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Using glass as a structural element that could obtain the role of the primary load-bearing elements of iconoclast structures has had a superficial history. Because of its aesthetic and admissible mechanical properties, glass has been considered one of the structural elements in the construction industry. Because of its extrasafety, laminated glass on top of the most widely used elements has been appraised with more depth. The study aimed to evaluate the failure modes in laminated glass and the possibility of detecting the pre-failure status of the surveyed elements, referring to the proffered approaches and image processing (IP) procedures in the literature. The outcomes have compared the peculiarity and strength points of various routes and have reached the inference for proper approaches in every procedure. According to the results, image processing (IP) is the most appropriate method of pre-failure detection in load-bearing laminated glass elements. Among the image processing (IP) techniques, digital image correlation has the widest application. The results of this study, in combination with other pieces of research, may propound a database for evaluating the failure status of glass structures. [ABSTRACT FROM AUTHOR]

Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework's integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy. [ABSTRACT FROM AUTHOR]

Paper based lateral flow systems are widely used biosensor platforms to detect biomolecules in a liquid sample. Proteins, bacteria, oligonucleotides, and nanoparticles were investigated in the literature. In this work we designed a magnetic platform including dual magnets and tested the flow of micron size immunomagnetic particles alone and when loaded with cells on two different types of papers. The prewetting conditions of the paper and the applied external magnetic field are the two dominant factors affecting the particle and cell transport in paper. The images recorded with a cell phone, or with a bright field optical microscope were analyzed to measure the flow of particles and cells. The effect of prewetting conditions and magnetic force were measured, and it was shown that in the worst case, minimum 90% of the introduced cells reached to the edge of the paper. The paper based magnetophoretic lateral flow systems can be used for cell assays. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices. [ABSTRACT FROM AUTHOR]

In this paper, the paper cups were used as the research objects, and the machine vision detection technology was combined with different image processing techniques to investigate a non-contact optical automatic detection system to identify the defects of the manufactured paper cups. The combined ring light was used as the light source, an infrared (IR) LED matrix panel was used to provide the IR light to constantly highlight the outer edges of the detected objects, and a multi-grid pixel array was used as the image sensor. The image processing techniques, including the Gaussian filter, Sobel operator, Binarization process, and connected component, were used to enhance the inspection and recognition of the defects existing in the produced paper cups. There were three different detection processes for paper cups, which were divided into internal, external, and bottom image acquisition processes. The present study demonstrated that all the detection processes could clearly detect the surface defect features of the manufactured paper cups, such as dirt, burrs, holes, and uneven thickness. Our study also revealed that the average time for the investigated Automatic Optical Detection to detect the defects on the paper cups was only 0.3 s. [ABSTRACT FROM AUTHOR]

Unprinting of paper can reduce multiple life-cycle stages in the recycling of paper to yield significant environmental impact. Laser-unprinting has been demonstrated for uncoated paper but causes significant damage to coated paper. This work explores a scalable optical (non-laser) process for unprinting coated paper. Printed coated paper is exposed to pulsed broad-spectrum Intense Pulsed Light (IPL) from a xenon lamp and the toner is then removed by dabbing gently with an ethanol wipe. While black toner is easily unprinted, unprinting of colored prints (red, blue, green) is best realized by incorporating an initial overprint of black toner. An unprinting throughput on the order of mm2/s is achieved. Three distinct regimes of unprinting are identified based on the extent of toner removal and damage of the paper. The optical properties of the unprinted paper, characterized via UV–Visible spectrophotometry, are correlated to these regimes to understand the potential for in-situ optical process monitoring. Scanning Electron Micrography and Fourier-Transform Infrared Spectroscopy are performed to understand the underlying mechanisms that govern the occurrence of the different unprinting regimes. Further, the potential impact of the developed approach on recycling of paper is discussed in the context of the capabilities of current optical unprinting approaches and the potential elimination of life-cycle stages in conventional paper recycling. • Intense Pulsed Light can scalably unprint toner from coated paper with minimal chemical usage. • Our overprinting approach enables colored prints to be removed as well. • Unprinting throughput of mm2/s is achieved. • Three distinct unprinting regimes exist, depending on process parameters and toner types. • Thermal weakening of toner-coating bonds and cracking are the primary governing mechanisms. [ABSTRACT FROM AUTHOR]

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel analytical technique for colorimetric glucose determination on µPADs. • Non-invasive sweat glucose detection independent of sample volume. • Seamless operation: no extra gadgets nor smartphone camera is required. • Computationally inexpensive readout on portable Arm platforms demonstrated. The colorimetric method is widely exploited for glucose measurement on microfluidic paper-based and microfluidic thread/paper-based analytical devices, μPADs and μTPADs, respectively. However, two significant challenges still hinder the real-world applications: the variation of the generated color based on sample volume variation, and the consistent dependency on an imaging camera - usually a smartphone camera - for color readout. The latter also suffers from multiple limitations, such as the variable ambient light conditions and imaging variation among different smartphone brands and models. This manuscript suggests a new device demonstrating a novel viewpoint to the colorimetric method to address both aforementioned challenges. The presented device, Volume-Independent Autonomous box (VIA box), continuously monitors the transient color of the µPAD upon the introduction of the sample till color stabilization, in contrast to similar studies where merely the final stabilized color is read. The interpretation of the transient color profile with respect to time results in the measurement of the glucose level independent of sample volume. In addition, the VIA box monitors µPAD's color using an ordinary RGB sensor instead of an imaging camera in similar studies. Since merely RGB values are recorded in any measurement instance, the computational cost is extremely low compared to the relevant literature where image processing techniques are used. VIA box exploits a simple low-end microcontroller to continuously monitor the transient color, interpret the color profile, and show the glucose level. The samples used in this manuscript are made of artificial sweat samples, including the known glucose range in real sweat samples. [ABSTRACT FROM AUTHOR]

We report a paper-based chemosensor array device (PCSAD) for the quantitative detection of oxyanions including the herbicide glyphosate (GlyP) in aqueous media. The mechanism of the oxyanion detection relies on a coordination-binding-based sensor array. In this study, the competitive coordination binding among Zn2+, four catechol dyes, and seven oxyanions caused noticeable colour changes. The colour changes were employed for qualitative and quantitative analyses using an in-house automated image-processing algorithm with pattern recognition for digital images. A linear discrimination analysis discerned similarly structured oxyanions with 100% accuracy. The regression analysis allowed the accurate quantification of GlyP in the herbicide products with a limit of detection of 16 mg/L, which is lower than the health advisory value for children (20 mg/L) stipulated by the environmental protection agency (EPA). PCSAD is a powerful sensor device for the on-site quantification of aqueous anions for environmental assessment. [ABSTRACT FROM AUTHOR]

During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks. [ABSTRACT FROM AUTHOR]

Thermal paper is widely used as a print medium for different applications but it constitutes a tricky substrate for fingermark visualization. An earlier work (J Forensic Sci 2015;60:1034) reported how to visualize fingermarks on untreated thermal paper by illuminating the item with a UV‐A light source. In the present paper, the potential of the near infrared (NIR) luminescence has been tested on thermal paper compared to the mentioned method. A controlled study was carried out utilizing eccrine enriched fingermarks. The promising outcomes obtained were further confirmed by performing a pseudo‐operational trial. Data clearly showed that the use of the NIR filter gave better results. Finally, preliminary tests suggested a different mechanism of reaction induced by fingermarks with respect to the one behind the thermal printing. Thus, NIR luminescence represents a refinement to the suite of optical examination processes, including the potential to increase the number of marks recovered in a noncontact, nondestructive way. [ABSTRACT FROM AUTHOR]

Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ($\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%. [ABSTRACT FROM AUTHOR]

The current work was aimed to develop the paper microzone plates (PµZP) as a scanometric system for determining the total antioxidant capacity of plant extracts. The PµZP was constructed by immobilizing CUPRAC reagent onto 70-well of patterned paper as a sensing zone for the scanometric detection system. The light blue-sensing zone showed a sensitive response to standard antioxidant (rutin) by forming light yellow color adducts which can be scanned and quantitatively measured by image processing program as a scanometric method. The sensor gave a notable signal at 8 min after rutin addition and presented a linear response in the concentration span of 1–10 mM. The developed method was shown to be reproducible (RSD < 3%) and accurate (98–101% recovery) for determining the TAC of plant extracts (as mM rutin equivalents) and showed strong correlation (r = 0.9887) with the standard CUPRAC method, suggesting that it can be applied for the simple and rapid method for TAC determination. [ABSTRACT FROM AUTHOR]

In the production of facial oil blotting papers, a certain number of papers must be counted before packaging. Currently, the papers are counted by hand, and this is hard work. Also, there are risks of adhesion of dust and wrinkles. In order to solve these problems, we propose a vision‐based approach. After the papers, which are arranged by shifting, are captured, the proposed image processing steps detect the boundaries of the papers. By counting the detected boundaries, the number of papers can be counted. Since the parameters for the proposed image processing are optimized by genetic algorithm, prior setting by a user is not necessary. For the experiments, an image dataset was constructed with six types of facial oil blotting papers. The proposed method achieved higher F‐measure than in other related works. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (µPADs) have attracted much attention over the past decade because they offer clinicians the ability to deliver point-of-care testing and onsite analysis. Many of the advantages of µPADs, however, are limited to work in a laboratory setting due to the difficulties of processing data when using electronic devices in the field. This review focuses on the use of µPADs that have the potential to work without batteries or with only small and portable devices such as smartphones, timers, or miniaturized detectors. The µPADs that can be operated without batteries are, in general, those that allow the visual judgment of analyte concentrations via readouts that are measured in time, distance, count, or text. Conversely, a smartphone works as a camera to permit the capture and processing of an image that digitizes the color intensity produced by the reaction of an analyte with a colorimetric reagent. Miniaturized detectors for electrochemical, fluorometric, chemiluminescence, and electrochemiluminescence methods are also discussed, although some of them require the use of a laptop computer for operation and data processing. [ABSTRACT FROM AUTHOR]

Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures. [ABSTRACT FROM AUTHOR]

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry. Image 1 • Optimization of device geometry and amount of assay reagent using D-optimal design. • Investigation of seven factors at three levels by only performing 44 trials. • MATLAB-based morphological recognition algorithm for signal readout. [ABSTRACT FROM AUTHOR]

To increase crop productivity, recognition of a plant disease is a very pivotal task. The prior identification of plant diseases helps farmers to prevent losses occurred by various types of plant diseases. Manual observation and identification of plant diseases is a very tedious task as it requires a lot of time, money, efforts etc. therefore an automated system is required that efficiently identifies plant disease in a minimum span of time without requiring much efforts. Plant disease have been identified using various techniques such as image segmentation, clustering, convolution neural network, decision trees and many more by determining the patterns of plant images based on their colour, spots, light intensity over a certain period of time by various researchers. This paper aims to portray the techniques formulate in this area. This work infers the major challenges that thus far not explored. [ABSTRACT FROM AUTHOR]

To increase crop productivity, recognition of a plant disease is a very pivotal task. The prior identification of plant diseases helps farmers to prevent losses occurred by various types of plant diseases. Manual observation and identification of plant diseases is a very tedious task as it requires a lot of time, money, efforts etc. therefore an automated system is required that efficiently identifies plant disease in a minimum span of time without requiring much efforts. Plant disease have been identified using various techniques such as image segmentation, clustering, convolution neural network, decision trees and many more by determining the patterns of plant images based on their colour, spots, light intensity over a certain period of time by various researchers. This paper aims to portray the techniques formulate in this area. This work infers the major challenges that thus far not explored. [ABSTRACT FROM AUTHOR]

In this paper, novel salicylidene fluorescent sensors ( 1 , 2 , and 3) were successfully synthesized by the one pot condensation reaction and coated onto filter paper to produce fluorometric paper-based sensor array. The compound 1 , 2, and 3 paper-based sensor arrays exhibited rapid fluorescent signal in response to exposure to 15 different VOC vapors. A smartphone was used as an alternative device to monitor the appearance of fluorescent signals. The images photographed using the smartphone camera were analyzed for RGB values by a conventional image-processing program. The ΔRGB data set (3 fluorophores × 15 VOC vapors × 4 repeats) from fluorescent images of 15 VOC vapors analysis have been statistically categorized by using principal component analysis (PCA) into 15 clusters with 100% discriminating accuracy. The proposed sensor array showed high potential as a portable electronic nose (e-nose) system for VOC vapors discrimination. [ABSTRACT FROM AUTHOR]

The accumulation of snow is one of the essential reasons for the significant decrease in the power generation efficiency of PV stations in winter. For this reason, this paper firstly provides a detailed review of the current research into the physical processes and shedding reasons for the natural melting of snow on PV module surfaces. Then, several common techniques to promote snow and ice melting on PV surfaces are discussed, and the feasibility and shortcoming of ultrasonic technology in removing high‐density snow and ice are analyzed in detail. Finally, this paper proposes two snow sensing technologies for PV modules, namely, image processing method and interdigital capacitance method. Among them, the image method based on fixed camera and UAV detection is more suitable for wide area PV power stations, while the interdigital capacitance method is helpful in obtaining snow characteristic parameters, such as icing type and LWC. The relevant research has certain reference significance for PV power stations to formulate a more reasonable snow removal plan and power generation forecast. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The matching problem often arises in image processing and involves finding a correspondence between similar objects. In particular, variational matching models optimize suitable energies that evaluate the dissimilarity between the current shape and the relative template. A penalty term often appears in the energy to constrain the regularity of the solution. To perform numerical computation, a discrete version of the energy is defined. Then, the question of consistency between the continuous and discrete solutions arises. This paper proves a Γ-convergence result for the discrete energy to the continuous one. In particular, we highlight some geometric properties that must be guaranteed in the discretization process to ensure the convergence of minimizers. We prove the result in the framework introduced in the 2017 paper of Charlier et al., which studies the matching problem between geometric structures carrying on a signal (fshapes). The matching energy is defined for L2 signals and evaluates the difference between fshapes in terms of the varifold norm. This paper maintains a dual attachment term, but we consider a BV penalty term in place of the original L2 norm. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

Segmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net. [ABSTRACT FROM AUTHOR]

• Phisically-based thermal infrared images rendering chain for artificial satellite targets. • Assessment and comparison of most relevant pixel-level VIS-TIR image fusion techniques. • Fused noisy image processing within the context of uncooperative space-craft relative navigation. State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions; however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented; secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain. [ABSTRACT FROM AUTHOR]

A stochastic network simulation is verified when its distribution of outputs is aligned with the ground truth, while tolerating deviations due to variability in real-world measurements and the randomness of a stochastic simulation. However, comparing distributions may yield false positives, as erroneous simulations may have the expected distribution yet present aberrations in low-level patterns. For instance, the number of sick individuals may present the right trend over time, but the wrong individuals were infected. We previously proposed an approach that transforms simulation traces into images verified by machine learning algorithms that account for low-level patterns. We demonstrated the viability of this approach when many simulation traces are compared with a large ground truth data set. However, ground truth data are often limited. For example, a publication may include few images of their simulation as illustrations; hence, teams that independently re-implement the model can only compare low-level patterns with few cases. In this paper, we examine whether our approach can be utilized with very small data sets (e.g., 5–10 images), as provided in publications. Depending on the network simulation model (e.g., rumor spread, cascading failure, and disease spread), we show that results obtained with little data can even surpass results obtained with moderate amounts of data at the cost of variability. Although a good accuracy is obtained in detecting several forms of errors, this paper is only a first step in the use of this technique for verification; hence, future works should assess the applicability of our approach to other types of network simulations. [ABSTRACT FROM AUTHOR]

Image denoising and edge extraction are two main tasks in image processing. In this paper, a game model is proposed to solve the image denoising and edge extraction, which combines an adaptive improved total variation (AdITV) model for image denoising and a global sparse gradient (GSG) model for edge extraction. The AdITV model is a forward-and-backward diffusion model. In fact, forward diffusion is applied to the homogeneous region to denoise, and backward diffusion is applied to the edge region to enhance the edge. A unified explicit discrete scheme is established in this paper to solve the AdITV model, which is compatible to forward diffusion and backward diffusion. The stability of the scheme is proved. On the other hand, GSG is a functional model based on sparse representation, which is robust to extract edges under the influence of noise. AdITV and GSG are chosen as two components of the game model. The alternate iteration method is used to solve the game problem. The convergence of the algorithm is proved and numerical experiments show the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Converting a camera's RAW image to an RGB format for human perception involves utilizing an imaging pipeline, and a series of processing modules. Existing modules often result in varying degrees of original information loss, which can render the reverse imaging pipeline unable to recover the original RAW image information. To this end, this paper proposes a new, almost reversible image imaging pipeline. Thus, RGB images and RAW images can be effectively converted between each other. Considering the impact of original information loss, this paper introduces a two-stage tone mapping operation (TMO). In the first stage, the RAW image with a linear response is transformed into an RGB color image. In the second stage, color scale mapping corrects the dynamic range of the image suitable for human perception through linear stretching, and reduces the loss of sensitive information to the human eye during the integer process. effectively preserving the original image's dynamic information. The DCRAW imaging pipeline addresses the problem of high light overflow by directly highlighting cuts. The proposed imaging pipeline constructs an independent highlight processing module, and preserves the highlighted information of the image. The experimental results demonstrate that the two-stage tone mapping operation embedded in the imaging processing pipeline provided in this article ensures that the image output is suitable for human visual system (HVS) perception and retains more original image information. [ABSTRACT FROM AUTHOR]

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes. [ABSTRACT FROM AUTHOR]

Simple Summary: In the sorting process of Chinese softshell turtles, it is necessary to classify them based on their weight and accurately identify their plastron and carapace. This process requires heavy manual labor and complex mechanical processing methods. To improve processing efficiency and reduce costs, this article introduces machine vision technology, and a new image processing method is proposed. This method can estimate the weight of Chinese softshell turtles and accurately locate the positions of their plastron and carapace. The automation level of aquaculture can be greatly enhanced, and hardware costs can be reduced through software optimization through this approach. With the rapid development of the turtle breeding industry in China, the demand for automated turtle sorting is increasing. The automatic sorting of Chinese softshell turtles mainly consists of three parts: visual recognition, weight prediction, and individual sorting. This paper focuses on two aspects, i.e., visual recognition and weight prediction, and a novel method for the object detection and weight prediction of Chinese softshell turtles is proposed. In the individual sorting process, computer vision technology is used to estimate the weight of Chinese softshell turtles and classify them by weight. For the visual recognition of the body parts of Chinese softshell turtles, a color space model is proposed in this paper to separate the turtles from the background effectively. By applying multiple linear regression analysis for modeling, the relationship between the weight and morphological parameters of Chinese softshell turtles is obtained, which can be used to estimate the weight of turtles well. An improved deep learning object detection network is used to extract the features of the plastron and carapace of the Chinese softshell turtles, achieving excellent detection results. The mAP of the improved network reached 96.23%, which can meet the requirements for the accurate identification of the body parts of Chinese softshell turtles. [ABSTRACT FROM AUTHOR]

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging paradigm that can tolerate some loss of accuracy to improve the energy consumption and design complexity. Multiplication is one of the important computational components in various digital signal processing (DSP) applications. Improvement of multipliers, as the building blocks of an approximate computing systems, has received significant attention. Compressors are key elements in an approximate multiplier to have an optimal design with high performance. This paper proposes an approximate/exact 4–2 compressor and an exact 5–2 compressor in carbon nanotube field-effect transistors (CNTFET) technology for utilisation in an approximate multiplier. Four efficient designs for utilising the proposed compressors are introduced and analysed for a Wallace multiplier. All of the proposed circuits are analysed in 32 nm CNTFET Stanford technology and are simulated by HSPICE. In addition, as an image processing application of approximate multipliers, this paper compares the quality metrics of the image multiplication for the proposed designs and previously presented approximated multipliers. For example, the proposed schemes provide excellent results in structural similarity index up to 99%, and peak signal-to-noise ratio more than 45 dB. Also, it uses figure of metric (FOM) that includes the accuracy and quality metrics of the approximate multipliers. The simulation results demonstrate the efficiency of proposed designs. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system's classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process. [ABSTRACT FROM AUTHOR]

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias. [ABSTRACT FROM AUTHOR]

Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

A rapid, simple and affordable method for arsenic speciation analysis is described in this work. The proposed methodology involves in situ arsine generation, transfer of the volatile to the headspace and its reaction with silver nitrate at the detection zone of a paper-based analytical device (PAD). Thus, silver nitrate acts as a recognition element for arsine in the paper-based sensor. The chemical reaction between the recognition element and the analyte derivative results in the formation of a colored product which can be detected by scanning the detection zone and data treatment with an image processing and analysis program. Detection and injection zones were defined in the paper substrate by formation of hydrophobic barriers, thus enabling the formation of the volatile derivative without affecting the chemical stability of the recognition element present in the PAD. Experimental parameters influencing the analytical performance of the methodology, namely color mode detection, composition of the paper-based sensor and hydride generation and mass transfer conditions, were evaluated. Under optimal conditions, the proposed method showed limits of detection and quantification of 1.1 and 3.6 ng mL −1 , respectively. Remarkably, the limit of detection of the method reported herein was much lower than the maximum contaminant levels set by both the World Health Organization and the US Environmental Protection Agency for arsenic in drinking water, unlike several commercially available arsenic test kits. The repeatability, expressed as relative standard deviation, was found to be 7.1% (n = 8). The method was validated against the European Reference Material ERM ® -CA615 groundwater and successfully applied to the determination of As(III), As(V) and total inorganic As in different water samples. Furthermore, the method can be used for the screening analysis of total arsenic in waters when a cut-off level of 7 ng mL −1 is used. [ABSTRACT FROM AUTHOR]

Simple and selective paper-based colorimetric sensors using silver nanoprism (AgNPrs) were conveniently fabricated and developed for the determination of chloride ions (Cl − ) contaminated in the environment. Samples containing different concentrations of Cl − were analyzed. The analysis is based on the oxidative etching of the AgNPrs into smaller silver nanospheres (AgNPss) by Cl − . In the presence of Cl − , the distinctive color change of the AgNPrs from dark-violet to red was rapidly visualized by the naked eye in 5 min without the requirement of surface modification. For quantitative measurement using image processing, a good linear relationship (R 2 = 0.996) between logarithmic of Cl − concentration and the average mean color intensity was obtained in the range of 10–1000 mg L −1 with a detection limit of 1.3 mg L −1 . The developed sensors were investigated for precision, accuracy, and sensitivity and validated by the classical method. Statistical analysis proved that the developed sensors were precise, sensitive and accurate and can be used effectively for the analysis of Cl − in natural waters. [ABSTRACT FROM AUTHOR]

A new analytical platform based on the use of thin-layer chromatography (TLC) coupled with paper-based analytical device (PAD) was developed for the determination of total capsaicinoids in chilli samples. This newly developed TLC-PAD is simple and low-cost without any requirement of special instrument or skillful person. The analysis consisted of two steps, i.e., extraction of capsaicinoids from chilli samples by using ethanol as solvent and separation of capsaicinoids by thin-layer chromatography (TLC) and elution of capsaicinoids from the TLC plate with in situ colorimetric detection of capsaicinoids on the PAD. For colorimetric detection, Folin-Ciocalteu reagent was used to detect phenolic functional group of capsaicinoids yielding the blue color. The blue color on the PAD was imaged by a scanner followed by evaluation of its grayscale intensity value by ImageJ program. This newly developed TLC-PAD method provided a linear range from 50 to 1000 mg L −1 capsaicinoids with the limit of detection as low as 50 mg L −1 capsaicinoids. The proposed method was applied to determine capsaicinoids in dried chilli and seasoning powder samples and the results were in good agreement with those obtained by HPLC method. [ABSTRACT FROM AUTHOR]

There is a high demand for facile on-site mercury ion quantification methods in water due to mercury's serious toxicity. Graphene quantum dot (GQD), fluorescent under ultra-violet (UV) light, can be easily synthesized and used as a mercury ion detection probe because the brightness of GQD's fluorescence decreases with the increase of the number of mercury ions. We developed a facile mercury (II) ion quantification method using GQD coated filter paper disks in a 3D-printed well array. Changes of GQD's fluorescent brightness dependent on mercury ion concentration were measured via optical microscopy image processing. We also observed the dependency of mercury ion detection sensitivity on the amount of GQD and type of GQDs. Our approach may be easily adapted for hand-held mobile mercury ion quantification devices in the future due to its simplicity and small sample size. • Concentrations of mercury (II) ions were optically quantified using graphene quantum dot coated filter paper disks. • The GQDs could be evenly physisorbed by making even contacts between filter paper disks and a well array's bottom surface. • Due to this work's simplicity, small sample volume, cost-effectiveness, it may be developed as mobile devices. [ABSTRACT FROM AUTHOR]

In this paper, a non-supervised technique for on-line paper characterisation is presented. The method uses self-organising maps (SOM) and texture analysis for clustering different kinds of paper according to their properties. A light-through technique is used to get pictures of paper. Then, effective texture features are extracted from greyscale images and the dimensionality of the feature data is reduced with SOM allowing visual analysis of measurements. The method makes it possible to implicitly extract important information about paper formation. The approach provides excellent results. A classification error below 1% was achieved for four quality classes when local binary pattern (LBP) texture features were used. The improvement to the previously used texture features in paper inspection is huge: the classification error was reduced by over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view of the inspected data. [ABSTRACT FROM AUTHOR]

Cellulose particles are among the aging products of the insulating paper that are used in power transformers. Too many cellulose particles can cause transformer accidents. Traditional research and detection methods that are used for this problem generally focus on the number and length information of cellulose particles, and it is usually difficult to quantitatively describe the spatial shape of cellulose particles. However, the shape of cellulose particles is also one of the factors affecting the safety of transformer insulation. In this paper, we successfully extracted quantitative information of the spatial shape of cellulose particles in transformer oil using an image processing technique and the transport of intensity equation, providing a new novel approach for the study and detection of the shape of cellulose particles in transformer oil. [ABSTRACT FROM AUTHOR]

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was >  0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing. [ABSTRACT FROM AUTHOR]

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of "illness", if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Optical tracking sensor technology has been widely used in the field of motion. However, there are still some challenges in the recognition of badminton players, and more accurate methods are needed to capture the dynamic characteristics of badminton players. The aim of this study is to design an optical tracking sensor system based on image feature extraction for badminton player motion recognition. In this paper, a high resolution camera is used to collect the image sequence of badminton match. Then through image processing and computer vision technology, the key image features are extracted from the image sequence. Then, machine learning algorithm is used to classify and recognize the extracted features to achieve accurate recognition of badminton players' movements. The experimental results show that the optical tracking sensor system can effectively extract the movement features of badminton players and identify their movements accurately. Compared with the traditional method, the system in this paper has higher precision and real-time performance, and can meet the needs of practical applications. [ABSTRACT FROM AUTHOR]

Oil painting education not only helps to improve students' drawing skills and quality but also fosters creative thinking in the field of art. In order to keep up with the development of art diversity, it is essential to establish the concept of art diversity education and reform university oil painting education to meet the needs of modern society. This study aims to explore the potential value of image optical processing technology based on computer vision in the application of oil painting teaching image simulation. In order to improve students' ability of oil painting simulation, this paper presents an image optical processing technology based on computer vision. The study collected a large number of oil painting images and teaching samples. Then, through image processing algorithm, we extract and analyze the optical features in the oil painting works, and then design an optical simulation method to apply these features to the students' simulation works. By comparing the traditional methods and the techniques proposed in this paper, the image optical processing technology based on computer vision has potential practical value in the application of oil painting teaching image simulation, which can help students better understand and simulate the optical effects of traditional oil paintings. [ABSTRACT FROM AUTHOR]

In order to understand the application of frost measurement of air source heat pump heat exchanger, a research on frost measurement of air source heat pump heat exchanger based on image recognition processing technology is put forward. In this paper, the scheme design of defrosting control system based on image processing technology is introduced, and the frost degree coefficient of outdoor heat exchanger fin surface is introduced to characterize the frost degree of fin surface. Secondly, the experimental environment room was built, and the conventional defrosting control method of the existing heat pump unit was tested on the spot. It was found that in the air temperature of -6~0 °C and the air relative humidity of 86~92%, the unit appeared the phenomenon of "defrosting in time" and "defrosting without frost". Finally, the change of frost layer on the surface of the outdoor heat exchanger fin during defrosting is analyzed, and the coefficient of frost degree on the surface of the fin is calculated and analyzed. At the beginning of defrosting, due to the large degree of frost, the P-value of the frost degree coefficient on the surface of the fin is close to 1, when the defrosting is carried out for 125 seconds, the P-value of the frost degree coefficient on the surface of the fin decreases sharply to 0.09, when the defrosting is completed, the P-value of the frost degree coefficient on the surface of the outdoor heat exchanger is maintained at about 0.04. The defrosting control method of air source heat pump based on image processing technology is proposed in this paper, and its technical feasibility and effect are verified by experimental tests, which lays a foundation for future popularization and application. [ABSTRACT FROM AUTHOR]