel method with an organically modified co‐reactant. The quantitative impact of the co‐reactant on each structural level of the structure is assessed quantitatively.     

A model for logarithmic image processing

Up to now, image processing and image analysis techniques have borrowed their basic tools from functional analysis: Fourier filtering, differential and integral calculus, and so on. These tools, however, only realize their efficiency when they are put into a well-defined algebraic frame, most of the time of a vectorial nature. Unfortunately, the class of functions modelling ‘images’, commonly referred to as ‘grey tone functions’ does not necessarily present this very type of structure. We present here an operation for the ‘addition’ of two images, with a physical justification in the context of transmitted light. Such an addition permits the construction of the family of ‘positive homothetics' of the grey tone function at hand. The vectorial context sought is well defined: The class of images associated with the class of their grey tone functions naturally becomes the positive cone of an ordered real vector space. Furthermore, the proposed model holds for logarithmic imaging and is compatible with what is known about the human visual process. This model has been called ‘LIP’ (logarithmic image processing model).     

基于图像配准的产品外形缺陷自动检测与分析

提出一种基于图像配准方法的产品外形缺陷自动检测和分析方案,可以对产品外形轮廓的缺陷进行定量测定,并对缺陷发生部位给出统计数据,为生产工艺的分析和改进提供依据。以瓷砖产品为例,进行检测和分析实验,结果表明,提出的方法是有效的。     

A new method based on image analysis for determining cyanobacterial biomass by CLSM in stratified benthic sediments

Cyanobacteria are the dominant primary producers in microbial mats, which are stratified benthic microbial ecosystems found in coastal environments. Some cyanobacteria form long filaments, which make difficult to apply classical methods to estimate their biomass because they establish strong interactions with detritic particles. In a previous study, we described a method for determining cyanobacterial biomass by means of confocal laser scanning microscopy (CLSM). However, the manual method used, based on summa projection images, was difficult to apply when analyzing a large number of samples. In this paper, we described a new automated method, based on stacks and applying the plugin voxel counter in the ImageJ analysis system, more adequate for obtaining biomass data quickly from a large number of CLSM images.     

Frequency domain image analysis for the characterization of porous products

Many pharmaceutical products are obtained via freeze-drying of liquid solutions to obtain stable long lasting preparations. The freeze-dry process produces porous cakes whose structure strongly depends on the freezing phase, so that monitoring and optimizing this phase can help both reducing the product cost and insuring its constant quality. Nowadays the optimization is usually performed by determining the cake mass transfer coefficient via a costly process in pilot plants, while the quality is assured only by controlling the process conditions. This paper describes an alternative way of approximately estimating the mass transfer coefficient, which is based on the observation of the product structure by a simple electron microscope followed by a frequency domain imaging process. While the process has been designed and characterized specifically for pharmaceutical products, the proposed approach can be used in several other fields where the characteristics of porous material have to be monitored.     

Measurements of translational slug velocity and slug length using an image processing technique

Slug flow is a common flow regime that occurs in various industries, such as oil, gas, and power generation industries. In this study, the mean slug translational velocity and slug liquid length were measured using Phantom 9.2 software and an image processing analysis technique. The adopted image processing technique involved the analysis of video frames recorded from a high-speed camera (Phantom 9.2) in a horizontal transparent pipe using a combination of the approximate median method and blob analysis, along with an additional morphological process for detecting and segregating individual slugs. The experimental data were obtained from a designed two-phase flow test section, in which sets of superficial water and air velocities were selected to generate numerous slug flows. A good agreement with a maximum deviation of 6.7% between the estimated slug parameters from the adopted technique and the Phantom cine view controller software was achieved. Additionally, the developed technique provided precise results with a high processing speed of 10 frames per second.     

A simple method to 'point count' silt using scanning electron microscopy aided by image analysis

This study presents a simple method to ‘point count’ silt‐sized grains using backscattered scanning electron microscopy together with image analysis. The work materialized out of the need to determine the heavy mineral abundance within silt obtained from coastal dunes to aid in the interpretation of dune weathering. This technique allows two broad mineral groups to be quantified according to their modal abundance. The groups are characterized by their dominant atomic elements present; atomic numbers > 20 are classified as ‘high’ (metal oxides, zircon, monazite, carbonates, pyroxenes and amphiboles) and those < 20 as ‘low’ (quartz, feldspars and organics). As a check on this technique, X‐ray fluorescence was used. This showed a strong positive correlation (r² = 0.85) with the developed point counting technique.     

基于图像处理的视线方向跟踪研究

提出一种基于图像处理技术实时跟踪视线方向的方法,并介绍其原理和组成,以及图像处理技术的应用.通过对采集到的序列图像进行图像处理和分析,以虹膜和瞳孔中心位置的变化来计算视线方向.结果表明,该方法采用两次图像预处理过程,保证了头部变动时视线方向跟踪的准确性,有效提高处理效率,为今后研究视线识别技术打下了一定基础.     

Measurement and analysis of fluorescent whitening agent content in soybean milk based on image techniques

This paper presents a method for measuring and quantitatively analyzing the fluorescent whitening agent in soybean milk based on image techniques. After collecting the fluorescent images of the soybean milk samples, the top seven wavelet moment invariants are selected according to the sample training and experimental comparison. Then calculate the standard templates of the 49 classes of calibration samples with different fluorescent whitening agent content ranging from 0.02 mg/ml to 0.5 mg/ml. The minimum distance method is carried out to match the testing sample with the calibration template, which realizes the quantitative analysis. To verify the effectiveness of the presented method, the prediction experiment is carried out. Results show that the absolute errors are within 0.005 mg/ml and the relative errors are within 5%, which means this method can measure the fluorescent whitening agent in soybean milk. This research presents a new approach for detecting the illegal fluorescence additive in food production.     

Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

Texture analyses methods incorporating fractal analysis using box-counting, grey level co-occurrence matrix (GLCM) technique and run length statistical (RLS) analysis technique have been carried out to automatically characterize micrographs and fractographs of Cu-strengthened high-strength low alloy (HSLA) steel. The selected steel has been solution treated and water quenched followed by ageing at different temperatures. Tensile tests have been carried out for water quenched and aged specimens. Microstructural and fractographic analyses are made using scanning electron microscope. Ten texture features from the three texture analyses methods are extracted to correlate with the observed mechanical properties. The obtained image texture features of both micrographs and fractographs have been compared with the estimated mechanical properties. The results obtained from the investigation reveal that there is a systematic correlation of the mechanical properties and image texture features with ageing temperatures.     

A new approach for image stitching technique using Dynamic Time Warping (DTW) algorithm towards scoliosis X-ray diagnosis

Consider a set of images of a single object, or scenery, taken from different viewpoints and time. Panorama image creation is the process of stitching such images into a single coordinate system to generate a wider viewing panoramic image. Image stitching consists of two processes which are image registration and image blending. In image registration, parts of two overlapping or consecutive images are considered to find an appropriate merging position and transformation to combine the images. In image blending, the intensities of pixels along the stitching line are modified so that they flow naturally without any noticeable break. In this paper, we propose a novel method that utilizes the Dynamic Time Warping (DTW) algorithm to match pairs of images for image stitching. We also perform a dimension reduction scheme that significantly reduces the computational complexity of the standard DTW without affecting its performance. The effectiveness of the proposed method is demonstrated in stitching 50 pairs of medical X-ray images and its performance is compared to those of normalized cross correlation (NCC), Minimum Average Correlation Energy (MACE) filters, sum-of-square-differences (SSD) and sum-of-absolute-differences (SAD). For the database used, the dimensionally reduced DTW outperforms the NCC, MACE, SSD and SAD methods in accuracy and average execution time. The method also outperforms two widely used stitching programs available on the internet called Hugin and Autostitch.