Digital Image Processing System Research Articles

Research on high-precision digital image correlation measurement techniques for highly stable structures

Abstract This study proposes a novel digital image processing system that combines a diffraction-limited resolution (DLRF)-based measurement technique with a windowed form-center tracking algorithm. To evaluate the accuracy of this system, this paper compares and analyzes the effectiveness of conventional digital image techniques and DLRF-based methods for deformation displacement measurements. In addition, the study includes thermal stability tests under ambient noise and uniform high temperature conditions to evaluate the stability performance of the system in a complex environment. The experimental results show that the DLRF-based digital image correlation method proposed in this study performs well in reducing the mean deviation (from a maximum of 5.17 × 10-3 to 1.73 × 10-3) and root-mean-square error (from a maximum of 5.14 × 10-3 to 0.75 × 10-3). It is worth noting that the DLRF method is faster in processing when using the single-precision format than the double-precision format, with a speedup of up to 1.05 times. In addition, the multiple displacement averaging processing method can effectively filter the noise in the test, and the noise effect is only in the range of 0 to 2 μm in most areas. In the analysis of test points 10-34 and 57-80, the displacement error is controlled within 5 μm, indicating that the modified structural analysis model can be used for on-orbit micrometer-scale thermal deformation analysis. The study proves the high accuracy and stability of the digital image system proposed in this paper in the measurement of deformation displacement, which provides adequate technical support for accurate measurement in related fields.

Design of real-time digital image processing system based on high performance computing

Design of real-time digital image processing system based on high performance computing

Automatic Contouring Analysis of Lung Cancer for Radiotherapy Radiation Planning from Ct-Simulator Image

Accurate determination of tumor and radiotherapy target volume is crucial to prevent local and regional failure in lung cancer. Radiotherapy planning considers critical normal tissue structures or Organs at Risk (OAR). While manual contouring is the gold standard in radiotherapy planning, it is still susceptible to intra- and inter-observer variation. A digital image processing system capable of automatically and semi-automatically determining tumor targets and OARs through computer programming can assist in the contouring process. The study aims to create a digital contouring computer program to aid in planning radiotherapy irradiation for lung cancer cases, guided by CT-Simulator image guidance. The research involved contouring CT-Simulator images of lung cancer cases using automatic segmentation, semi-automatic segmentation, and a combination of automatic, semi-automatic, and manual segmentation. Contour accuracy was assessed by a radiation oncologist. Automatic segmentation showed high accuracy for lungs (>95%), moderate for tumor targets (72.22%) and the heart (73.33%), but low accuracy for the esophagus (34.44%) and spinal cord (36.67%). Semi-automatic segmentation achieved high accuracy for the esophagus (96.67%) and spinal cord (95.56%), and moderate accuracy for tumor targets (73.33%), heart (66.67%), right lung (66.25%), and left lung (72.94%). The combination of automatic, semi-automatic, and manual segmentation resulted in high accuracy for tumor targeting and OARs (>95%). Automatic or semi-automatic segmentation using location and gray level classification methods for lung cancer cases did not produce optimal results. However, a contouring program combining automatic, semi-automatic, and manual segmentation proved more effective, balancing human interaction and automation in the lung cancer contouring process. This digital contouring program offers valuable support for radiation oncologists, potentially leading to improved treatment outcomes for lung cancer patients

Secure transmission for IoT wireless energy-carrying communication systems.

The wireless energy-carrying communication method for the Internet of Things (IoT) presents several difficulties for information security such as eavesdropping or data loss. To solve these issues, this paper presents a new secure transmission method for IoT wireless energy-carrying communication systems. In this method, first the secret message is turned into a word, delivered to the intended recipient and unlawful listener, respectively, and the received message is characterized as an entropy function. The message is iteratively solved using the block coordinate descent technique, and for each iteration, a digital baseband signal containing the receiver's secret message symbol and the matching beamforming vector is delivered. By concurrently optimizing the transmit beamforming vector, the noise covariance matrix, and the receiver power allocation factor based on a design that complies with the security rate and energy acquisition limitations for each receiver, the overall system transmit power is reduced. The Lagrangian method is used to solve the secure transmission problem of the communication system based on an iterative block coordinate descent algorithm, as well as to change the nonconvex problem into a convex problem and precisely derive the upper and lower bounds of the original transmission problem. In comparison to the conventional policy transmission scheme, the experimental results demonstrate that the DIPS (Digital Image Processing System) scheme can increase the STP (Signaling Transfer Point) by approximately 34.16 percent in the eavesdropper independent eavesdropping and joint eavesdropping scenarios. The usefulness of the secure transmission strategy for wireless energy-carrying communication systems is confirmed by this investigation.

Design of Digital Image Processing System Based on Machine Vision

Digital image processing technology is a technology that has developed rapidly in recent years[1]. A series of images are processed by computers to improve the visual effect of the image in order to achieve the desired presentation effect. Digital image processing technology has been continuously developed with the innovation of computers, and its application range has expanded from the original image field to traffic command, signal processing, medical treatment and other fields closely related to people's lives. With the support of many algorithms, the speed of digital image processing technology is getting faster and faster, which brings a lot of convenience to people's lives, and which becomes an indispensable technology in life. With the help of MATLAB's GUI user interface, image processing toolbox and MATLAB's comprehensive mathematical function library, this article designs an image processing operating platform that realizes some image processing functions after repeated trials.

Performance Analysis of Graph theory-based Contrast Limited Adaptive Histogram Equalization for Image Enhancement

Nowadays, image enhancement has become a major area of research because of the development of applications that are based on vision.Several digital image processing systems employ such image enhancement strategies with the help of graph theory. As the visibility level in low contrast image features is very less,several image enhancement strategies have been introduced with spatial transformations to enhance image qualityfor improved visualization. Nowadays, image processing plays an important role in the analysis of a patient’s health status and has become extremely popular in medical areas for a wide range of clinical assessments. Generally, medical images contain several complex areas and thereby,few pre-processing approaches are applied to reduce the challenges that occur during different phases of the CAD system. Furthermore, because of external noise interferences, poor illuminating settings as well as other imaging device limitations, the clinical diagnosis becomes a challenging process and medical images do not provide important information for precise categorization. Medical images are available in a variety of applications such as computed tomography, Magnetic Resonance Imaging (MRI), mammography, chest X-ray (CXR), and many more. Only the pixel intensity variations between different areas as well as object boundary information are essential for categorization and must be enhanced simultaneously. As a result, the rate of classification in medical images and intensity are increased so that every object during the analysis can be easily identified. The main goal of any image enhancement process is to enhance the quality of the image by reducing noise and on other hand by using three different algorithms such as Luminance Modulation (LM), Gradient Modulation (GM), and Dynamic Histogram Equalization (DHE). These three algorithms are designed with the help of graph theory for effective preservation of edges, losses, and efficient smoothing and to preserve the basic information without any modifications. Image restoration is also referred to as image enhancement and it is concerned with the precise assessment of real images. Generally, the degradation process is not included in many of the image-enhancement approaches that are already existing. Furthermore, with the application of enhancement techniques, the degradation process for medical images results in some significant performance loss. Several techniques have been proposed and the technique which is examined in this research is image enhancement that is based on histogram which mainly concentrates on equalizing the histogram of values. Histogram Equalization (HE) possesses a few basic properties such as altering spatial patterns as well as intensity which in turn results in significant challenges in medical imaging. As a result,Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed in this work as a feasible approach for medical image analysis to address the problem. The suggested research work demonstrates that the intensity limiting image enhancement with histogram equalization detects the irregularities in dense mammograms with enhanced quality.

Evaluation of edge detection algorithm of frontal image of facial contour in plastic surgery

With the improvement of medical levels and the continuous improvement of people’s living standards, the demand for beauty by the general public is increasing. The plastic surgery industry has also developed by leaps and bounds. People’s dissatisfaction with their own facial appearance, facial injuries and some other reasons have prompted people to carry out facial reconstruction, and facial plastic surgery has developed rapidly. However, in the current facial plastic surgery, the edge detection effect on the contour image is general. In order to improve the edge detection effect of facial contour lines in medical images, this paper proposed a facial contour line generation algorithm. First, the detection effects of four operators were compared. After comparing the effects, the Sobel operator was used as the input data to generate an edge detection algorithm. Then, the grayscale features of the tissue in the image and the symmetry of the image were used to perform bidirectional contour tracking on the detected image to extract facial contour lines. In addition, for facial contour features, the midpoint method can be used to generate auxiliary contours. The algorithm was verified by a set of facial CT (Computed Tomography) images in the experiment. The results showed that the new generation algorithm accelerated the edge detection speed, had good denoising performance, and enhanced the edge detection effect by about 12.05% compared with the traditional edge detection algorithm. The validity and practicability of facial edge detection were verified, and it provided a theoretical basis for further realizing the design of a facial contour digital image processing system.

High-performance digital image filtering architectures in the residue number system based on the Winograd method

Continuous improvement of methods for visual information registration, processing and storage leads to the need of improving technical characteristics of digital image processing systems. The paper proposes new high-performance digital filter architectures for image processing by the Winograd method with calculations performed in a residue number system with special-type moduli. To assess the performance and hardware costs of the proposed architectures, hardware simulation is carried out using a field-programmable gate array in a computer-aided design envi-ronment Xilinx Vivado 2018.3 for the target device Artix-7 xc7a200tffg1156-3. The results of hardware simulation show that the proposed filter architectures have 1.13 – 5.42 times higher performance, but require more hardware costs compared to the known methods. The results of this study can be used in the design of complex systems for image processing and analysis for their performance to be increased.

Integrated representation for discrete Fourier and wavelet transforms using vector notation

Many mathematical operations are implemented easily through transform domain operations. Multiple transform domain operations are used independently in large and complex applications. There is a need to develop integrated representations for multiple transform domain operations. This paper presents an integrated mathematical representation for the discrete Fourier transformation and the discrete wavelet transformation. The proposed combined representation utilizes the powerful vector notation. A mathematical operator, called the star operator, is formulated that merges coefficients from different transform domains. The star operator implements both convolution and correlation processes in a weighted fashion to compute the aggregated representation. The application of the proposed mathematical formulation is demonstrated successfully through merging transform domain representations of time-domain and image-domain representations. Heart sound signals and magnetic resonance images are used to describe transform-domain data merging applications. The significance of the proposed technique is demonstrated through merging time-domain and image-domain representations in a single- stage that may be implemented as the primary processing engine inside a typical digital image processing and analysis system.

ANALISIS KARAKTERISTIK SEGMEN PADA CITRA MAMOGRAFI DENGAN MENGGUNAKAN METODE SEGMENTASI WATERSHED

Breast cancer is the most common cancer and is a major cause of women worldwide. Early examination of breast cancer is done by mammography. Mammographic images were analyzed manually by radiologists. That is in the manual system, but can help with digital image processing systems known as Computer-Aidid Diagnostics (CAD). One of the digital image processing is segmentation. segmentation is an object for object objects with a background (background) contained in an image. Segmented segmentation, namely watershed. Image segmentation is presented as a different area from the initial image as relief topography. This study uses 125 images which are divided into 100 image databases and 25 test data images. After testing with watershed, measurements were taken using Receiver Operating Characteristic (ROC). Based on testing, obtained pixel values ​​and minor and major ratios of 171.72 and 0.64. The values, sensitivity and specificity of the program were 46.00%, 45.00% and 68.00% respectively.

Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis

To learn about electronic devices, one must know about the types of electronic components. Capacitors are one of several important electronic components. Capacitors are part of passive components that are able to store energy or electric charge at a temporary time. Capacitors or commonly called capacitors have many types. However, some people do not know about these types of capacitors. Especially for someone or a student who will learn about electronic components. The purpose of this study is to develop a digital image processing system for the classification of transistor types by applying the K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) methods. PCA serves to reduce and retain most of the relevant information from the original features according to the optimal criteria. Based on the results of feature extraction and data reduction performed by PCA, it is easier for the KNN algorithm to classify. KNN performs a classification based on the data closest to the object being processed. Based on the test results, the developed model is able to produce an average accuracy value of 82.50%. This means that PCA and KNN algorithms can be used in the process of classifying capacitor type images properly

Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

Noise interference during the acquisition of digital images can severely degrade image quality, particularly for images captured under low-light conditions; however, the removal of image noise requires sophisticated digital image processing systems. This study presents a hardware-based solution to real-time image denoising using an existing algorithm designed for the removal of mixed impulse noise (salt-and-pepper and random-valued impulse noise), while preserving image edge details and image borders, without the need for additional computation time or memory capacity. Note that mixed impulse noise is typical of most real-world situations, such as the video noise associated with dashboard cameras. The proposed design was implemented using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, consuming only 21.7 mW when operated at 200 MHz. This operating frequency allows the proposed chip to process noisy video streams with resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\mathbf {\times }1080$ </tex-math></inline-formula> at 60 frames per second in real time. In terms of image restoration, the proposed algorithm achieved image quality on par with that achieved using software simulation. We also demonstrated the efficacy of the proposed scheme in denoising noisy video images from a dashboard camera.

A speed inference planning of groove cutting robot based on machine vision and improved fuzzy neural network

To ensure the cutting speed during the cutting operation, this paper proposes a groove cutting speed inference planning system that relies on production experience and set parameters and is based on machine vision and a two-level fuzzy neural hybrid network. The overall structure of the inference system is designed, including the mechanical body, vision system, and fuzzy neural hybrid network. The contour information of the part is obtained using industrial cameras and digital image processing systems. The cutting speed of the trajectory segment is inferred based on the related processing parameters and the secondary fuzzy neural hybrid network. Finally, all of the processing parameters are transmitted to the PLC, so that the robot can work according to the predetermined displacement and speed. Simulations verify that the speed inference planning system offers certain advantages compared to the traditional one. The appearance of the speed inference planning realises independent design and planning of the cutting speed, and further ensures the unity of the cutting quality and cutting speed. This proposed method provides a new direction for the development and transformation of machining processes that rely on manual experience and in which expert systems cannot be used.

RETRACTED ARTICLE: Research on varietal classification and germination evaluation system for rice seed using hand-held devices

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Labelled Image Dataset Preparation for Rice Seed Germination Prediction and Variety Classifictaion using Low Cost Devices

Machine vision and Digital Image processing systems widely used in many of the applications like Quality control, Classification, Recognition, Identification, Security etc. For the implementation of machine vision system and machine learning, system must be feed by collection of images. Collecting the required images for the system are time consuming and complex task. We can collect the images from internet, research centres or existing data prepared by the researchers. Most of the systems images are not available, many of the researchers preparing the image datasets by themselves. Preparation of datasets needs costly equipments like high resolution cameras, highly configured computer systems, scanners etc. Nowadays handheld devices are equipped with high resolution cameras and cost also when compared to digital cameras very low. In this article we are going to demonstrate the step by step procedure of hardware setup, capturing image, processing images and feature extracted for those images by using mobile phone. For this implementation we have used Vivo z1pro mobile and prepared the data sets for the research of germination prediction and variety identification for the rice seeds. Preparing the datasets we have used four major rice seeds cultivated in Tamilnadu namely Andhraponni, Atchayaponni, KO50and IR20 which are collected from Agricultural university Trichirappalli,Tamilnadu,India. The prepared data set is freely available in https://github.com/duraitrichy/Riceseed.

Influence of fibers on desiccation cracks in sodic soil

Drying shrinkage of soil contaminated by alkaline solutions in ecologically fragile areas is a key issue that affects the strength, stability, and permeability of contaminated soil. Reinforcement with polypropylene fibers is a common physical treatment method. This paper presents experimental investigations of the shear strength, permeability, formation, and development of desiccation cracks in sodic soil with four different contents of polypropylene fibers: 0%, 2%, 4%, and 8%. The shear strength and permeability coefficient were tested by the SLB-1 triaxial shear permeability tester. Meanwhile, a series of crack images and water content evolution were obtained during free desiccation tests with self-designed monitoring equipment. The dynamic changes in the cracks are then quantitatively analyzed by using digital image processing and geographic information system (GIS) analysis. The experimental results show that shear strength increases and permeability coefficient decreases with the increase of fiber content. The evaporation process with different polypropylene fiber contents has three stages, namely, Stages I, II, and III that are in accordance with the slope of the evaporation curve. However, the polypropylene fiber content has little effect on the total duration of crack development. The crack development process has three stages. The crack ratio, fractal dimension, and average width decrease as the polypropylene fiber content increases. The fractal dimension gradually and linearly increases with time. The fractal dimensions of the cracks are reduced with increased polypropylene fiber content, which indicates that polypropylene fibers can inhibit the development of soil cracking. Because the elastic modulus of the fibers is very high, it is difficult for the fibers to be pulled out from the soil during cracking. Therefore, friction is produced at the interface between the fibers and soil particles, which can inevitably inhibit the relative sliding of the fibers and share the tensile stress caused by the structural shrinkage in the soil. Therefore, polypropylene fibers can dramatically improve the stability and integrity of soil, similar to a “bridge.” In conclusion, these research results provide an important explanation for dealing with alkali-contaminated foundations and soils.

RETRACTED: Design of embedded digital image processing system based on ZYNQ

Zynq- real time edge detection system based on execution of the image reconstructed from the sequence. Although the object edge detection is an essential tool in computer vision, edge detection results in the negative image frame noise significantly. Further, due to the high computational complexity of an image filtering operation, implemented in the hardware configuration of the re-configurable hardware it is necessary. In this portion, the proposed dynamic Zynq embedded system reconfiguration capability to detect according to the input image frame to the noise level of the different density filters perform during runtime bit stream re Configuration.The results show that the accuracy of edge detection is highly evaluated. We analyze various noise density levels, and the results show that the edge detection results of the proposed filter bit stream are more accurate, while effectively providing computing capacity to support the real-time processing of the image frame. In the configuration time, CPU usage, and hardware resource utilization performance test results were compared.

The Numerical Implementation of the Image Filtering Method with Computations in the Residue Number System

The use of the Residue Number System (RNS) instead of the known binary arithmetic proposes, it is substantiated that the application of the non-position RNS will allow increasing the speed and reducing power consumption of digital image processing applications. Analyzed known schemes, based on which the process of image filtration occurs. The inefficiency of the application of known schemes is substantiated, connected with the possibility of errors due to the overflow of the dynamic range of the RNS. A new scheme of the filtration transformation process is given, which allows obtaining correct results of digital processing, independent of the size of the digital image being processed and the filter mask. The obtained results can be used in the construction of specialized digital image processing systems with low power consumption and high speed of operation.

Development of a low-cost digital image processing system for oranges selection using hopfield networks

The orange processing industry has grown vertiginously around the world recently once that orange derived products are embedded in a profitable market that constitutes a substantial part of the economy in many countries. Normally, orange classification in industries still is performed manually or using expensive technologies. Recent researches aimed to develop systems capable of executing this task considering elements of artificial intelligence to find ways of automating this process. This work aims to present the development of a low-cost oranges classification system through image processing and artificial neural networks concepts for classification and prediction of their main characteristics. Therefore, a systematic photographic and methodological procedure was applied for image processing and implementation of Hopfield recurrent artificial neural networks creating a trustworthy selection system. The results obtained achieved an acceptable average percentage of 85% for correct answers considering both criteria of quality and size, which means that the implemented system reaches similar or better results when compared to methods proposed in similar works. Additionally, an economic analysis indicated a favorable payback between 3 and 5 months, attesting the feasibility of its implementation. Overall, this work ensures an effective orange selection system with minimal human contact and low-cost for the orange industry.

Digital image processing systems based on functional-oriented processors with a homogeneous structure

Real-time digital image processing as part of mobile systems requires solving the problems of development high-performance specialized processors for intraframe image processing. The graphs of intraframe processing algorithms are analysed and their parameters are evaluated, primarily the degree of parallelism. The architectures of functional-oriented processors based on homogeneous computing environments for ultrafast intraframe processing are described. The results of research and development of microelectronic implementation of such processors are presented.