Showing papers on "Image processing published in 2022"

Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing

Abstract: This paper surveys recent advances in pulse-coupled neural networks (PCNNs) and their applications in image processing. The PCNN is a neurology-inspired neural network model that aims to imitate the information analysis process of the biological cortex. In recent years, many PCNN-derived models have been developed. Research aims with respect to these models can be divided into three categories: (1) to reduce the number of manual parameters, (2) to achieve better real cortex imitation performance, and (3) to combine them with other methodologies. We provide a comprehensive and schematic review of these novel PCNN-derived models. Moreover, the PCNN has been widely used in the image processing field due to its outstanding information extraction ability. We review the recent applications of PCNN-derived models in image processing, providing a general framework for the state of the art and a better understanding of PCNNs with applications in image processing. In conclusion, PCNN models are developing rapidly, and it is projected that more applications of these novel emerging models will be seen in future.

Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Abstract: Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

Image-to-Image Translation: Methods and Applications

Abstract: Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, segmentation, style transfer, restoration, and pose estimation. In this paper, we provide an overview of the I2I works developed in recent years. We will analyze the key techniques of the existing I2I works and clarify the main progress the community has made. Additionally, we will elaborate on the effect of I2I on the research and industry community and point out remaining challenges in related fields.

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

Abstract: We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LABKIT is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LABKIT is easy to install on virtually all laptops and workstations. Additionally, LABKIT is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LABKIT via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Finally, LABKIT comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LABKIT in a number of practical real-world use-cases.

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Abstract: One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

A study on image processing with data analysis

Abstract: Image analysis is from images extracting meaningful information; mainly through digital image processing techniques from digital images. In this context, Tasks menu coded the tags are as simple as reading or a person from their face as sophisticated as identification. This paper presentation is made on various techniques used in analyzing images,like Computer view is from digital images or videos Dealing with how computers can gain a higher level of understanding Is an intermediate field of science, Machine vision (MV) is for applications such as automation Used to provide imaging based automation analysis and analysisTechnology and methods. Study, process control and robot guidance. Format authentication is the automatic recognition of methods and regulations in data. This includes statistical data analysis, signal processing, and Contains applications in image analysis. The dividing a digital several, the innovations are detailed in more detail in the text.

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

Abstract: Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Abstract: Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.

Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning

Abstract: Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Image analysis demonstrated the difficulty of processing the captured image under variant illumination in natural environment and with textured objects of non-ideal geometric shapes. However, this is subjected to certain consideration settings and image-processing algorithms. The study presents an automatic method for identifying and recognising the pineapple’s crown images in the designated plot using image processing and further counts the detected images using machine learning classifiers namely artificial neural network (ANN), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision trees (DT) and k-nearest neighbours (KNN). The high spatial-resolution aerial images were pre-processed and segmented, and its extracted features were analysed according to shape, colour and texture for recognising the pineapple crown before classifying it as fruit or non-fruit. Feature fusion using one-way analysis of variance (ANOVA) was incorporated in this study to optimise the performance of machine learning classifier. The algorithm was quantitatively analysed and validated for performance via accuracy, specificity, sensitivity and precision. The detection for the pineapple’s crown images with ANN-GDX classification has demonstrated best performance fruit counting with accuracy of 94.4% and has thus demonstrated clear potential application of an effective RGB images analysis for the pineapple industry.

Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques

Abstract: The inspection of sewing defects is an essential step in the quality assurance of garment manufacturing. Although traditional automated defect detection applications have shown good performance, these methods are usually configured with handcrafted features designed by a human operator. Recently, deep learning methods that include Convolutional Neural Networks (CNNs) have demonstrated excellent performance in a wide variety of computer-vision applications. To take advantage of the CNN’s feature representation, the direct utilization of feature maps from the convolutional layers as universal feature descriptors has been studied. In this paper, we propose a sewing defect detection method using a CNN feature map extracted from the initial layers of a pre-trained VGG-16 to detect a broken stitch from a captured image of a sewing operation. To assess the effectiveness of the proposed method, experiments were conducted on a set of sewing images, including normal images, their synthetic defects, and rotated images. As a result, the proposed method detected true defects with 92.3% accuracy. Moreover, additional conditions for computing devices and deep learning libraries were investigated to reduce the computing time required for real-time computation. Using a general and cheap single-board computer with resizing the image and utilizing a lightweight deep learning library, the computing time was 0.22 s. The results confirm the feasibility of the proposed method’s performance as an appropriate manufacturing technology for garment production.

Computing metasurfaces for all-optical image processing: a brief review

Abstract: Abstract Computing metasurfaces are two-dimensional artificial nanostructures capable of performing mathematical operations on the input electromagnetic field, including its amplitude, phase, polarization, and frequency distributions. Rapid progress in the development of computing metasurfaces provide exceptional abilities for all-optical image processing, including the edge-enhanced imaging, which opens a broad range of novel and superior applications for real-time pattern recognition. In this paper, we review recent progress in the emerging field of computing metasurfaces for all-optical image processing, focusing on innovative and promising applications in optical analog operations, image processing, microscopy imaging, and quantum imaging.

Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis

Abstract: Fruits are vulnerable to mechanical damages and physiological disorders caused by the static and dynamic forces acting on them during transportation and abiotic stresses throughout their growth and development, respectively. Identifying these defects is central to quality monitoring in the fruit processing industry. Conventionally, industries employ manual separation to segregate damaged fruits in the processing line. However, manual sorting is laborious, time-consuming, skilled labor-intensive, and destructive. Besides, it is incapable of inspecting every fruit on a fast-moving conveyor belt. Therefore, industries are looking for rapid, non-destructive, and precise technologies for the online inspection of every fruit in the process line. Non-destructive techniques (NDTs) such as biospeckle, X-ray imaging, hyperspectral imaging (HSI), and thermal imaging (TI) involve noninvasive testing of sample surfaces. Earlier review articles have emphasized the applications of various NDTs in determining fruit quality and safety, but with limited focus on image processing and analysis. Therefore, this review focuses on the working principle of these NDTs in detecting fruit damages, their instrumentation, and the steps involved in image processing and analysis. The final sections highlight the limitations and future prospects pertaining to each technique. Biospeckle, HSI, and TI techniques can detect surface damages due to their limited light penetration depth. The HSI spectrum is useful in detecting the defects and fruit quality parameters. Active TI can detect even minor damages in the fruit, but it is not appropriate for industrial production lines. Conversely, X-ray imaging can detect fruit internal damages. The synergistic applications of these NDTs along with appropriate chemometric procedures are useful in identifying damaged fruits without human interference and evade their entry into the processing line.

An Automatic Procedure for Overheated Idler Detection in Belt Conveyors Using Fusion of Infrared and RGB Images Acquired during UGV Robot Inspection

Abstract: Complex mechanical systems used in the mining industry for efficient raw materials extraction require proper maintenance. Especially in a deep underground mine, the regular inspection of machines operating in extremely harsh conditions is challenging, thus, monitoring systems and autonomous inspection robots are becoming more and more popular. In the paper, it is proposed to use a mobile unmanned ground vehicle (UGV) platform equipped with various data acquisition systems for supporting inspection procedures. Although maintenance staff with appropriate experience are able to identify problems almost immediately, due to mentioned harsh conditions such as temperature, humidity, poisonous gas risk, etc., their presence in dangerous areas is limited. Thus, it is recommended to use inspection robots collecting data and appropriate algorithms for their processing. In this paper, the authors propose red-green-blue (RGB) and infrared (IR) image fusion to detect overheated idlers. An original procedure for image processing is proposed, that exploits some characteristic features of conveyors to pre-process the RGB image to minimize non-informative components in the pictures collected by the robot. Then, the authors use this result for IR image processing to improve SNR and finally detect hot spots in IR image. The experiments have been performed on real conveyors operating in industrial conditions.

Robust Image Encryption and Zero-watermarking Scheme Using SCA and Modified Logistic Map

Abstract: In this work, we first present a modified version of the traditional logistic chaotic map. The proposed version contains an additional parameter that is used to increase the security level of the proposed digital image copyright protection scheme. The latter merges two methods of image copyright protection, namely the image zero-watermarking and image encryption, which provides a high level of security when communicating images via the Internet. Next, we discuss the influence of geometric attacks on the efficiency of the proposed scheme, and then we introduce an efficient solution that can resist such attacks. The proposed solution involves the use of Sine Cosine Algorithm (SCA) with an appropriate algorithm suitable for the correction of geometric attacks (image translation, orientation and its combination) applied to the encrypted image. On the one hand, the simulation results show that the proposed scheme provides a high level of security and can resist various attacks (differential, common image processing, geometric, etc.). On the other hand, the conducted comparison in terms of robustness against geometric attacks clearly demonstrates the superiority of our scheme over recent image encryption ones.

A comparative study of x-ray and ct images in covid-19 detection using image processing and deep learning techniques

Abstract: The deadly coronavirus has not just devastated the lives of millions but has put the entire healthcare system under tremendous pressure. Early diagnosis of COVID-19 plays a significant role in isolating the positive cases and preventing the further spread of the disease. The medical images along with deep learning models provided faster and more accurate results in the detection of COVID-19. This article extensively reviews the recent deep learning techniques for COVID-19 diagnosis. The research articles discussed reveal that Convolutional Neural Network (CNN) is the most popular deep learning algorithm in detecting COVID-19 from medical images. An overview of the necessity of pre-processing the medical images, transfer learning and data augmentation techniques to deal with data scarcity problems, use of pre-trained models to save time and the role of medical images in the automatic detection of COVID-19 are summarized. This article also provides a sensible outlook for the young researchers to develop highly effective CNN models coupled with medical images in the early detection of the disease.

A Conceptual Investigation on the Image Processing using Artificial Intelligence and Tensor Flow Models through Correlation Analysis

Abstract: Artificial intelligence plays a vital role in image processing and tensor flow models. Due to increasing demand of generating data from image as well as to make distinction between images the necessity and relevance of these two topics are also growing. Image classification and processing is a recent trend where traffic lights and other objects are identified by Machine Learning (ML) and Deep Learning (DL) technologies. These technologies are further implemented inside self-driven vehicles for developing autonomous processing's and information transfer. However, an accuracy and speed of detection process is a concern in today's world. Thus, this study paper provides an insight into image processing accuracy by “single-stage object detection model” or SSD MobileNetV2 and “Regional-based Convolutional Neural Network” or RCNN. Pearson correlation analysis and mean was obtained using the input data. The output data were further analysed and discussed. Findings suggested that RCNN is 80.90% accurate when it is allowed to detect traffic lights present at 11.9 metres apart; whereas SSD MobileNet V2 has shown 71.8% of accuracy when the traffic light was detected present at the same distance. On average, the RCNN shows higher accuracy than SSD MobileNet V2. The discussion found that RCNN is more accurate and slower than SSD MobileNetV2.

A Novel Text Recognition Scheme using Classification Assisted Digital Image Processing Strategy

Abstract: In the industry of Digital Image Processing, Text Recognition is an important task due to the significance of many classical records available today is in the format of paper record. The main objective of such text recognition schemes are transforming the textual records from hard copy to the system oriented records, in which it will be easier to maintain it into the database or any server entities in easy way. This paper is intended to design a novel text recognition using digital images taken from any camera with a set of different pixel densities. In this paper, a Logical Text Classification Strategy (LTCS) is introduced to perform an effective text recognition process using digital images. The proposed LTCS process the input image based on certain characteristics such as: Image Pre-Processing, Segmenting the Image, Extracting the Features, Classification Principle and the Image Post-Processing. These are all the different steps involved in the processing of proposed text recognition approach. For information indexing and search applications, characters in the document implanted in images portray a valuable source of data. Moreover, owing to the unique dimensions, gray - scale true values and background clutter, these text words/characters are harder to identify and recognize. This paper analyses approaches for designing an integrated implementation tool for classifying and recognizing text hidden in digital images that has any grey scale value. In this process two main considerations are important, such as text identification and text recognition, both of these empirical image processing techniques and quantitative classification approaches are investigated in this paper. The resulting section shows the processing efficiency, time required to process the digital image and character recognition efficiency in clear manner using graphical representations.

Underwater image enhancement using adaptive color restoration and dehazing.

Abstract: Underwater images captured by optical cameras can be degraded by light attenuation and scattering, which leads to deteriorated visual image quality. The technique of underwater image enhancement plays an important role in a wide range of subsequent applications such as image segmentation and object detection. To address this issue, we propose an underwater image enhancement framework which consists of an adaptive color restoration module and a haze-line based dehazing module. First, we employ an adaptive color restoration method to compensate the deteriorated color channels and restore the colors. The color restoration module consists of three steps: background light estimation, color recognition, and color compensation. The background light estimation determines the image is blueish or greenish, and the compensation is applied in red-green or red-blue channels. Second, the haze-line technique is employed to remove the haze and enhance the image details. Experimental results show that the proposed method can restore the color and remove the haze at the same time, and it also outperforms several state-of-the-art methods on three publicly available datasets. Moreover, experiments on an underwater object detection dataset show that the proposed underwater image enhancement method is able to improve the accuracy of the subsequent underwater object detection framework.