Computer Image Processing Technology Research Articles

Design of virtual reality augmented reality mobile platform and game user behavior monitoring using deep learning

To explore the design of the virtual reality (VR) augmented reality (AR) mobile platform and game decision model based on deep learning (DL), the gesture-based interaction of VR games based on Leap Motion is researched. Based on the interactive features of gestures, a set of general gesture interaction rules in VR games is established. In the meantime, according to the theoretical basis and the characteristics of VR, a set of general models of VR gesture interaction is designed, the factors affecting the efficiency of VR gesture interaction are studied, and reasonable interaction feedback is designed. By using the computer vision and image processing technology, gesture-based interaction can collect natural gestures, extract gesture features, recognize gesture indications, and respond to the user demands. Also, it can extract the basic gestures from gesture-based interaction in VR, analyze the basic features of gestures and gesture-based interaction in VR games, and describes the gesture features by mathematical vectors and sets. The research results show that the application of gesture feature design method in the game can analyze the factors affecting the interaction efficiency. Also, the usability of the gesture-based interaction designed by the gesture design method is verified by tests. Therefore, the AR&DL platform of “AR+DL” establishes a learning platform supported by DL and AR technology. The game decision model is used to describe the process of gesture-based interaction in the game, and the factors affecting the interaction efficiency are reduced, which has certain reference and guidance for VR applications using gesture-based interaction.

The evolution of cracks in red clay under wet and dry cycles and its influence on shear strength

Using computer image processing technology and lots of triaxial experiments, the fracture evolution rules and shear strength indexes of red clay in the wet and dry cycles were analyzed, and the functional relationship between shear strength indexes of red clay and fracture density was established. The parameters in slope stability calculation were also discussed by the strength indexes reduction method. The results show that with the increase of the times of dry and wet cycles, the crack density gradually increases and eventually it remains stable. During the first two wet and dry cycles, the crack development of red clay was the most obvious. After the dry-wet cycle, the stress-strain curve of the red clay is weakly hardened and the failure mode is bulging. The shear strength of red clay is significantly reduced by the influence of dry wet cycle. After the first cycle, the attenuation of strength indexes is obvious, and the final strength indexes trend to be stable. The attenuation amplitude of the cohesive force is significantly larger than the internal friction angle. After the wet-dry cycle, the stable value of its cohesion becomes 54% ∼ 57% of the initial value, and the stable value of the internal friction angle becomes 45% ∼ 63% of the initial value, so it is recommended to use the value of the long-term strength index and it is more accurate to calculate the slope stability under dry and wet cycles. Under the action of dry and wet cycles, a quadratic polynomial can be used to fit the functional connection between the crack density of red clay and the index of shear strength.

Quick image stitching algorithm based on template matching for Mask defect detection

With the computer technology and image processing technology gradually applied to the mask detection system, computer vision detection method has become the mainstream way of automatic mask inspection. Masking is usually a large area, and in order to ensure detection accuracy, the camera’s field of view is small, and the panorama stitching of mask images has become an essential image processing link in defect detection. In the mask image acquisition system, the precision workbench is in the working mode of horizontal vertical translation, and the adjacent two images have horizontal and vertical offsets. In view of the characteristics of big data volume of mask image, cell repetition, small variation of rotation scale, this paper proposes a fast multi-mask image stitching algorithm based on template matching. The template matching algorithm is optimized, compared with the traditional image stitching algorithm, which greatly improves the stitching speed, avoids the problem of feature matching repeated graphics mismatch, and the stitching effect is good.

Development process of animal image recognition technology and its application in modern cow and pig industry

The construction of digital farms had emerged since 2018. It used computer image processing and pattern recognition technology to study cow and pig recognition through interdisciplinary research. This paper introduced specific cases about using image recognition technology to build modern cow and pig farms. Then reviewed the research on animal image recognition technology and had the following research directions: iris recognition, retinal recognition, nose pattern recognition and face recognition. In the end this article proposed how to “DIY” classification for pigs.

Automatic Detection of Bike Riders Without Helmet

As per Indian scenario, motorcyclists break traffic rules very frequently by not wearing helmet and Indian traffic police is not strict to such riders because of their tedious work. Automatic Detection of Bike Riders without Helmet system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image. This paper presents the technique by which motorcyclists without helmet are detected. In this technique moving vehicles are detected by thresholding and then classified into motorcyclist & non-motorcyclist by area and aspect ratio. To detect helmet first ROI is determined and by cascade classifier without helmet are detected. Finally we get the details of the particular bike rider from the database.

License Plate Recognition

<p>License Plate Recognition (LPR) system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image and by extracting their number plate from whole vehicle image, it displays the number plate information into text. The overall accuracy and efficiency of whole LPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The approach used to segment the image is bilateral filtering algorithm and canny edge detection algorithm. Then we predict the license plate from processed image using py–tesseract OCR and match the retrieved text which is vehicle number plate with database. Finally we get the details of the particular vehicle from the database.</p>

Application of Computer Image Processing Technology in Ethnic Digital Imaging

In the computer field, graphics and image processing technology has occupied a relatively key core position in computer technology. Image processing not only provides convenience for people’s daily life, but also makes people’s visual experience have a strong impact. In today’s information age, science and technology play a leading role. Whether it is a cause, a field, a country, or the world, the rapid development and progress cannot be achieved without the integration and application of science and technology, as well as the digital digital image. At present, the background for the development of national digital imaging is the continuous and in-depth development of science and professionalism, and the growing international development trend. At present, the continuous improvement and development of computer image processing technology will have a profound impact on the field of national digital imaging. The purpose of this article is to study the application of ethnic digital images based on computer image processing technology. This paper uses the Dropout method to fit the training data and uses the Softmax function to calculate the probability distribution. In the experiment, 60% of the reference image is selected, 20% of the training set is used as the verification set, and 20% is used as the test set to repeat the median hundreds of times to reduce the error. And the data preprocessing method with local contrast normalization is adopted to improve the effect. The experimental results show that when the number of filters is 64, the image quality is the best. In the current IQA-Basic image processing results, the VSI is relatively poor. In IQA.Weighted2, the training image block is calibrated by the reference image quality assessment method, which has better results.

High-precision autofocus using a double wedge splitter

We present an autofocus technique that combines focusing technology by using a double wedge splitter and computer image processing technology in order to achieve high-precision focusing. The focusing technique we propose uses a cooperation target (a ring object) as an imaging object. The ring image is divided into two half-ring images by using a double wedge splitter, which extracts their edge information for curve fitting to obtain the center of curvature of the two half rings so we can calculate the deviation distance between the two half-ring images. The control system uses the deviation distance as feedback, and the real-time closed loop adjusts the relative position of the secondary imaging system and the double wedge splitter so that the deviation distance is the smallest and the defocusing amount is also the smallest. In this paper, we propose the optical system model, analyze the algorithm flow in detail, and verify the feasibility of the focusing method by experiments. The results show that this focusing method has the advantages of high focusing accuracy and easy operation. The defocusing amount of this technology is within 10 µm.

Computer image processing and neural network technology for thermal energy diagnosis of boiler plants

Objective: The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.

Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

The paper aims to study the identification and diagnosis of infrared thermal fault of airborne circuit board of equipment, expand the application of intelligent algorithm in infrared thermal fault diagnosis, and promote the development of computer image processing technology and neural network technology in the field of thermal diagnosis. Taking the airborne circuit board in the boiler plant as the research object, first, the sequential analysis method was selected to collect the temperature changes during the operation of the circuit board. Second, on the basis of convolutional neural network, the program was written in Python, and the Relu function was used as the activation function establish the thermal fault diagnosis method of the on-board circuit board of the boiler plant equipment based on the convolutional neural network model. Third, based on the support vector machine intelligent algorithm, genetic algorithm was used to optimize the parameters, and combined with the grey prediction model, the infrared thermal fault diagnosis scheme of the circuit board of the multistage support vector machine boiler plant equipment was constructed. The results showed that the accuracy of the model after 6000 iterations was stable between 0.92-0.96, and the loss function value was stable at about 0.17. After the optimization of genetic algorithm, the accuracy of thermal fault diagnosis based on support vector machine model was optimized. Compared with grey prediction model, the accuracy of support vector machine model for fault diagnosis was higher, mean square error value was 0.0258, and the correlation coefficient was 91.55%. To sum up, the support vector machin model shows higher accuracy than grey prediction model, which can be used for thermal fault diagnosis.

Computer image processing and neural network technology for boiler thermal energy diagnosis

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

The Application of Airborne Geophysical and Satellite Imagery Data in the Delineation of Rock Boundaries and Structural Lineaments in Anka Sheet 52, Northwestern, Nigeria

The understanding of different lithologies and geological structures is a major task of geologists for accurate geological map during geological mapping exercise. In doing so, geologist must be able to cover broad areas of the surface or subsurface of the earth through collection of reliable remote sensing data over a wide region. The Landsat ETM + from Global Land Cover Facility (GLCV), Shuttle Radar Thematic Mapper- Digital Elevation Model (SRTM, DEM) data from Global Mapper version 14, Airborne Magnetic and Radiometric data from Nigeria Geological Survey Agency (NGSA) was acquired and interpreted with the help of computer-image processing technology such as Oasis Montaj and ArcGIS software to process, display and represent results covering the study area (Anka sheet 52). The basis of this technology is the representation of the radiometric data in a digital raster format that contain both amplitude and spatial information. In this format, the data was analyzed, enhanced for visual inspection and combined with other types of data to form colour composite images that clearly subdivide the Basement complex rocks into Pan-African Granites from the Metasediments and the most eye-catching feature is the basement-sedimentary contact. Also, a major NE–SW trending structural lineament referred to as the Anka Fault trends for almost 25km and other subsidiary lineaments trending NW–SE with few E–W trends. Keywords: Landsat ETM + , Magnetic, Radiometric, Structural Lineament, Satellite Imagery. DOI : 10.7176/JEES/10-1-05 Publication date: January 31 st 2020

Recognition of Mobile Robot Navigation Path Based on K-Means Algorithm

With the rapid development of computer technology and electronics industry, computer processing capability and image processing technology have been greatly improved, making robots based on computer processing and image processing have entered a new development in the field of navigation path recognition research. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. The key to the successful execution of the mobile robot is to move according to the planned path and to avoid obstacles autonomously. These two points depend on the validity and accuracy of navigation path identification. At present, research on mobile robot navigation path recognition mainly uses visual navigation as the main method, which uses visual sensors to simulate human eye functions, obtains relevant information from external environment images, and processes them to realize related functions that the system needs to complete. The two major problems in visual navigation are poor recognition ability and insufficient ability to resist light source interference. The main purpose of this paper is to improve the recognition ability of mobile robot navigation path and the ability to resist light source interference. It mainly uses the [Formula: see text]-means algorithm for visual navigation research. By simulating the acquired image and the selected color space, the results show that the average time taken to complete a path identification method is 152[Formula: see text]ms. Under different illumination environments, the information extraction rate of mobile robot navigation path can reach 90%, and the effect of strong light on navigation path recognition is effectively reduced under strong illumination environment. The results show that the recognition of the visual navigation path of a mobile robot using the [Formula: see text]-means algorithm is more precise than the conventional method, and it takes less time to better meet the timeliness requirements of mobile robots.

Application of the Quantitative Monochrome Calibrated Schlieren Technique in a Highly Loaded Turbine Cascade Test

Abstract As the load of the turbine components of aircraft engines continuously increases, shock loss becomes the dominant factor of turbine stage loss and has become a hot topic. The Schlieren technique is one of the few effective experimental methods to observe and study shock wave and, thus, has been widely used. Nevertheless, limited by camera accuracy and computer image processing technology, quantitative schlieren analysis methods were difficult to achieve in engineering applications. Fortunately, several quantitative schlieren methods have been developed with the help of new digital technology. Applying the schlieren technique to the highly loaded turbine cascade test is of great significance to the study of shock wave in highly loaded turbine cascades. In this paper, the results of the quantitative density field and shock intensity and loss in the cascade are obtained by using a double-reflection-type monochrome schlieren device. The boundary condition of the density field is obtained by pressure test, and matlab software is used as image processing calculation tool. The quantitative results of this paper prove the feasibility of applying quantitative schlieren method to highly loaded turbine cascade tests. Also, the implemented image processing method and density boundary condition acquisition method are suitable and convenient for cascade flow and shock measurement tests.

Driver Drowsiness Detection using MATLAB

Drowsiness is major cause of accidents. So, this drowsiness detection system alerts the drowsy drivers in order to reduce the risk of potential accidents. The proposed system uses computer vision and image processing technology of MATLAB for detecting the drowsiness. MATLAB detects if eyes are closed or open using various image processing techniques performed using Viola-Jones face features detecting algorithm and skin y,cb,cr values detection function ,converting image into a binary image which was further employed to extract eye characteristics, and its closing frequency, determining drowsiness.

Feature mining simulation of video image information in multimedia learning environment based on BOW algorithm

With the development of computer image processing technology, video image information feature mining in multimedia environment has become a research hotspot in the field of education with its unique characteristics. However, the current method is to classify and retrieve through the video image information features. When the video image information is disordered, the multimedia video image information features cannot be classified, and the criterion for measuring the size of the video image information cannot be given, and the information feature mining accuracy is low. Therefore, this paper proposes a new efficient algorithm for multimedia video image information retrieval. Firstly, the SIFT features of the video image are analysed to obtain the SIFT features of the video image. Secondly, the SIFT feature is used for feature matching to identify the target image. Finally, the BOW algorithm is introduced to index the matched SIFT features, and the bag-of-words model, TF-IDF weighting and Euclidean distance are used to complete the similarity calculation of the image, and the feature mining of multimedia video image information is completed. The simulation results show that the proposed method effectively improves the feature mining speed and feature mining accuracy and has better robustness.

Identifikasi Kematangan Buah Mentimun Berbasis Citra Digital Menggunakan Jaringan Syaraf Tiruan Backpropagation

Cucumber, cucumber, or cucumber (Cucumis sativus L) are plants that produce edible fruit. Especially in NTB cucumber production in 2015 reached 5,224 tons with a harvest area of 326 hectares. It is at number five after onions, chili, tomatoes and cabbage [2]. There are several parameters that can affect the quality of cucumbers, one of which is the shape, level of planting age and maturity [5]. Maturity of cucumbers can be recognized physically in terms of skin texture and color. The identification process of physical properties conventionally still has many disadvantages including the time needed is relatively long and produces a variety of products due to the limitations of human visuals. This becomes an obstacle so that it requires the application of computer image processing technology, especially in agriculture. Because of this, the researchers proposed using GLCM as feature extraction and using Backpropagation artificial neural networks for testing and training so that the research resulted in an accuracy of 89.6%. DOI : 10.29408/jit.v2i1.976

Research Advances on Quality Evaluation Methods of Tea Color, Aroma and Taste

With the progress of the time, the limitations of traditional tea quality evaluation methods have become increasingly prominent. Although the tea quality characteristics can be quickly identified by sensory evaluation, it is also easily affected by some subjective and objective factors. By contrast, determination of chemical components is relatively objective and accurate. However, it is complex to operate and can't reflect the overall quality of the tea. If combined with modern instruments and technologies on the basis of sensory evaluation and physico-chemical analysis, tea quality evaluation can realize advantage complementation and be more comprehensive. In this paper, some new technologies used in tea quality evaluation studies in recent years were summarized in terms of tea color, aroma and taste. Technologies included computer vison and image processing technology, colorimetric sensor array technology, electronic nose technology, comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry technology (GC×GC-TOFMS), gas chromatography and olfactometry technology (GC-O), electronic tongue technology and near infrared spectroscopy technology (NIRS). The development trend of new technology in tea quality evaluation was also discussed.

Research on rapid mapping technology in the field of unmanned aerial vehicle (UAV) aerial survey

Through the development of computer image processing technology and UAV technology, after installing high-definition cameras on UAVs and taking high-resolution images, the technology of directly generating DOM images in the surveyed areas using software has been well-developed. However, under the emergency conditions such as dealing with disasters, the long-term processing time required for aerial surveys of UAVs has become a major efficiency constraint to surveying and mapping. This paper focuses on the rapid mapping and mosaicking of survey images from unmanned aerial vehicles under emergency conditions, and elaborates current research status, difficulties and resolvents of the rapid mapping of existing UAVs.

Research on glass surface quality inspection based on machine vision

ABSTRACTIn the diversified development of glass industry and technology products, the demand of quality performance of the glass surface increases. The surface quality detection is becoming more and more strict, especially the evaluation of the surface quality defects. The glass surface quality detection means, and ability has certain limitations, most methods for artificial detection, improve the detection accuracy and objectivity. The authors studied the glass surface quality of machine vision detection method based on the detailed discussion of computer vision and image processing technology. This study draws the conclusion that machine vision plays an important role in the quality detection of the glass surface and its high efficiency & accuracy has broad application prospects.