Grayscale Conversion Research Articles

A study of applications in archive management empowered by artificial intelligence

Abstract As a critical component of information management, the development of digital and information archive systems enhances both the efficiency and quality of enterprise operations, thereby increasing economic value. This paper employs the ReLU activation function to augment the non-linear capabilities of the neural network model. Additionally, we introduce the MP-CNN model, designed to extract and recognize textual content from archival images. These images are initially processed using the BP neural network algorithm, followed by grayscale conversion for clarity, and subsequently backed up for analytical purposes. The implementation of intelligent archive management resulted in a more than 35% improvement in efficiency. The distribution interval of the Return on Investment (ROI) values from archival data at the test site ranged between [0,5], displaying a concentrated pattern that aligns with the typical economic value distribution. The integration of AI technology in archive management not only streamlines processes but also maximizes resource utilization and enhances returns.

Research on nonlinear corrosion law of epoxy coating on steel bridges based on image processing

This paper adopted the accelerated corrosion test method to study the corrosion law of epoxy coatings on steel bridges. An image processing algorithm based on MATLAB was used to perform grayscale conversion, normalized histogram drawing, and binary image processing on the surface morphology images of coated specimens. Four coated specimens were prepared and placed in the cyclic accelerated corrosion environment. The coated specimens were subjected to the accelerated corrosion test for 90 days, observed changes in coating thickness and corrosion area rate and analyzing the corrosion laws of epoxy coatings in different corrosion environments. Using image processing technology to process images of coating corrosion and analyze the corrosion mechanism of coatings. The research results indicate that the corrosion rate of epoxy coatings exhibits a non-linear corrosion law of slow corrosion in the early stage and accelerated corrosion in the middle and later stages. The corrosion rate of epoxy coatings in saltwater corrosive environments is faster than in freshwater corrosive environments. Based on the corrosion laws and mechanisms of coatings, a nonlinear corrosion function model for epoxy coatings on steel bridges under different accelerated corrosion environment was established, which has a high correlation with experimental data

Face Recognition using SVM with the Help of Kernel Characteristics Function

Abstract: This project focuses on building a face recognition system using the Labeled Faces in the Wild (LFW) dataset.The dataset includes diverse facial images of individuals across various age groups, such as kids, women, men, and the elderly. The model employs a Support Vector Machine (SVM) with a radial basis function (RBF) kernel for effective classification. Preprocessing techniques like normalization, grayscale conversion, and face alignment enhance the dataset's quality. The system's performance is assessed using accuracy, precision, recall metrics, and a Receiver Operating Characteristic (ROC) curve. The project aims to create a reliable and versatile face recognition solution applicable in security, authentication, and surveillance scenarios

COMPREHENSIVE POTHOLE DETECTION SYSTEM FOR ROAD MAINTENANCE AND SAFETY USING IMAGE PROCESSING AND STEREO VISION

This paper introduces a systematic approach to pothole detection, emphasizing its significance in road maintenance and safety. The proposed system is comprised of meticulously designed modules that collectively contribute to achieving accurate and effective results. Commencing with data collection through cameras capturing road areas of interest, the images undergo manual cropping and resizing for standardization. The core principle of the system revolves around image enhancement, involving grayscale conversion, application of blurring techniques, and adjustments to brightness and contrast. Automatic thresholding extracts essential information encoded within pixels, paving the way for precise edge detection refined through morphological operations. The focus then shifts to pothole detection, incorporating a stereo camera setup to calculate depth and disparity of road surfaces. Critical steps, including image rectification, correspondence matching, and disparity calculations, contribute to the accurate identification and delineation of potholes using depth thresholds. Subsequent modules employ K-Means clustering to segregate regions of interest from the image background, and post-processing steps, including filtering, masking, and refinement, fine-tune the images. Utilizing the HSV color space for grayscale image refinement and the connected component method to isolate white pixel objects further enhance the system's capability. The final step involves the addition of bounding boxes around identified potholes, streamlining their identification process. This comprehensive methodology ensures effective image processing, precise pothole detection, and contributes significantly to road safety and maintenance efforts.

COUNTERFEIT CURRENCY DETECTION USING MACHINE LEARNING

This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing. Keywords: KNN, MobileNetV2, Image Processing, Machine learning algorithms, Feature extraction.

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

Adaptive enhancement method of irregular low-pixel architectural design image based on lightness component

ABSTRACT This study explores adaptive enhancement for irregular, low-pixel architectural design images, focusing on lightness components. Utilizing a median filter and wavelet threshold method removes image noise, followed by grayscale conversion in HSV space. Applying Retinex theory generates the initial enhanced image, further improved through wavelet transform image fusion technology, resulting in the final enhanced architectural design image. Experimental results demonstrate the method's effectiveness in adapting and enhancing irregular, low-pixel architectural design images, achieving notably improved outcomes. The proposed approach successfully extracts and utilizes valuable image information, enhancing overall image quality. This study found that the proposed method can effectively extract and better utilize useful information in images, achieving more ideal image enhancement effects.

Dual‐granularity feature fusion in visible‐infrared person re‐identification

AbstractVisible‐infrared person re‐identification (VI‐ReID) aims to recognize images of the same person captured in different modalities. Existing methods mainly focus on learning single‐granularity representations, which have limited discriminability and weak robustness. This paper proposes a novel dual‐granularity feature fusion network for VI‐ReID. Specifically, a dual‐branch module that extracts global and local features and then fuses them to enhance the representative ability is adopted. Furthermore, an identity‐aware modal discrepancy loss that promotes modality alignment by reducing the gap between features from visible and infrared modalities is proposed. Finally, considering the influence of non‐discriminative information in the modal‐shared features of RGB‐IR, a greyscale conversion is introduced to extract modality‐irrelevant discriminative features better. Extensive experiments on the SYSU‐MM01 and RegDB datasets demonstrate the effectiveness of the framework and superiority over state‐of‐the‐art methods.

Advanced Detection of Document Tampering Using Structural Similarity Index and Image Analysis Techniques

This research focuses on the application of the Structural Similarity Index (SSIM) technique for detecting tampering in various identity documents, such as PAN cards, Aadhar cards, and voter IDs. The SSIM method is employed to assess the structural similarity between the original and provided document images. Additionally, grayscale conversion and thresholding techniques are utilized to analyze shapes and contours, further aiding in the identification of tampered areas. Experimental results show that a low SSIM score indicates potential tampering in the provided image. Visualizations, including contour overlays, difference maps, and threshold-based comparisons, enhance the clarity of differences between original and tampered document images.

An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm

This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.

Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends

ABSTRACT This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the feasibility of using these two simple multivariate statistical techniques for image classification.

Automated Disease Severity Measurement Using Image Processing

Apple orchards are vital to the agricultural industry, but they face constant threats from diseases like apple scab, powdery mildew, rust, and rot. Traditional methods for measuring disease severity in apple leaves are labour-intensive, subjective, and prone to errors, limiting their practicality for large-scale agriculture. This paper introduces an innovative approach leveraging image processing techniques to automate disease severity measurement in apple leaves, offering a transformative solution for orchard management. The research aims to provide an efficient and accurate method for assessing disease severity, addressing the critical need for precise disease management and improved crop yields. Current methods relying on visual inspections by human experts are inefficient and unreliable. In contrast, the proposed method automates the measurement process, saving time and resources, enabling frequent monitoring, and enhancing early disease detection. This automation, in turn, leads to more effective disease management and increased crop yield and quality. The methodology involves image preprocessing, grayscale conversion, histogram equalization, thresholding, and defect area extraction, ultimately resulting in a quantitative disease severity index. The study utilizes a diverse dataset of apple leaf images, allowing for robust model development capable of accurate disease detection under varying conditions. This research holds significant implications for disease management and crop yield optimization, with broader applicability across agriculture. It paves the way for precision agriculture by enabling data-driven decisions on disease control measures while reducing the environmental impact of chemical inputs. Furthermore, the image processing techniques developed here can be adapted to other crops and plant diseases, promising efficient and accurate disease severity measurement methods across agricultural domains.

AI Based Tamil Palm Leaf Character Recognition

A brand-new character recognition method has been created especially for Tamil manuscripts written on palm leaves. A variety of preprocessing techniques are used to obtain reliable recognition because these manuscripts are complicated and have age-related deterioration. Using cutting-edge image processing techniques, manuscript photos are enhanced and preprocessed in the earliest stage to improve clarity. Then, in accordance with the distinctive arrangement of these historical artifacts, these photos are divided into three separate portions. After segmentation, these parts are converted to grayscale to create the ideal contrast needed for character detection. Noise canceling algorithms have been incorporated to prevent any distortions or degradations that may occur as a result of the age of the manuscripts or other environmental influences. After this mitigation, the photos are binarized to emphasize the isolation of the figures from their surroundings. This makes sure that each character stands out clearly, laying the groundwork for the crucial next step: segmenting individual characters. The foundation of the training phase is this segmentation. Convolutional neural networks (CNNs), which excel at handling image-based tasks, were incorporated into the deep learning architecture used for model building. Utilizing a sizable dataset compiled from numerous articles, the model underwent intensive training. As a result, the model was able to recognise characters from these texts with an astounding accuracy rate of 89%. With the help of Flask, the complete system has been easily integrated into a user-friendly web interface. This platform's goal is quite clear: fusing the elegance and profundity of ancient scripts with the effectiveness of contemporary technology. It is positioned as a crucial tool for both academic research and teaching purposes. By doing this, it guarantees that the priceless history and knowledge found in these manuscripts remain accessible and lays the groundwork for their preservation for future generations. Keywords— Character recognition, Tamil palm manuscripts, Preprocessing, Visual clarity, Grayscale conversion, Noise cancellation, Binarization, Segmentation, Training, Flask-based web interface, Research, Education.

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment

Digit Recognition Using Local Projection Dependent Clustering

Water companies utilize water meters to measure and calculate water usage bills. However, the current process employed by PDAM requires redundant resources, as it involves taking photos of each customer's house and having other officers read the numbers from the water meter images, resulting in inefficiency. The problem is further compounded by the neglect and improper maintenance of water meters, with some being buried in garbage or soil. Additionally, officers contribute to the challenges by capturing blurry and tilted photos, hindering the accurate reading of the water meter numbers. This study applies a water meter reading system by processing water meter photos and converting them into text using image processing methods to process images and Neural Networks to perform digit recognition. The image processing process includes steps such as (1) grayscale conversion, (2) gamma correction, (3) x-Histogram Projection, (4) White Temporal Ascent Accumulation, and (5) Peak Identification. Furthermore, image segmentation techniques are applied to enhance image quality and eliminate noise using clustering methods. The segmented images are then processed by a neural network to recognize the meter digits. The system achieves a digit recognition accuracy of 75.2%, despite encountering various technical and non-technical challenges during the water meter photo capture process.

A new deep learning approach based on grayscale conversion and DWT for object detection on adversarial attacked images

A new deep learning approach based on grayscale conversion and DWT for object detection on adversarial attacked images

Text Recognition Using Image Processing Technology for Visiting Card

Abstract: Image recognition and optical character recognition technologies have become an integral part of our daily lives due to increasing computing power and the proliferation of scanning devices. A printed document can be quickly converted to a digital text file using optical character recognition and edited by the user. The time required to digitize documents is therefore minimal. This is especially useful when archiving large print volumes. In this study, we show how image processing techniques can be used in combination with optical character recognition to improve recognition accuracy and improve efficiency in extracting text from images. Two of his software systems are developed and tested in this study: a character recognition system applied to cosmetics-related advertising images and a recognition and text recognition system for natural scenes. Experimental results show that the proposed system can accurately recognize text in images. Keywords: Image processing, Text detection, Visiting card, Optical character recognition (OCR),Named entity recognition (NER),Computer vision, OpenCV, Pytesseract, Tesseract OCR engine, Grayscale conversion, Noise removal, Thresholding, Named entities, Business name extraction, Owner name extraction, Address extraction, Contact number extraction, Email address extraction, Website extraction, Data categorization, Information extraction, Data organization, Spreadsheet integration, Accuracy enhancement, Efficiency improvement, Error reduction, Python programming, Data processing, Data extraction, Text extraction

An approach for improving Optical Character Recognition using Contrast enhancement technique

Digital cameras and mobile image capture of documents are two examples of new developments in the fields of optical character recognition and text recognition. Scans of text or text photographic images and even natural photography results can be distorted to the point where OCR digitization is inaccurate. It offers a unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.

Deep Learning Res-U-Net Model for Retinal Vascular Segmentation

A characteristic of retinal vessels is a crucial representation of many disorders linked to retinal blood vessels, which are frequently significant for diagnosis. These methods have less of an impact on lower segmentations because they require manual annotations. The interfaces that will make the benefits of employing the RES-UNET model for segmenting retinal blood vessels easier. overcome the time-consuming manual annotation method, and describe the different uncommon features, which are scarce in number. The numerous details are viewed as a resource that will be further improved to meet accuracy requirements and complete the segmentation process without any overt interruptions. High contrast and noisy images make them more tedious and time-consuming. To overcome this, we propose a combined model of Res-U-Net to enhance the quality of fundus images that includes pre-processing, grayscale conversion, and the implementation of deep learning techniques using two-chained convolutional neural networks (CNNs).

Counterfeit currency detection using machine learning

This paper addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) and the efficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing.