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This paper aims to overview the current status of development and application of digital image processing technology used for the yarn hairiness evaluation.

Digital image processing technology is one of the new methods used for the yarn detection, which can be used for the digital characterization and objective evaluation of yarn appearance. The comparison between the traditional detection methods and this new developed method was made and analyzed.

Compared with the traditional methods, image-based methods have the advantages of being objective, fast and accurate. Therefore, it was proved that digital image processing techniques should be a new trend in terms of the yarn appearance evaluation.

This paper aims to propose a system for defect detection in constructed elements that is able to indicate deformity positions. It also evaluates the defects in finishing materials of constructed building elements to support the subjective visual quality investigation of the aesthetics of an architectural work.

This strategy depends on defect features analysis that evaluates the defect value in digital images using digital image processing methods. The research uses the three-dimensional (3D) modeling techniques and image processing algorithms to generate a system that is able to perform some of the monitoring activities by computers. Based on the collected site scans, a 3D model is created for the building. Then, several images can be exported from the 3D model to investigate a specific element. Different image denoizing techniques are compared such as mean filter, median filter, Wiener filter and Split–Bregman iterations. The most efficient technique is implemented in the system. Then, the following six different methods are used for image segmentation to separate the concerned object from the background; color segmentation, region growing segmentation, histogram segmentation, local standard deviation segmentation, adaptive threshold segmentation and mean-shift cluster segmentation.

The proposed system is able to detect the cracks and defected areas in finishing works and calculate the percentage of the defected area compared to the total captured area in the photo with high accuracy.

The proposed system increases the precision of decision-making by decreasing the contribution of human subjective judgment. Investigation of different finishing surfaces is applied to validate the proposed system.

The purpose of this paper is to report on the results of an exploratory study on the use of unmanned aerial vehicles (UAV) as a visual data collection tool in the architecture, engineering, construction and facility management industry for the inspection of pathological manifestations in building facades.

The methodology used a field research experimental approach, where three case studies were carried out involving buildings of medium and high elevation. The protocol of activities included image collection and processing stages, as well as detailed analysis of the collected visual data for the identification of pathological manifestations in building facades.

The findings emphasize the technical feasibility and efficacy of inspections with UAV, showing that among the visual assets produced, digital photographs collected with the aircraft were more effective for the detection of pathologies when compared to the three-dimensional models and orthomosaics generated by digital photogrammetry software.

The research has formulated the protocol for the inspection of facades using UAV and the comparative analysis of visual assets that can be generated for inspection purposes.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

In order to fully understand the properties of porous asphalt, investigation should be conducted from different point of views. This is from the fact that porous asphalt mixture designed with the same aggregate gradation and air void content can give different infiltration rate due to the different formation of the internal structure. Therefore, the purpose of this paper is to investigate the micro-structural properties and functional performance of porous asphalt simultaneously.

The aim is to develop imaging techniques to process and analyze the internal structure of porous asphalt mixture. A few parameters were established to analyze the air void properties and aggregate interlock within the gyratory compacted samples captured using a non-destructive scanning technique of X-ray computed tomography (CT) throughout the samples. The results were then compared with the functional performance in terms of permeability. Four aggregate gradations used in different countries, i.e. Malaysia, Australia, the USA and Singapore. The samples were tested for resilient modulus and permeability. Quantitative analysis of the microstructure was used to establish the relationships between the air void properties and aggregate interlock and the resilient modulus and permeability.

Based on the results, it was found that the micro-structural properties investigated have successfully described the internal structure formation and they reflect the results of resilient modulus and permeability. In addition, the imaging technique which includes the image processing and image analysis for internal structure quantification seems to be very useful and perform well with the X-ray CT images based on the reliable results obtained from the analysis.

In this study, attention was limited to the study of internal structure of porous asphalt samples prepared in the laboratory using X-ray CT but can also be used to assess the quality of finished asphalt pavements by taking core samples for quantitative and qualitative analysis. The use of CT for material characterization presents a lot of possibilities in the future of asphalt concrete mix design.

Based on the validation process which includes comparisons between the values obtained from the image analysis and those from the performance test and it was found that the developed procedure satisfactorily assesses the air voids distribution and the aggregate interlock for this reason, it can be used.

The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search images similar to the query image based on colour histogram and texture features in the database. Thus, retrieving images by this image technique is expected to make the retrieval of Chinese painting images more precise and convenient for users.

In this study, a technique is proposed that considers spatial information of colours in addition to texture feature in image retrieval. This technique can be applied to retrieval of Chinese painting images. A database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. The authors develop an image-retrieval technique that considers colour distribution, spatial information of colours and texture.

In this study, a database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. An image-retrieval technique was developed that considers colour distribution, spatial information of colours and texture. Through adjustment of feature values, this technique is able to process both landscape and portrait images. This technique also addresses liubai (i.e. blank) and text problems in the images. The experimental results confirm high precision rate of the proposed retrieval technique.

In this paper, a novel Chinese painting image-retrieval technique is proposed. Existing image-retrieval techniques and the features of Chinese painting are used to retrieve Chinese painting images. The proposed technique can exclude less important image information in Chinese painting images for instance liubai and calligraphy while calculating the feature values in them. The experimental results confirm that the proposed technique delivers a retrieval precision rate as high as 92 per cent and does not require a considerable computing power for feature extraction. This technique can be applied to Web page image retrieval or to other mobile applications.

The impact of climate change towards water surface resources is crucial, particularly in developing and non-developing countries. Groundwater as a main water resource is thus an essential. However, contamination due to hydrocarbon spills affects the groundwater as a water resource, especially as a main source of drinking water. This chapter investigates the light non-aqueous phase liquid (LNAPL) penetrations in double-porosity soil with different moisture contents and with or without vibration impact. It also explains the LNAPL penetration phenomena by employing image analysis. The physical laboratory experiments were implemented using an acrylic cylinder, a mirror, toluene and a Nikon D90 DSLR digital camera. Prepared soil was poured in an acrylic cylinder and compressed with compressor until it became 10 cm in height. LNAPL was then poured instantaneously onto the acrylic cylinder that was filled with soil sample. The LNAPL penetration patterns were recorded and monitored using a Nikon D90 DSLR digital camera. The processing technique was conducted at predetermined time intervals using Surfer software and Matlab routine to plot the LNAPL pattern. The results showed that a higher penetration rate of LNAPL occurred with higher moisture content and without vibration impact. The penetration time for LNAPL to reach the bottom of the soil sample was found to be longer for the soil that had low moisture content and with vibration impact.

The purpose of this paper is to monitor the backup indicators in case of indicator failure and to minimize the situations when the pilot may be unable to monitor the indicator effectively in emergency situations.

In this study, the pointer positions of different indicators were determined with a deep learning-based algorithm. Within the scope of the study, the pointer on the analog indicators obtained from aircraft cockpits was detected with the YOLOv4 object detector. Then, segmentation was made with the GrabCut algorithm to detect the pointer in the detected region more precisely. Finally, a line including the segmented pointer was found using the least-squares method, and the exact direction of the pointer was determined and the angle value of the pointer was obtained by using the inverse tangent function. In addition, to detect the pointer of the YOLOv4 object detection method and to test the designed method, a data set consisting of videos taken from aircraft cockpits was created and labeled.

The analog indicator pointers were detected with great accuracy by the YOLOv4 and YOLOv4-Tiny detectors. The experimental results show that the proposed method estimated the angle of the pointer with a high degree of accuracy. The developed method can reduce the workloads of both pilots and flight engineers. Similarly, the performance of pilots can be evaluated with this method.

The authors propose a novel real-time method which consists of detection, segmentation and line regression modules for mapping the angle of the pointers on analog indicators. A data set that includes analog indicators taken from aircraft cockpits was collected and labeled to train and test the proposed method.

The purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.

By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.

With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.

The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accurate results with a low‐error rate.

In terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection.

The paper describes how pre‐inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi‐defects to inspect and improve six defects simultaneously, which improves the system stability greatly.

Froth flotation is a process whereby valuable minerals are separated from waste by exploiting natural differences or by chemically inducing differences in hydrophobicity. Flotation processes are difficult to model because of the stochastic nature of the froth structures and the ill‐defined chemorheology of these systems. In this paper a hierarchical configuration hybrid neural network has been used to interpret froth images in a copper flotation process. This hierarchical neural network uses two Pulse‐Coupled Neural Networks (PCNNs) as preprocessors that ‘convert’ the froth images into corresponding binary barcodes. Our technique demonstrates the effectiveness of the hybrid neural network for process vision, and hence, its potential for use for real time automated interpretation of froth images and for flotation process control in the mining industry. The system is simple, inexpensive and is very reliable.