Search Results (18)

The images acquired from near infrared cameras can contain thermal noise, which degrades the quality of the images. The quality of the images obtained from underwater environments suffer from the complex hydrological environment. All these issues make the profile-extraction in these images a difficult task. In this work, two non-learning systems are built for making filters by using wavelets transform combined with simple functions. They can be shown to extract profiles in the images acquired from the near infrared camera and underwater environment. Furthermore, they are useful for low-light image enhancement, edge/array detection, and image fusion. The increase in the measurement by entropy can be found by enhancing the scale of the filters. When processing the near infrared images, the values of running time, the memory usage, Signal-to-Noise Ratio (SNR), and Peak Signal-to-Noise Ratio (PSNR) are generally smaller in the operators of Canny, Roberts, Log, Sobel, and Prewitt than those in the Atanh filter and Sech filter. When processing the underwater images, the values of running time, the memory usage, SNR, and PSNR are generally smaller in Sobel operator than those in the Atanh filter and Sech filter. When processing the low-light images, it can be seen that the Atanh filter obtains the highest values of the running time and the memory usage compared to the filter based on the Retinex model, the Sech filter, and a matched filter. Our designed filters require little computational resources comparing to learning-based ones and hold the merits of being multifunctional, which may be useful for advanced imaging in the field of bio-medical engineering. Full article

(1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Methods: The OCT image undergoes a Discrete Wavelet Transform (DWT) to decompose it into multiple scales, isolating high-frequency wavelet coefficients that encapsulate fine texture details. These high-frequency coefficients are further processed using a Shift-Invariant Wavelet Transform (SWT) to generate an additional set of coefficients, ensuring an enhanced feature preservation and reduced artifacts. Both the original DWT high-frequency coefficients and their SWT-transformed counterparts are independently denoised using a Deep Neural Convolutional Network (DnCNN). This dual-pathway approach leverages the complementary strengths of both transform domains to suppress noise effectively. The denoised outputs from the two pathways are fused using a correlation-based strategy. This step ensures the optimal integration of texture features by weighting the contributions of each pathway according to their correlation with the original image, preserving critical diagnostic information. Finally, the Inverse Wavelet Transform is applied to the fused coefficients to reconstruct the denoised OCT image in the spatial domain. This reconstruction step maintains structural integrity and enhances diagnostic clarity by preserving essential spatial features. (3) Results: The MSE, PSNR, and SSIM indices of the proposed algorithm in this paper were 4.9052, 44.8603, and 0.9514, respectively, achieving commendable results compared to other algorithms. The Sobel, Prewitt, and Canny operators were utilized for edge detection on images, which validated the enhancement effect of the proposed algorithm on image edges. (4) Conclusions: The proposed algorithm in this paper exhibits an exceptional performance in noise suppression and detail preservation, demonstrating its potential application in OCT image denoising. Future research can further explore the adaptability and optimization directions of this algorithm in complex noise environments, aiming to provide more theoretical support and practical evidence for enhancing OCT image quality. Full article

In order to solve the problems of low efficiency and accuracy in the traditional detection of tunnel cracks, this paper proposes a tunnel crack detection method based on a UAV (unmanned aerial vehicle) equipped with a high-speed camera and a crack recognition algorithm using the improved multi-scale Retinex (MSR) algorithm and the Prewitt–Otsu algorithm, aiming to improve the accuracy and efficiency of detection. The tunnel crack detection method, based on a UAV equipped with a high-speed camera to acquire tunnel surface images, significantly improves the detection efficiency. The recognition method employs an improved multi-scale Retinex algorithm to process the acquired images, enhancing the details of the crack images and improving the contrast between cracks and the background. The enhanced images are input to the Prewitt–Otsu algorithm, which segments the crack image by combining Prewitt edge detection and Otsu thresholding. Finally, the pseudo-crack and isolated edges are removed by the minimum bounding rectangle principle. Using the UAV-collected tunnel surface images as targets, the tunnel crack recognition algorithm proposed in this paper is compared with other existing methods. The experimental results show that the method proposed in this paper improves the recognition ability of the small-texture features of the tunnel’s surface, and the overall crack recognition accuracy is higher than the existing methods. The proposed method not only enhances the efficiency of tunnel crack detection but also significantly improves the recognition accuracy, demonstrating substantial practical significance for tunnel maintenance and safety management. Full article

In certain applications of acoustic emission sensors, acoustic maps can be generated from captured signals. The work “In-Dressing Acoustic Map by Low-Cost Piezoelectric Transducer” introduces an innovative technique using these sensors to map grinding wheel surfaces, essential for finishing machined parts. However, producing sharp acoustic maps is challenging due to industrial interference. This study explores digital image processing techniques to enhance these maps, using cloud-based tools. Techniques such as smoothing, equalization, and edge detection (Sobel, Canny, Roberts, and Prewitt) were applied. The processed acoustic maps revealed sharper details, enabling more accurate assessments of dressing conditions. The results demonstrate the effectiveness of digital image processing when applied to acoustic maps, significantly improving the evaluation of the dressing process and contributing to the development of Industry 4.0. Full article

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections. Full article

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images. Full article

Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images. Full article

This paper aims to evaluate detection algorithms for perimeter security systems based on phase-sensitive optical time-domain reflectometry ($\Phi$-OTDR). Our own designed and developed sensor system was used for the measurement. The main application of the system is in the area the perimeter fencing intrusion detection. The system is unique thanks to the developed motherboard, which contains a field-programmable gate array (FPGA) that takes care of signal processing. This allows the entire system to be integrated into a 1U rack chassis. A polygon containing two different fence types and also cable laid underground in a plastic tube was used for testing. Edge detection algorithms using the Sobel and Prewitt operators are considered for post-processing. The comparison is made based on the signal-to-noise ratio (SNR) values calculated for each event. Results of algorithms based on edge detection methods are compared with the conventional differential method commonly used in $\Phi$-OTDR systems. Full article

As a revolutionary new technique, laser-engineered net shaping (LENS) is a layer additive manufacturing process that enables accurate, rapid and automatic repair of industrial component damage. In the laser repair (LR) process or in service, surface cracks can appear, which have a detrimental effect on the repair quality and the mechanical performance; therefore, the surface crack detection of repaired components has attracted much attention. Laser spot thermography is an important nondestructive testing method with the advantages of non-contact, full-field and high precision, which shows great potential in the crack detection of repaired components. The selection of thermographic process parameters and the optimization of thermal image processing algorithms are key to the success of the nondestructive detection. In this paper, the influence of material properties and thermographic process parameters on the surface temperature gradient is studied based on the simulation of laser spot thermal excitation, and the selection windows of thermographic process parameters for iron-based and nickel-based alloys are obtained, which is applied to the surface crack detection of repaired components. To improve the computational efficiency of thermal images, the Prewitt edge detection algorithm is used in the thermal image processing, which realized fast extraction of cracks with a high signal-to-noise ratio (SNR), and the detection sensitivity of crack width can reach 10 μm. To further study the influence of surface roughness on the thermographic detection, repair layers with and without polishing process are characterized, which show that the Prewitt edge detection algorithm is well applicable to crack detection on surfaces with different roughness level. Full article

The aim of this work is to study the influence of lighting on different types of filters in order to create adaptive systems of perception in the visible spectrum. This problem is solved by estimating symmetry operations (operations responsible for image/image transformations). Namely, the authors are interested in an objective assessment of the possibility of reproducing the image of the object (objective symmetry of filters) after the application of filters. This paper investigates and shows the results of the most common edge detection filters depending on the light level; that is, the behavior of the system in a room with indirect natural and standard (according to the requirements of the educational process in Ukraine) electric lighting was studied. The methods of Sobel, Sobel x, Sobel y, Prewitt, Prewitt x, Prewitt y, and Canny were used and compared in experiments. The conclusions provide a subjective assessment of the performance of each of the filters in certain conditions. Dependencies are defined that allow giving priority to certain filters (from those studied) depending on the lighting. Full article

Tuberculosis is a potential fatal disease with high morbidity and mortality rates. Tuberculosis death rates are rising, posing a serious health threat in several poor countries around the world. To address this issue, we proposed a novel method for detecting tuberculosis in chest X-ray (CXR) images that uses a three-phased approach to distinguish tuberculosis such as segmentation, feature extraction, and classification. In a CXR, we utilized the Weiner filter to distinguish and reduce the impulse noise. The features were extracted from CXR images and trained using a decision tree classifier known as the stacked loopy decision tree (SLDT) classifier. For the classification process, the ROI-based morphological approach was applied in the mentioned three-phased approach, and the feature extraction was accomplished through chromatic and Prewitt-edge highlights. Full article

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 am and 5:00 am under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 am, 1:00 pm and 3:00 pm. Full article

Online measurement of particle size distribution in the crushing process is critical to reduce particle obstruction and to reduce energy consumption. Nevertheless, commercial systems to determine size distribution do not accurately identify large particles (20–250 mm), leading to particle obstruction, increasing energy consumption, and reducing equipment availability. To solve this problem, an online sensor prototype was designed, implemented, and validated in a copper ore plant. The sensor is based on 2D images and specific detection algorithms. The system consists of a camera (1024 p) mounted on the conveyor belt and image processing software, which improves the detection of large particle edges. The algorithms determine the geometry of each particle, from a sequence of digital photographs. For the development of the software, noise reduction algorithms were evaluated and selected, and a routine was designed to incorporate morphological mathematics (erosion, dilation, opening, lock) and segmentation algorithms (Roberts, Prewitt, Sobel, Laplacian–Gaussian, Canny, watershed, geodesic transform). The software was implemented (in MatLab Image Processing Toolbox) based on the 3D equivalent diameter (using major and minor axes, assuming an oblate spheroid). The size distribution adjusted to the Rosin Rammler function in the major axis. To test the sensor capabilities, laboratory images were used, where the results show a precision of 5% in Rosin Rambler model fitting. To validate the large particle detection algorithms, a pilot test was implemented in a large mining company in Chile. The accuracy of large particle detection was 60% to 67% depending on the crushing stage. In conclusion, it is shown that the prototype and software allow online measurement of large particle sizes, which provides useful information for screening equipment maintenance and control of crushers’ open size setting, reducing the obstruction risk and increasing operational availability. Full article

Regular power line inspections are essential to ensure the reliability of electricity supply. The inspections of overground power submission lines include corridor clearance monitoring and fault identification. The power lines corridor is a three-dimensional space around power cables defined by a set distance. Any obstacles breaching this space should be detected, as they potentially threaten the safety of the infrastructure. Corridor clearance monitoring is usually performed either by a labor-intensive total station survey (TS), terrestrial laser scanning (TLS), or expensive airborne laser scanning (ALS) from a plane or a helicopter. This paper proposes a method that uses unmanned aerial vehicle (UAV) images to monitor corridor clearance. To maintain the adequate accuracy of the relative position of wires in regard to surrounding obstacles, the same data were used both to reconstruct a point cloud representation of a digital surface model (DSM) and a 3D power line. The proposed algorithm detects power lines in a series of images using decorrelation stretch for initial image processing, the modified Prewitt filter for edge enhancement, random sample consensus (RANSAC) with additional parameters for line fitting, and epipolar geometry for 3D reconstruction. DSM points intruding into the corridor are then detected by calculating the spatial distance between a reconstructed power line and the DSM point cloud representation. Problematic objects are localized by segmenting points into voxels and then subsequent clusterization. The processing results were compared to the results of two verification methods—TS and TLS. The comparison results show that the proposed method can be used to survey power lines with an accuracy consistent with that of classical measurements. Full article

Image interpolation is important in image zooming. To improve the quality of image zooming, in this work, we proposed a class of rational quadratic trigonometric Hermite functions with two shape parameters and two classes of $C^1$ -continuous Coons patches constructions over a triangular domain by improved side–side method and side–vertex method. Altering the values of shape parameters can adjust the interior shape of the triangular Coons patch without influencing the function values and partial derivatives of the boundaries. In order to deal with the problem of well-posedness in image zooming, we discussed symmetrical sufficient conditions for region control of shape parameters in the improved side–side method and side–vertex method. Some examples demonstrate the proposed methods are effective in surface design and digital image zooming. $C^1$ -continuous Coons patches constructed by the proposed methods can interpolate to scattered 3D data. By up-sampling to the constructed interpolation surface, high-resolution images can be obtained. Image zooming experiment and analysis show that compared to bilinear, bicubic, iterative curvature-based interpolation (ICBI), novel edge orientation adaptive interpolation scheme for resolution enhancement of still images (NEDI), super-resolution using iterative Wiener filter based on nonlocal means (SR-NLM) and rational ball cubic B-spline (RBC), the proposed method can improve peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Edge detection using Prewitt operator shows that the proposed method can better preserve sharp edges and textures in image zooming. The proposed methods can also improve the visual effect of the image, therefore it is efficient in computation for image zooming. Full article