Gray Transformation Research Articles

An Improved Product Defect Detection Method Combining Centroid Distance and Textural Information

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection.

Advancements in low light image enhancement techniques and recent applications

Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to those taken in daylight, resulting in occluded images characterized by distortion, low contrast, low brightness, a narrow gray range, and noise. Low-light image enhancement techniques play a crucial role in enhancing the effectiveness of object detection. This paper reviews state-of-the-art low-light image enhancement techniques and their developments in recent years. Techniques such as gray transformation, histogram equalization, defogging, Retinex, image fusion, and wavelet transformation are examined, focusing on their working principles and assessing their ability to improve image quality. Further discussion addresses the contributions of deep learning and cognitive approaches, including attention mechanisms and adversarial methods, to image enhancement.

Automated sugarcane crop disease forecasting with colour and texture features

ABSTRACT This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of ‘Neural Network (NN) and Support Vector Machine (SVM)’ for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively.

Super-resolution image restoration for microlens array imaging system

Microlens array (MLA) has been widely applied in optical imaging fields. However, the existing MLA imaging system (MLAIS) often suffers the problems of low imaging resolutions and luminance contrast due to the limitation of the sensor size and microlens aperture. To address these problems, we propose a super-resolution (SR) image restoration method for the microlens array imaging system (MLAIS). The proposed method uses an Otsu-Kmeans gravity-based center extraction method to segment the image captured by MLAIS into many sub-images and an enhanced correlation coefficient-based approach to align the sub-images. A maximum a posteriori probability algorithm is developed to recover the SR image from the aligned sub-images. A two-step gray transformation algorithm is designed to enhance the SR image. Numerical simulations and practical experiments are implemented to verify the effectiveness of the proposed method, and its SR restoration performance is evaluated by comparing the results with classical and state-of-the-art methods. The results indicate that the proposed method can achieve high-quality SR image restoration for the MLAIS, and its SR performance is better than the comparison methods. The proposed method may find applications in multispectral SR imaging, remote sensing SR imaging, muti-sensor SR imaging, and so on.

Application of vibration compensation based on image processing in track displacement monitoring

Abstract The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

For enhanced adaptability to poor light enhancement whilst achieving high image contrast, a new method for color image correction based on the advantages of non-linear function in grey transformation and histogram equalization techniques is proposed in this work. Firstly, the original red, green and blue (RGB) image is converted into the HSV color space, and the V channel is used for enhancement. An adaptive gamma generator is proposed to adaptively calculate gamma parameters in accordance with dark, medium, or bright image conditions. The computed gamma parameters are used to propose a cumulative distribution function that produces an optimized curve for illumination values. Next, a second modified equalization is performed to evenly correct the offset of the illumination curve values on the basis of the equal probability of the available values only. Finally, the processed V channel replaces the original V channel, and the new HSV model returns to the RGB color space. Experiments show that the proposed method can significantly improve the low contrast and poor illumination of the color image whilst preserving the color and details of the original image. Results from benchmark data sets and measurements indicate that the proposed method outperforms other state-of-the-art methods.

An Image Enhancement Method Using Nonlinear Function

Image enhancement plays an important role in image processing. This paper proposed an image enhancement method using a nonlinear(exponential) function. Firstly, we use the exponential function to construct the gray transformation equation. Then, depending on the position of each pixel, the gray scale obtained by gray transformation is further adjusted based on the compression factor. In section 3, the comparative experiment shows that the proposed method could achieve good performance.

Dust pollution evaluation based on grayscale average and fractal dimension of digital image

The automatic and effective identification of mine dust draws growing public concern for its universality and being highly hazardous to health and property. Based on improved methods for improved grayscale average (IGSA) and fractal dimension (FD) theory, a vision-based system that employs digital image processing was proposed to recognize dust particles. The proposed approach primarily includes the following procedures: image matrix generation; pixel gradient calculations; image processing for sharpening, gray transformation, and binarization; IGSA and FD calculations, and visualization accomplishments. Image example results show that FD appears to have an exponential relationship with IGSA as well as dust pollution. Then, a dust pollution-level evaluation method based on the pollution-factor Fp was established and contributed to the division of dust pollution regions. During the experiment, wind speed and hole distance were proved to demonstrate a positive and negative correlation with IGSA, respectively, while little impact was observed on the FD and Fp with the factors. A dust pollution monitoring system that is theoretically viable was developed to form an Industrial Internet of Things with a topological structure. The proposed approach provides a digital image-processing method to integrally and automatically characterize the dust morphology. This approach can achieve dust pollution-level and regional divisions.

Experimental Analysis of Low-Light Image Enhancement Techniques: A Review

Images acquired in low-light situations generally suffer from subjective visual effects on human eyes and the performance of different machine vision systems due to low brightness, low contrast, a limited gray range, color distortion, and high noise. Enhancing photographs taken in low light is done so that they may be used in other applications more effectively. In this study, we look back at some of the most important methods for improving images in dim light. We categorize these techniques into seven distinct groups: gray transformation, histogram equalization, Retinex, frequency-domain, picture fusion, defogging model, and machine learning. Methods are presented across the board, from the broadest to the most specific categories, emphasizing the underlying concepts and distinguishing features. Different improved picture quality assessment techniques are also described, as are comparisons of the various algorithms used. Finally, a state-of-the-art summary and recommendations for where the field should go are presented.

Research on Lane Line Detection Algorithm based on Improved Hough Transform

In order to solve the problems existing in the traditional lane line detection algorithm, such as weak robustness and low accuracy, an improved Hough transform lane line detection algorithm is proposed. Firstly, the method preprocesses the road image, including gray transformation, histogram equalization, Gaussian filtering, image binarization and so on. Then the canny edge detection operator is used to detect the edge of the image to be processed, and an improved Hough transform algorithm is proposed. Simulation results show that the algorithm has stronger robustness and higher accuracy than the traditional algorithm.

Research of neural network for weld penetration control

Abstract A method has been proposed for welding penetration status prediction in the paper. First, an experimental system was set up and welding experiments were performed. Some groups of welding images could been obtained. A composite filtering system composed of a neutral light reduction filter and a narrow band filter was developed to filter the weld arc disturbance. Some operations were performed to the images, namely the median filter and gray transformation. Then a neural network was setup, containing three layers. The inner widths of pool xn , the outer widths of pool xw , the width difference values e between the inner and outer of pool, ratios of inner pool widths Rn and ratios of outer pool widths Rw between two adjacent images were determined to be the input parameters. The penetration parameter p was chosen to be the output. Based on the images, groups of pool parameter data have been obtained and used to train the network. In this way, the weld penetration prediction model can be deduced. Finally, verification tests have been done. It showed that weld penetration situation predicted by the model is fit to its real condition. The accuracy rate is up to 96%, which affords a new way for penetration detection.

Surface defect detection of Si3N4 ceramic bearing ball based on improved homomorphic filter-Gaussian filter coupling algorithm

Considering the tiny surface defects of Si3N4 ceramic bearing balls and the low accuracy of defect detection using a single traditional algorithm, we find that the performance of the aerospace mechanical power system is poor. A coupling algorithm based on the improved homomorphic filter and Gaussian filter is proposed. The Si3N4 ceramic bearing ball nondestructive testing platform is established, by which the surface defect images of Si3N4 ceramic bearing balls are collected. The image acquisition card obtains the image information and then transmits it to the image storage module. The image processing module handles surface defect images. The gray transformation algorithm is used to complete image preprocessing. Gaussian noise in images is filtered out using the Gaussian filter algorithm. The homomorphic filtering algorithm is used to enhance the high frequency component, compress the low frequency component, and filter out the convolution noise and promiscuous signal. The contrast of the defect part has been reinforced using the coupling algorithm. It turns out that the accuracy of the coupling algorithm is 100%, 96.7%, 98.9%, and 94.4%.

Edge detail enhancement algorithm for high-dynamic range images

Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.

Competition Equilibrium Analysis of China’s Luxury Car Market Based on Three-Dimensional Grey Lotka–Volterra Model

An enterprise must be able to conduct in-depth analysis of the existing data as the information has certain grey characteristics, if it wants to occupy a dominant position in the fierce market competition. In this paper, a compound three-dimensional grey Lotka–Volterra model is developed to carry out the grey transformation of the original data, so that the data can have better simulation accuracy, and the observation noise of the original data can be reduced. The competitive situation analysis based on the three-dimensional grey Lotka–Volterra model can help enterprises better understand the market situation. This paper takes the luxury brand automobile market in mainland China as an example to conduct a competitive analysis and a balanced development simulation. It can be found that, based on Three Species System Analysis, there is a symbiotic relationship among automobile enterprises and that the three species model can be adopted in analyzing the competition and cooperation among enterprises. Through balanced development of a Symbiotic System Analysis, the results of symbiotic optimization under the achievable equilibrium state of three populations are obtained and they show that the proposed method can be used effectively to conduct the market competition analysis. It is thus of great importance to study the relationships among enterprises as it is helpful for enterprises to make strategic policies.

Compound Grey-Logistic Model and Its Application

Logistic regression model is widely used in ecology and in the analysis of social economic systems, because of its good adaptability. In order to improve the measurement accuracy of logistic model, this paper proposes a new method. A compound grey-logistic model is developed to carry out the grey transformation of the original data. Practice shows that the grey transformation data has better simulation accuracy; at the same time, grey transformation can reduce the observation noise of the original data. Mean absolute percentage error index has been used to evaluate the accuracy of prediction model, and information entropy can be used to evaluate the change of information entropy of forecasting data. In this paper, three cases are used to verify the applicability of grey-logistic model. From the perspective of the type of original data, the three cases represent three different data conditions: sufficient data, insufficient data, and fragmentary data. The cases represent different related fields: market share data, economic growth data, and R&D output data. The results show that the proposed grey-logistic method can effectively carry out the population growth analysis.

A Novel and Secure Image Encryption Scheme Based on Chaotic Logistic Map and Random Scrambling Using a Special Parameter of Gray Code Transformation

Background: Image security is a major concern in the digital communication media. So there are so many algorithms symmetric and asymmetric are available for securely transmit the image. In symmetric key algorithm many algorithms are available based on bit plane decomposition and random scrambling using chaotic maps. Objective: A secure image encryption algorithm is proposed based on the combination of (n, k, p) gray code and chaotic logistic map to improve the performance of decomposition encryption methods. Methods: We suggest a new algorithm that improves the security of image encryption based on (n, k, p) gray code and bit plane decomposition method. This new encryption technique uses the bit plane decomposition of (n, k, p) gray code then random scrambling of each bit plane and chaotic logistic by map using pixel substitution method. Results: The performance and security of proposed algorithm have been analyzed on different evaluation metrics like MSE, PSNR, UAIC and NPCR and Correlation Coefficient etc. The experimental results show that the proposed algorithm is secure and provides better results from the XOR based substitution method. Conclusion: This new method can be utilized in imaging systems like tomography images of medical field, tele-medicine and digital video, video conferencing in communication system. Further, we will study to enhance and examine the execution of (n, k, p) gray code for data hiding and picture de-noising.

Identification and Test Analysis of Scattered Objects in Traffic Accident Scene

The scattered objects contain a lot of important information on the scene of traffic accidents. The identification and extraction of relevant characteristic values of the scattered objects on the scene of traffic accidents can provide important basis for solving the collision speed of the vehicles in the accident and provide some evidence for the handling of traffic accidents. In this paper, scattered objects on the scene of traffic accidents are taken as the research object. According to the theoretical model of camera imaging and the characteristics of scattered objects in the accident site, a camera calibration system was proposed by combining with MATALB software. In order to reduce the error caused by background environment in the calibration process, the red calibration object with known structure size was designed to obtain the aerial photography image containing the calibration object. MATLAB was used to extract the calibration object in the image and establish the relationship model between the actual value of the calibration object and the image value. The image processing accuracy was improved by gray transformation, Wiener filter denoising, Laplace sharpening and Canny algorithm edge detection. Taking circular and irregular scattered objects as examples, the simulation test is designed. The proposed calibration method and image processing method are applied to identify the features of scattered objects. The identification results are calculated and compared with the actual measurement results to verify the accuracy of the proposed method and provide a reference for on-site accident treatment.

Gray Transformation of Internal Medicine.

AimTo study the profile of patients hospitalized in an internal medicine facility of a public hospital and explore if there is a shift in the age distribution of hospitalized patients toward an older age group.MethodsThe study was conducted at a tertiary care hospital in Delhi where the department of medicine has six units and a particular unit is in charge of the emergency room one day a week and every sixth Sunday. A total of 716 patients hospitalized in the medicine ward through the emergency services each Wednesday and every sixth Sunday (i.e, admission days of a particular unit) during a period for six months (November 2017 to April 2018) were retrospectively identified and their name, age, sex, comorbidities, final diagnosis, duration of hospital stay, and the outcome were noted. This was compared with similar data collected from a previous study conducted in the same setting in the years 2010-2011. The data were analyzed using Stata version 13 software (StataCorp, College Station, Texas). Findings were compared using the chi-squared test or t-test, wherever applicable.ResultsThe mean age of patients hospitalized in 2010-2011 was 43.9 years, and this had increased to 48 years in 2018-2019 suggesting that the average patient being hospitalized in general medicine wards of a public hospital was now approximately five years older. Thirty-nine percent (39%) of the individuals admitted were more than 60 years of age in 2018-2019 as compared to 28.0% in 2010-2011. There was a significant rise in the cases of cardiovascular and gastrointestinal diseases and a decline in poisoning and infectious diseases from the year 2010-2011 to 2018-2019. A significantly higher prevalence of cardiovascular diseases, diabetes, and respiratory diseases was found in the current study among older adults.ConclusionThere has been a significant shift in the distribution of hospitalized patients in the internal medicine in-patient wards toward the older age group during the last decade. Also, there is a significant difference between the disease profiles of older and young patients. A comprehensive approach to geriatric medicine should be introduced and followed.

Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality

Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to manage the water quality (WQ) of lakes. In this study, an automatic approach for extracting the pen aquaculture area was developed based on Landsat data. The approach integrates five algorithms, including grey transformation, discrete wavelet transform, fast Fourier transform, singular value decomposition and k-nearest neighbor classification. It was successfully applied in the automatic mapping of the pen aquaculture areas in Lake Yangcheng from 1990 to 2016. The overall accuracies were greater than 92%. The result indicted that the practice of pen aquaculture experienced five stages, with the general area increasing in the beginning and decreasing by the end of the last stage. Meanwhile, the changes of nine WQ parameters observed from 2000 to 2016, such as ammonia (NH3-N), pH, total nitrogen (TN), total phosphorus (TP), chlorophyll a, biochemical oxygen demand (BOD), chemiluminescence detection of permanganate index (CODMn), Secchi disk depth (SDD) and dissolved oxygen (DO), were analyzed in the lake sectors of Lake Yangcheng, and then their relationships were explored with the percentage of pen aquaculture area. The result suggested that the percentage of pen aquaculture area exhibits significantly positive correlations with NH3-N, TN, TP, chlorophyll a, BOD and CODMn, but significantly negative correlations with SDD and DO. The experimental results may offer an important implication for managing similar shallow lakes with pen aquaculture expansion and water pollution problems.

A Performance Analysis Model for the Training and Education of Information Security Talents

There are many defects with the existing performance analysis system for the training and education (T&E) of information security talents in Chinese colleges, ranging from the difficulty in transforming fuzzy information to the lack of objectivity. To overcome these defects, this paper develops an improved performance analysis model for the T&E of information security talents. Firstly, the existing analysis strategies for the T&E performance of information security talents were fully analyzed, and a performance analysis indicator system was established through the analysis. Based on the established indicator system, the gray classification and fuzzy information transformation were discussed for the performance analysis. On this basis, a grey clustering model for the T&E of information security talents was constructed, and the realization of the corresponding algorithm was explained in details. The proposed model was proved feasible and effective through case analysis. In addition to theoretical innovation, our model also has a great potential in engineering, because the solving algorithm can effectively process fuzzy information with only a small sample set.