scholarly journals Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

2020 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Dr. Dhaya R.

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.

2020 ◽  
Vol 1 (6) ◽  
pp. 1-6
Author(s):  
Vyacheslav Lyashenko ◽  
Tetiana Sinelnikova ◽  
Oleksandr Zeleniy ◽  
Asaad Mohammed Ahmed Babker

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.


Author(s):  
Eimad Abdu Abusham

Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.


2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Reza Bohlouli ◽  
Babak Rostami ◽  
Jafar Keighobadi

Polyethylene (PE) pipelines with electrofusion (EF) joining is an essential method of transportation of gas energy. EF joints are weak points for leakage and therefore, Nondestructive testing (NDT) methods including ultrasonic array technology are necessary. This paper presents a practical NDT method of fusion joints of polyethylene piping using intelligent ultrasonic image processing techniques. In the proposed method, to detect the defects of electrofusion joints, the NDT is applied based on an ANN-Wavelet method as a digital image processing technique. The proposed approach includes four steps. First an ultrasonic-phased array technique is used to provide real time images of high resolution. In the second step, the images are preprocessed by digital image processing techniques for noise reduction and detection of ROI (Region of Interest). Furthermore, to make more improvement on the images, mathematical morphology techniques such as dilation and erosion are applied. In the 3rd step, a wavelet transform is used to develop a feature vector containing 3-dimensional information on various types of defects. In the final step, all the feature vectors are classified through a backpropagation-based ANN algorithm. The obtained results show that the proposed algorithms are highly reliable and also precise for NDT monitoring.


2012 ◽  
Vol 12 (4) ◽  
pp. 66-76 ◽  
Author(s):  
Lyubka Doukovska ◽  
Venko Petkov ◽  
Emil Mihailov ◽  
Svetla Vassileva

Abstract The paper presents an overview of the image-processing techniques. The set of basic theoretical instruments includes methods of mathematical analysis, linear algebra, probability theory and mathematical statistics, theory of digital processing of one-dimensional and multidimensional signals, wavelet-transforms and theory of information. This paper describes a methodology that aims to detect and diagnose faults, using thermographs approaches for the digital image processing technique.


2020 ◽  
Vol 4 (2) ◽  
pp. 69
Author(s):  
A.M. Sirisha ◽  
P. Venkateswararao

The wide spread of COVID-19 all over the world inspires every human to know and visualize its effect on human body. As   COVID-19 effects the human lungs here a number of radiological images of human lungs are analysed using an image processing technique called Threshold Segmentation. A significant difference is observed between healthy lung images and COVID-19 effected lung images.


Author(s):  
Ketut Abimanyu ◽  
Saeful Rohman ◽  
Asgia Setya ◽  
Pradito Octa

This research explain about garbage carrier roboboat. the purpose of making this robot is to reduce the trash found on river surfaces. Indonesia is an archipelagic country, this country dominated by ocean as rives estuary, however many of the resources produced from the sea have been polluted due to dirty and full of garbage from the river. So with the creation of this robot, it is expected to minimize the pollution. The shape of this robot is a boat that can operate in rivers and waters that still allows this robot to operate. This robot uses a brushless motor to move on the surface of the water and electronic speed control to control the brusless motor speed with the PWM setting method, for the navigation system the HC5409 ultrasonic sensor is used to detect any obstacles that avoid collisions, then image processing techniques are applied to detect garbage on the surface of the water, The Matlab R2012a program is an application used to process this image processing technique. In sum, the working process of this boat robot is, first, the navigating robot looks for garbage objects by detecting it using image processing. If garbage is detected, the robot will maneuver and activate brushless motors with garbage manuever algorithms, instructions sent from the microcontroller to Arduino Mega 2560, the robot approaches garbage object and crawl it. There are two garbage objects determined, namely styrefoam and a blue bottle, this boat can execute advanced, right and left maneuvers with the percentage of success reaching 80-90%. The advantage of this boat is that garbage objects can be detected from various directions and operate semi-automatically . Keywords :MATLAB,  Image processing ,Motor brushless,  Arduino Mega 2560 , manuever .


2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.


Author(s):  
Wesley S. Hunko ◽  
Vishnuvardhan Chandrasekaran ◽  
Lewis N. Payton

The purpose of this paper is to present the results of a study comparing an old technique for measuring low surface roughness with a new technique of data acquisition and processing that is potentially cheaper, quicker and more automated. It offers the promise of in-process quality monitoring of surface finish. Since the late 1800s, researchers have investigated the light scattering effects of surface asperities and have developed many interferometry techniques to quantify this phenomenon. Through the use of interferometry, the surface roughness of objects can be very accurately measured and compared. Unlike contact measurement such as profilometers, interferometry is nonintrusive and can take surface measurements at very wide ranges of scale. The drawbacks to this method are the high costs and complexity of data acquisition and analysis equipment. This study attempts to eliminate these drawbacks by developing a single built-in MATLAB function, to simplify data analysis, and a very economically priced digital microscope (less than $200), for data acquisition. This is done by comparing the results of various polishing compounds on the basis of the polished surface results obtained from MATLAB’s IMHIST function to the results of stylus profilometry methods. The study with the MATLAB method is also to be compared to 3D microscopy with a Keyence microscope. With surface roughness being a key component in many manufacturing and tribology applications, the apparent need for accurate, reliable and economical measuring systems is prevalent. However, interferometry is not a cheap or simple process. “Over the last few years, advances in image processing techniques have provided a basis for developing image-based surface roughness measuring techniques” [1]. One popular image processing technique is through the use of MATLAB’s Image Processing Toolbox. This includes an array of functions that can be used to quantify and compare textures of a surface. Some of these include standard deviation, entropy, and histograms of images for further analysis. “These statistics can characterize the texture of an image because they provide information about the local variability of the intensity values of pixels in an image. For example, in areas with smooth texture, the range of values in the neighborhood around a pixel will be a small value; in areas of rough texture, the range will be larger. Similarly, calculating the standard deviation of pixels in a neighborhood can indicate the degree of variability of pixel values in that region” [2]. By combining the practices of interferometry with the processing techniques of MATLAB, this fairly new method of roughness measurement proved itself as a very viable and inexpensive technique. This technique should prove to be a very viable means of interferometry at an affordable cost.


2012 ◽  
Vol 433-440 ◽  
pp. 727-732
Author(s):  
Anton Satria Prabuwono ◽  
Siti Rahayu Zulkipli ◽  
Doli Anggia Harahap ◽  
Wendi Usino ◽  
A. Hasniaty

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.


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