Computer Image Processing System Research Articles

A systematic literature review of Internet of Video Things: Trends, techniques, datasets, and framework

In the last decade, Internet of Things (IoT) and machine learning (ML) have been used for developing semi or fully automated systems of any application. Due to the advancement in networking technologies, the contribution of video-based input is also rising for IoT systems. Hence, Internet of Video Things (IoVT) has been an emerging field to gain more human trust in the systems. IoVT is an integrated attempt of computer vision sensors, networking, video or image processing and information storage systems. In this paper, we have highlighted the literature evidence in the IoVT field. There is a need to analyze it in terms of different aspects, that enables the researchers to understand research trends in the field and lead them to future research directions. Therefore, this article presents a systematic literature review (SLR) in IoVT using various research publications, since its evolution. The study applies article selection criteria on different search methods and compares the relevant published articles. We have analyzed these filtered articles for identifying the study trends, their used techniques & frameworks for data processing and inputs or datasets used for their studies. At the end, various research opportunities and challenges have been framed in the field for future research references.

New bidirectional recurrent neural network optimized by improved Ebola search optimization algorithm for lung cancer diagnosis

The early detection of cancerous and malignant lung cancer by medical imaging techniques, CT-scan for example, which never needs to do sampling reduces the risk of cancer growth and spreading. Accordingly, computer image processing and diagnostic system development, followed by cancer's classification into malignant and benign, is of primary importance in the early discovery of lung cancer which plays a pivotal role in the treatment improvement and saving the patient's life. This work intended to improve malignant and benign gland categorization accuracy and, as a result, detection accuracy. Here, a new methodology has been proposed to get an accurate lung cancer diagnosis system using an improved Bidirectional Recurrent neural network. The improvement of the network has been done by designing an improved form of an Ebola optimization search algorithm. Before applying the major diagnosis system, some preprocessing techniques have been done. The model is then applied to IQ-OTH/NCCD lung cancer dataset and its results are compared with some published works to indicate the eminence of the suggested method toward the comparative ones.

A review of smart parking systems

Conventional parking methods often result in inefficiencies, such as traffic congestion caused by drivers searching for available spots and inadequate revenue management for parking operators. Smart parking systems (SPS) aim to address these challenges by offering real-time information on parking availability, optimizing parking space utilization, and providing convenient payment options. This study seeks to comprehensively examine the academic research conducted on SPS to contribute to a deeper understanding of the current research landscape in this field. The research involved an extensive analysis of 124 published research papers, journals, and articles and revealed an intriguing trend: the proliferation of IoT-based SPS in urban settings is revolutionizing decision-making processes and elevating user experiences. Among the diverse range of SPS options, wireless sensor networks have emerged as a popular choice, as they not only eliminate the hassle of extensive wiring but also significantly reduce deployment expenses. Computer vision or image processing systems for vehicle detection require fewer sensors but come with substantial installation costs due to the necessity of a camera network. Conversely, SPSs based on vehicular ad hoc networks are experiencing slower adoption rates, mainly due to the high costs associated with installation and the requirement of on-board units in vehicles. The findings of this study will help researchers identify areas that require further investigation and city planners integrate smart parking systems into their overall smart city initiatives.

An effective nonlocal means image denoising framework based on non-subsampled shearlet transform

Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artefact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. Initially, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition levels of NSST is set to two, resulting in set of low-frequency coefficients (coarser layer) and four sets high-frequency coefficients (finer layers). The two number of levels of decomposition are used in order to preserve memory, reduce processing time, and mitigate the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter preserves textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.

Intelligent System for Detecting Tooth Decay in OPG Images by Image Processing

Introduction: Dental caries and lesions are one of the most common dental diseases worldwide, affecting a high percentage of the world's population. Research has shown that more than 90% of adults have experienced tooth decay, so early detection of dental lesions in the early stages is a major aspect of maintaining dental health.Materials and Methods: In order to diagnose decayed teeth, first, the difference between decayed teeth and healthy teeth was discussed. By first calling the OPG images and removing the inappropriate margins of the image. Then the gray-scale image was converted to binary and after removing the noise, thresholding was performed. Then we found the location of the white spots in the black image and displayed the processed image on the user interface.Results: Computer image processing systems have been developed to be able to process and analyze images with better speed and accuracy. The proposed system has a good performance in diagnosing dental caries and the results show that the proposed system is reliable and optimal and as a clinical decision support system can help dentists in accurate and early diagnosis of dental caries at the initial level of caries.Conclusion: In this article, using the advancement of technology that has been formed in recent years, we were able to provide a solution to facilitate medical science. With the science of image processing, eye errors that may cause problems during the diagnosis and recording of the disease in the process of recovery are significantly reduced.

Computer Image Processing System Based on Feature Extraction Technology

This article studies the methods of computer image acquisition, processing and recognition. The study found that the images collected from the computer image processing system often have blurring phenomenon. For this reason, the paper proposes a wavelet transform feature extraction algorithm based on colour images, which can separate the image and the background without greying the colour image. Through experimental research, it is found that this method can efficiently and quickly solve the problem of automatic image registration.

Versatile image processing technique for fuel science: A review.

The evolution of computer vision and image processing system paved the way that any technologists can extract quantitative data sets from the visual results of an image. Digital image processing (DIP) technique precisely measures and quantifies the image and eliminates the subjectivity of manual interpretation. DIP is a non-destructive, inexpensive and rapid method that has been used by many researchers in various applications of biofuel. In fuel science, DIP and artificial intelligence (AI) techniques have been successfully applied for the classification of biodiesel, selection of biomass for biofuel production. DIP can be used in the combustion process and its control parameters, gas leakage, monitoring fuel reactant conversion reactions, impurities present and adulteration of fuel, also automation process of a fuel injection system. This review gives an overview of the applications of image processing in fuel science.

Research on Computer Multimedia Software Graphics and Image Processing and Analysis System in Colleges

With the development of computer software and hardware technology in colleges and universities, the application of multimedia computers has become increasingly widespread. Graphics and image processing occupy a major position in the application of multimedia computers in colleges and universities. Image display is the basis of image processing. The key of special effect display is to divide the image into different image area blocks. The usual image special effect display method is to divide the image into different image blocks according to certain rules. Based on the analysis of the principle of image special effects display, this paper proposes a template-based image special effects display method. Experimental results show that compared with traditional interpolation algorithms, such as bilinear interpolation and bicubic polynomial interpolation, this algorithm has better interpolation effects and overcomes the speckle interference in single wavelet interpolation.

Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles

Given the recent advances in computer vision, image processing and control systems, self-driving vehicles has been one of the most promising and challenging research topics nowadays. The design of vision-based robust controllers to keep an autonomous car in the center of the lane, despite uncertainties and disturbances, is still an ongoing challenge. This paper presents a hybrid control architecture that combines Deep Reinforcement Learning (DRL) and Robust Linear Quadratic Regulator (RLQR) for vision-based lateral control of an autonomous vehicle. Evolutionary estimation is used to model the vehicle uncertainties. For performance comparison, a DRL method and three other hybrid controllers are also evaluated. The inputs for each controller are real-time semantically segmented RGB camera images which serve as the basis to calculate continuous steering actions to keep the vehicle on the center of the lane with a constant velocity. Simulation results show that the proposed hybrid RLQR with evolutionary estimation of uncertainties architecture outperforms the other algorithms implemented. It presents lower tracking errors, smoother steering inputs, total collision avoidance and better generalization in new urban environments. Furthermore, it significantly decreases the required training time.

THE METHOD OF IMPROVING FOGGED IMAGES VISIBILITY AND ITS USING IN THE PROCESSING IMAGES COMPUTER SYSTEM

Context. Presence of fog and haze on digital images may cause problems in processes of recognition, tracking, classification of objects. Thus methods for removing fog and improving visibility of objects in images obtained under poor visibility conditions are in demand in many computer vision problems. In foggy weather, contrast and color of an image get worse. Fog removal is often accompanied by artifacts in the image and color distortion. Therefore, it is relevant to seek methods for correct assessing presence and removal of fog while preserving image details and colors and developing appropriate methods for blurred images processing. Objective. The purpose of this research is to find effective approaches to solving the problem of removing fog and haze from digital images and implementing them in a digital image processing computer system [1]. Method. Main stages of image processing are performed on the intensity channel, which helps to preserve colors. The proposed approach keeps the values of the processed pixels in an acceptable range, which allows better preservation of image details. Frequency filters are used to evaluate the transmission map. In a modified method, fog density is estimated using a neural network. Results. The method of removing fog and haze from single image is proposed. This method effectively improves the objects visibility, preserves details and colors in the image. A modification of the method with another fog density estimation method is also proposed. The presented methods were implemented in a computer system [1]. Conclusions. The proposed method and its modification effectively remove fog and haze from single image and improve the objects distinguishability in them. The implementation of these methods in a computer image processing system [1] has expanded the functionality of the system and increased its ability to improve the quality of images obtained under poor visibility conditions. The system can be used for preliminary image processing to prevent errors in further operation of computer vision algorithms.

DELP-DAR system for license plate detection and recognition

Automatic License Plate detection and Recognition (ALPR) is a quite popular and active research topic in the field of computer vision, image processing and intelligent transport systems. ALPR is used to make detection and recognition processes more robust and efficient in highly complicated environments and backgrounds. Several research investigations are still necessary due to some constraints such as: completeness of numbering systems of countries, different colors, various languages, multiple sizes and varied fonts. For this, we present in this paper an automatic framework for License Plate (LP) detection and recognition from complex scenes. Our framework is based on mask region convolutional neural networks used for LP detection, segmentation and recognition. Although some studies have focused on LP detection, LP recognition, LP segmentation or just two of them, our study uses the maskr-cnn in the three stages.The evaluation of our framework is enhanced by four datasets for different countries and consequently with various languages. In fact, it tested on four datasets including images captured from multiple scenes under numerous conditions such as varied orientation, poor quality images, blurred images and complex environmental backgrounds. Extensive experiments show the robustness and efficiency of our suggested Extensive experiments show the robustness and efficiency of our suggested system that achieves in accuracy rate 99.3% on AOLP and 98.9% on Caltech dataset.

A novel framework for very high resolution remote sensing image change detection

This paper proposes a novel framework for very high resolution remote sensing image change detection. The change detection technology is the goals or the phenomenon conditions of different time interval to the change that have analysed the recognition and computer image processing system, including judgement goal whether changes, to determine changes the region and the time and spatial distribution of pattern category and appraisal change of distinction change. Over the past few years, researchers from all over the world have devoted themselves to the research of change detection technology. Many detection methods based on remote sensing images have been developed successively. However, no change detection method has absolute superiority in present research. This paper obtains the inspiration from PSO and OTSU to propose the particle swarm optimisation segmentation jointed model to construct the optimal solution of generating change map and the PSO jointed OTSU is introduced to help obtain the optimal threshold. Numerical simulation proves that the proposed method can segment the changed regions accurately while keeping the high noise robustness.

A novel framework for very high resolution remote sensing image change detection

This paper proposes a novel framework for very high resolution remote sensing image change detection. The change detection technology is the goals or the phenomenon conditions of different time interval to the change that have analysed the recognition and computer image processing system, including judgement goal whether changes, to determine changes the region and the time and spatial distribution of pattern category and appraisal change of distinction change. Over the past few years, researchers from all over the world have devoted themselves to the research of change detection technology. Many detection methods based on remote sensing images have been developed successively. However, no change detection method has absolute superiority in present research. This paper obtains the inspiration from PSO and OTSU to propose the particle swarm optimisation segmentation jointed model to construct the optimal solution of generating change map and the PSO jointed OTSU is introduced to help obtain the optimal threshold. Numerical simulation proves that the proposed method can segment the changed regions accurately while keeping the high noise robustness.

Three-dimensional finite analysis of acetabular contact pressure and contact area during normal walking

This study aims to analyze the contact areas and pressure distributions between the femoral head and mortar during normal walking using a three-dimensional finite element model (3D-FEM). Computed tomography (CT) scanning technology and a computer image processing system were used to establish the 3D-FEM. The acetabular mortar model was used to simulate the pressures during 32 consecutive normal walking phases and the contact areas at different phases were calculated. The distribution of the pressure peak values during the 32 consecutive normal walking phases was bimodal, which reached the peak (4.2 Mpa) at the initial phase where the contact area was significantly higher than that at the stepping phase. The sites that always kept contact were concentrated on the acetabular top and leaned inwards, while the anterior and posterior acetabular horns had no pressure concentration. The pressure distributions of acetabular cartilage at different phases were significantly different, the zone of increased pressure at the support phase distributed at the acetabular top area, while that at the stepping phase distributed in the inside of acetabular cartilage. The zones of increased contact pressure and the distributions of acetabular contact areas had important significance towards clinical researches, and could indicate the inductive factors of acetabular osteoarthritis.

English

Histopathology refers to the examination of biopsy samples by a pathologist using a microscope for analysing and classifying diseases. In order to study the manifestations of a disease, the analysis of histopathological image is done manually by a pathologist and therefore the diagnosis is subjective and greatly dependent on the level of expertise of the professional. In order to overcome this problem of a possible erroneous diagnosis and for early stage detection, an automated computerized image processing system is needed for quantitative diagnosis of biopsy samples. In this paper we have proposed a model for early stage cancerous cell detection and have reviewed and summarized the phases involved, namely, Image Pre-processing, Image Segmentation, Feature Extraction and Classification.

Analysis of the Computer Processing System of Remote Sensing Satellite Images

Remote sensing satellite images can intuitively reflect the information of the Earth's surface. The computer image processing system is of the advantages of high-precision and low-cost. It has a strong application value to study the computer processing system of remote sensing satellite image. The paper first discussed the design principles of the computer processing system and the implementation of its workflow, and then the application of the image processing system is briefly analyzed.

유과의 품질규격 유지를 위한 외형 정보 측정 시스템 설계 및 적용 연구

Korean oil and honey Yukwa has been paid attention as formal cake for traditional national seasons' holiday and religious service. Quality of Yukwa, however, has been maintained arbitrarily by each Yukwa manufacturer. Since even same Yukwa had severe differences in size, weight, and pattern, it has given the negative effect to the consumer. Yukwa industries need to setup the quantitative quality specifications instead of qualitative ones to maintain the uniformity of Yukwa quality. Efficient and economical inspection and process control system should be developed. In developing quality standards of Yukwa, features which can measure quality quantitatively in real time should be properly chosen. Existing quality features such as acidity, oxidization, hardness, viscosity, and texture were measured by the chemical or physical base destructive methods. Many research and developments have been performed in investigating and analyzing chemical transition states of those quality features as environment or storage condition changes. Most methods, however, require either off-line or complex treatment or time consuming process of analysis in evaluating quality features. Consumer, however, selects products mostly based on the external features such as shape, size, and color. Therefore, critical visual quality features should be chosen and the efficient real time measurement system must be developed. In this paper, computer image acquisition and processing system were developed and software modules were developed to extract the quantitative data of those features in real-time. Computer image processing system will promote in maintaining uniform quality of Yukwa and establishing quality standards of Yukwa.

SportsTech

The cameras are connected to a powerful image-processing computer system, which tracks the movement of all objects on the pitch and filters out the players, referee and all other objects. All that's left is the ball and the system knows its three dimensional x-, y- and z-position to within a few millimeters in the coordinate system of the pitch. When the ball passes the goal line, the system sends a vibration and optical signal to the officials' watches. All camera images of every event are stored and can be replayed anytime.

Particle Size Distribution and Shape Characterization of the Chips Produced During Granite Machining in Relation to Process Forces and Specific Energy

The objective of the present work was to characterize the particle size/size distribution and shape of the chips produced during granite machining in relation to process forces and specific energy. To achieve this goal, two different series of circular sawing experiments were performed on an instrumented full-scale rig. The first series of sawing tests was carried out with a newly dressed blade, while the second series of tests were carried out at a relatively worn state. For each case, cutting forces and specific energy values were determined. By using a computer image-processing system, size and shape descriptors of the collected chips from the cutting debris of both series of tests were quantitatively evaluated. The results of the analyses showed that the size/size distributions of the produced chips were greatly influenced by the wear state of the diamond segments. Size/size distribution characteristics of the chips were also found to correlate with the force ratio and specific energy, indicating that particle size analysis of the cutting debris could be used as an efficient guide to sawing efficiency in industrial applications. Despite to diversity in their size/size distribution characteristics, the chips produced in both series of sawing tests showed quite similar shape characteristics.

Studies on measurement system for rice chalkiness based on computer image processing

Based on Chalkiness V1.0,using Visual C++.NET as tools,a computer image processing system ChalkinessV2.0 has been developed for the detection of chalky rice rate,chalkiness degree and grain shape for grading of the high quality rice required by the national standard NY147-88.The system is composed of modules of image preprocessing,sample identification,chalkiness distinguishing,ratio of length and width detection,statistics and analysis and results export.The system can analyze the rice chalkiness of three rice varieties: Fengyuan B,Miyang 46 and Nipponbare.Chalky scores such as chalky rice rate,chalkiness degree and projected area can be gotten with parameters imported only once.