Optical Image Processing Research Articles

A COMPARATIVE STUDY OF MONITORING METHODS IN SUSTAINABLE PAVEMENT SYSTEM DEVELOPMENT

Pavement management system (PMS) has been receiving increasing attention from both the government and private sectors in the attempt to ensure and keep the roads in good condition. The appropriate level of road maintenance activity is often contingent upon the type of pavement distress. Valid and reliable pavement data would lead to develop a PMS which is more suitable for agencies. Previous studies which attempted to identify modes of monitoring pavements were limited by constraints such as cost, time, and safety. This study was conducted to review some of the pavement monitoring modes introduced in previous studies. After completing a literature review, three mostly used modes, namely manual survey, smart sensor, and optical image processing, are selected for a comparative study to determine which mode is the most effective method in terms of cost, time, safety, accuracy, and sustainability. A data quality guideline was modified to produce a rating system for ranking the modes. In conclusion, the findings of this study could provide a guideline for the government and private sectors in determining the most effective pavement monitoring mode to be used in the sustainable PMS strategy.

Simulation of Moon-based Earth observation optical image processing methods for global change study

Global change affected by multiple factors, the consequences of which continue to be far-reaching, has the characteristics of large spatial scale and long-time scale. The demand for Earth observation technology has been increasing for large-scale simultaneous observations and stable global observation over the long-term. A Moon-based observation platform, which uses sensors on the nearside lunar surface, is considered a reasonable solution. However, owing to a lack of appropriate processing methods for optical sensor data, global change study using this platform is not sufficient. This paper proposes two optical sensor imaging processing methods for the Moon-based platform: area imaging processing method (AIPM) and global imaging processing method (GIPM), primarily considering global change characteristics, optical sensor performance, and motion law of the Moon-based platform. First, the study proposes a simulation theory which includes the construction of a Moon-Sun elevation angle model and a global image mosaicking method. Then, coverage images of both image processing methods are simulated, and their features are quantitatively analyzed. Finally, potential applications are discussed. Results show that AIPM, whose coverage is mainly affected by lunar revolution, is approximately between 0% and 50% with a period of 29.5 days, which can help the study of large-scale instant change phenomena. GIPM, whose coverage is affected by Earth revolution, is conducive to the study of long term global-scale phenomena because of its sustained stable observation from 67°N-67°S on the Earth. AIPM and GIPM have great advantages in Earth observation of tripolar regions. The existence of top of the atmosphere (TOA) albedo balance line is verified from the GIPM perspective. These two imaging methods play a significant role in linking observations acquired from the Moon-based platform to Earth large-scale geoscience phenomena, and thus lay a foundation for using this platform to capture global environmental changes and new discoveries.

Zernike functions, rigged Hilbert spaces, and potential applications

We revise the symmetries of the Zernike polynomials that determine the Lie algebra su(1, 1) ⊕ su(1, 1). We show how they induce discrete as well as continuous bases that coexist in the framework of rigged Hilbert spaces. We also discuss some other interesting properties of Zernike polynomials and Zernike functions. One of the areas of interest of Zernike functions has been their applications in optics. Here, we suggest that operators on the spaces of Zernike functions may play a role in optical image processing.

Complex approach for monitoring the parameters of the wire

The paper briefly describes the diffraction method for measuring the diameter of long-length products such as metal wire, optical fiber, chemical fiber, metal ingots or nonmetal. A method and optical device for monitoring the diameter and surface temperature of such products are proposed. The main advantage of the proposed method is to monitoring the parameters of the heated product even on the defocused image. In this paper we propose a set of methods that can improve the accuracy of measuring the diameter and temperature of such products. The methods are based on optical phenomena and digital image processing methods.

Imaging vitreous floaters and cataracts with optical simulations

This study proposes a practical method for modeling floating images in a human eye with vitreous floaters by using optical software and image processing. A floating image is an overlapping image of vitreous floaters inside the eye and an object outside. Firstly, the diffraction pattern from a small floater ball was studied. To model the images caused by floater groups, such as threadlike strands and cataract-induced floaters, a nonsequential software and Mie scattering theory were applied. On the basis of our simulation, an artificial reduced eye having floater-like micro-structures is utilized to show different floating images. The main benefit of this modeling method is the ability to assist quantitatively identifying the vision deficiency induced by floaters.

Hybrid Algorithm for Twin Image Removal in Optical Scanning Holography

Optical scanning holography is an incoherent optical image processing system. It is a technique, where the complete information of an object or image will be recorded as a hologram and later recons...

Polarization and hyperspectral imaging matter for newly emerging perspectives in optical image processing: guest editorial

In this note, we briefly discuss the opportunities to use polarized light and hyperspectral imaging as additional degrees of freedom in optical polarimetric image processing. The additional polarization and spectral information in recognition technologies allow them to identify visually indistinguishable features in a scene within a large region of interest.

Twin image reduction method for digital in-line holography by using a digital periphery

Measuring the complex field of objects with a single intensity recording is desirable. Obviously, digital in-line holography can realize it with a compact and less expensive method. However, the existence of the twin image degrades the imaging quality seriously. Here, we propose a method that can eliminate the twin image and improve the performance of digital in-line holography by employing a digital periphery. In the proposed method, the periphery is obtained by introducing a diffuser in the optical system and digital image processing technology. And the twin image is removed by the phase retrieval algorithm which uses the periphery as a boundary condition. The method can realize quantitative imaging complex samples in real-time with a compact optical system. Results of numerical simulations are presented, which validate satisfactory performances of the proposed method.

Optimization of stereoscopic imager performance by computer simulation of geometrical calibration using optical design software.

Stereoscopic imagers are widely used in machine vision for three-dimensional (3D) visualization as well as in non-destructive testing for quantitative characterization of cracks, delamination and other defects. Measurement capability in these systems is provided by a proper combination of the optical parameters and data processing techniques. Conventional approach to their design consists of two sequential stages: optical system design and optimization of calibration and image processing algorithms. Such two-stage procedure often complicates both the hardware and the software, and results in a time-ineffective design procedure and cost-ineffective solution. We demonstrate a more effective approach and propose to estimate errors of 3D measurements at the early optical design stage. We show that computer simulation using optical design software allows not only optimizing optical parameters of the imager but also choosing the most effective mathematical model of the system and the equipment necessary for calibration. We tested the proposed approach on the design of miniature prism-based stereoscopic system and analyzed the impact of various factors (aberrations, tolerances, etc.) as on the image quality, so on the quality of calibration and 3D measurements accuracy. The proposed joint design approach may be highly effective for various measurement systems and applications when both optical parameters and image processing algorithms are not defined in advance and are necessary to be optimized.

Adsorption Performance of Activated-Carbon-Loaded Nonwoven Filters Used in Filtering Facepiece Respirators.

Filtering nonwovens loaded with activated carbon are among the most popular materials used in the construction of filtering facepiece respirators (FFRs) with anti-odour properties that can be used for respiratory protection at workplaces where the occupational exposure limits of harmful substances are not exceeded. Such FFRs, in addition to a polymer filter material of varying effectiveness, also contain a layer of activated-carbon-loaded nonwoven filter, which limits the quantity of chemical compounds entering the breathing zone. The aim of this work was to analyse the influence of challenge concentration (20–120 ppm), relative humidity (2–70%), flow rate (20–55 L/min), and flow pattern (steady-state and pulsating) on the breakthrough of polymer/carbon nonwovens. A commercial activated-carbon-loaded nonwoven filter was used in this study. Its morphology and textural parameters were determined using optical microscopy, image processing, and nitrogen adsorption/desorption measurements at 77 K. Breakthrough experiments were carried out using cyclohexane vapours to assess adsorption characteristics of polymer/carbon media. The results showed that the breakthrough times decreased with increasing challenge concentration (up to 30%), relative humidity (up to 73%), and flow rate (up to 72%). The pulsating flow pattern was found to be more favourable in terms of odour reduction efficiency (up to 30%). The results indicate that all of these factors should be considered during selection and performance assessment of respirators used for odour relief.

Application of surface replication combined with image analysis to investigate wear evolution on gear teeth – A case study

Gear wear is a critical degradation process in many applications and its monitoring is important to ensure safe operation of machines. The case study presents a novel approach to obtain comprehensive and high-resolution wear information for monitoring the evolution of gear wear. This is achieved through the combination of a moulding method and non-contact imaging-based measurements in two- and three-dimensions and without gearbox disassembly and tooth removal. Optical and laser scanning confocal microscopy and image processing tools were employed to provide quantification of surface roughness, wear severity and wear depth on gear teeth during a lubricated endurance test. Also, a detailed qualitative analysis on the progression of the two main wear mechanisms (abrasive wear and fatigue pitting) was provided, alongside an evaluation of the diagnostic capabilities offered by the proposed methodology. The insights on gear wear offered in this case study also represent a valuable guideline for researchers and engineers in the field of gear condition monitoring. Moreover, the proposed wear analysis technique can be used to characterise and understand wear phenomena in other conditions and geared transmissions.

Differentiation of Pigmented Skin Lesions Based on Digital Processing of Optical Images

A method for differentiation of pigmented skin lesions is proposed that is based on digital processing of optical images. The optical images are detected using a digital camera and a standard for its color and spatial calibration. The method involves segmentation and automatic recognition of neoplasms on the basis of five parameters: diameter, area, color, shape, and border sharpness. The analysis reveals clinical features of the lesion, required for diagnosis, and allows for calculating the probability of its malignant transformation. The method proposed was tested on 360 digital images of in vivo skin pigment lesions, including ordinary dysplastic nevi and melanoma. The sensitivity and specificity of the method proved to be 97 and 95%, respectively.

A comparative study of monitoring methods in sustainable pavement management system

Pavement management systems (PMS) have been receiving increasing attention in the attempt to ensure that roads are always in good condition and serving its purpose. Appropriate PMS must be implemented to gather valid and reliable pavement data. Previous studies which attempted to identify methods for monitoring pavements were limited by constraints such as cost, time, safety, data accuracy and sustainability. This study was conducted to review some of the pavement monitoring models introduced in previous studies. After completing a literature review, three models, i.e. manual survey, smart sensor, and optical image processing, were selected for a comparative study to determine which of these models is the most effective model in terms of cost, time, safety, accuracy and sustainability. A data quality guideline was modified to produce a rating system for ranking the models from the worst to the best. The results of this study shows that smart sensor has the highest score in the radar chart, and hence is the best model. In conclusion, the findings of this study could provide a guideline for the government and private sectors in determining the most effective pavement monitoring model to be used in their sustainable PMS strategy despite the constraining factors.

Mapping subsurface tile drainage systems with thermal images

In Midwestern agricultural fields, subsurface tile drainage has been widely used to remove excess water from the soil through perforated tubes installed beneath the ground surface. While it plays an important role in enabling agricultural activities in wet but productive areas, this system is a major driving factor affecting water and nutrient dynamics, and water quality in this region. However, despite its critical role, the specific locations of subsurface tile drainage structures are not generally available nor well captured by conventional optical image processing due to soil surface features, such as topographic depressions and tillage. To overcome these challenges, in this study, we have explored the potential of using thermal images to identify the location of a subsurface drainage pipe. The hypothesis is that the unique spatial distribution of soil moisture set up by tile drains can result in the difference in surface soil temperature between areas near and away from drainage pipes. Toward this objective, we designed and developed an experimental device based on a dimensionless analysis at a scale of 1:20, which was deployed in the open air for 4.5 months. The experimental results demonstrate that (1) there is an ideal time for thermal image acquisition that maximizes the contrast between the regions close to and distant from subsurface drainage systems, and (2) the thermal image processing approach proposed in this study is a promising tool that has advantages of higher accuracy and stability in localizing subsurface drainage pipes over optical image-based approaches

Optically guided level set for underwater object segmentation.

Combining underwater optical imaging principles and the level set, this paper proposes a novel type of level set method called optically guided level set. This novel method can transform optical challenges in underwater environments (such as the illumination bias and wavelength-selective absorption) into valuable guidance for underwater object segmentation. Using the underwater optical guidance, our novel method can generate accurate object segmentation results by suitable initialization and regular evolving of the level set. The optical guidance core lies in two observations pertaining to the underwater optical imaging process: (i) the overlap between the object region and optical collimation region and (ii) the correspondence between the object structure and irradiation distribution inside the optical collimation. The high accuracy of our proposed method is demonstrated via comparisons to the state-of-the-art level set and salient object detection methods for public underwater images collected in diverse environments. Moreover, by using the work presented in this paper, we plan to demonstrate optical principles' potential for improving computer vision research, which is a promising research topic with many practical applications.

Partial TMR method for on‐orbit processors based on PageRank algorithm

On-orbit processors generally require reinforcement due to the harsh space radiation environment. Although the traditional full triple modular redundancy (TMR) method can effectively reinforce circuits, it requires excessive physical footprints and energy consumptions. To address this problem, this Letter proposes a general partial TMR method that can effectively reinforce a circuit. The method is based on the PageRank algorithm for sorting the importance of the triggers in a circuit, and the mean time between failure based on the dual logic cone model is used as the criterion to determine triggers that require redundancy under the partial TMR method. The arithmetic module of an optical image processing platform is tested to verify the effectiveness of the partial TMR method proposed in this Letter.

Quantitative X-Ray Microcomputed Tomography Assessment of Internal Tablet Defects

Physical tablet defects are related to internal structural defects that are not easily assessed by the traditional methods, such as dusting, laminating, or fracturing during appearance, friability, or hardness testing. Also, these methods do not allow objective and quantitative investigation of the role of formulation and process variables, which is essential for quality-by-design drug product development. In this study, an X-ray microcomputed tomography (XμCT) method to analyze internal tablet defects is developed using tablets from a quality-by-design design-of-experiment study. The design of experiment investigated the effect of roller compaction roll force, filler composition, and the amount of magnesium stearate on tablet quality attributes. Average contiguous void volume by optical image processing and fracture size distribution and direction by artificial intelligence–based image processing quantified the internal tablet fracture severity. XμCT increased formulation and process knowledge in support of scale-up manufacturing. We demonstrated how XμCT can be incorporated as a part of a holistic approach to quantitatively identify and mechanistically assess the risks of internal tablet defects. Furthermore, expanding the use of XμCT with an artificial intelligence–based quantitative analysis can deepen our tableting knowledge from an empirical understanding to a mechanistic understanding of compaction phenomenon.

Chabazite-Type Zeolite Membranes for Effective CO2 Separation: The Role of Hydrophobicity and Defect Structure.

Chabazite (CHA)-type zeolites are promising for the separation of CO2 from larger molecules, such as N2 (relevant to postcombustion carbon capture) and CH4 (relevant to natural gas/biogas upgrading). In particular, the pore size of CHA zeolites (0.37 × 0.42 nm2) can recognize slight molecular size differences between CO2 (0.33 nm) and the larger N2 (0.364 nm) or CH4 (0.38 nm) molecules, thus allowing separation in favor of CO2 through CHA membranes. Furthermore, the siliceous constituents in the CHA zeolite can reduce the adsorption capacity toward the smaller H2O molecule (0.265 nm) and, thus, the H2O permeation rate. This is highly desirable for securing good molecular sieving ability with CO2 permselectivity in the presence of H2O vapor. Indeed, a siliceous CHA film obtained with a nominal Si/Al ratio of 100 (CHA_100) showed high CO2/N2 and CO2/CH4 separation performance, especially in the presence of H2O vapor; ∼13.4 CO2/N2 and ∼37 CO2/CH4 separation factors (SFs) at 30 °C. These SFs were higher than the corresponding values (∼5.2 CO2/CH4 SFs and ∼31 CO2/CH4 SFs) under dry conditions; such improvement could be ascribed to defect blocking by physisorbed water molecules. Finally, the contribution of molecular transport through zeolitic and nonzeolitic parts was quantitatively analyzed by combining information extracted from image processing of fluorescence confocal optical microscopy images with a one-dimensional permeation model. It appears that ∼19 and ∼20% of the total CO2 permeance for CHA_100 were reduced due to transport inhibition by the physisorbed water molecules on the membrane surface and defect, respectively.

Deep learning wavefront sensing

We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources.

3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images

Over the past several years, researchers have shown increased interest in fiber system filter materials, as the formed fiber assembly exhibits a three-dimensional network structure with dense microscopic curved channel. The accurate measurement of various morphological parameters of fiber is the key to improve the filtration performance of fiber net filter material. However, most studies dealing with 2D micro-images cannot accurately measure some morphological features such as three-dimensional orientation. In this paper, a 3D image of fiber system filter material was reconstructed based on optical microscopic image processing technology. Firstly, a series of image layers of the fiber assembly were captured as the stage moved vertically; Then a fusion image was established for abstracting fiber edge; Thirdly the three-dimensional coordinates of the fiber edge was measured based on the pixel clarity; Finally, the three-dimensional structure the fiber system was reconstructed through distance transformation method based on the location of fiber edge.