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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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Alzheimer's Disease and Dementia Detection from 3D Brain MRI Data Using Deep Convolutional Neural Networks

Alzheimer's Disease and Dementia Detection from 3D Brain MRI...

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international conference for Convergence of Technology (I2CT)

作者： H. M. Tarek Ullah ZishanAhmed Onik Riashat Islam Dip Nandi Faculty of Science & Information Technology American International University Bangladesh Dhaka Bangladesh School of Computer Science McGill University Montreal Canada

As reported by the the Alzheimer's Association, there are more than 5 million Americans living with Alzheimer's today, with an anticipated 16 million by 2050. The neurodegenerative disease is currently the 6th leading source of death in the US. In 2017 this disease would cost the nation $1.1 trillion. 1 in 3 seniors die in Alzheimer's disease or another dementia. It kills more than breast cancer and prostate cancer combined. [14] As of the this papers writing, detecting Alzheimer's is a difficult and time consuming task, but requires brain imaging report and human expertise. Needless to say, this conventional approach to detect Alzheimer's is costly and often error prone. In this paper an alternative approach has been discussed, that is fast, costs less and more reliable. Deep Learning represents the true bleeding edge of Machine Intelligence. Convolutional Neural Networks are biologically inspired Multilayer perceptron specially capable of image processing. In this paper we present a state of the art Deep Convolutional Neural Network to detect Alzheimer's Disease and Dementia from 3D MRI image.

关键词： Alzheimer's disease Magnetic resonance imaging Three-dimensional displays Biological neural networks

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Proposal of Effective Orthogonal and Hexagonal Hierarchical Structures for Disc Queries

Proposal of Effective Orthogonal and Hexagonal Hierarchical ...

引用

international conference on Cybernetics, Robotics and Control (CRC)

作者： Vojtěch Uher Petr Gajdoš Václav Snášel Department of Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9781538677391

The Fixed-Radius Nearest Neighbors (FRNN) query is a common task in scientific areas dealing with multidimensional point clouds (PCs) such as computer graphics, image processing, geographic information systems, machine learning and pattern recognition. We especially focus on 2D points in this paper. The FRNN means search all the points within defined radius for tasks when local properties of data are crucial. As this procedure is time consuming, an effective accelerating structure indexing the points is required. The standard methods are based on the orthogonal grids subdividing the space into uniform cells or hierarchies of orthogonal grids. However, several papers highlight the regular hexagonal grids for their superior theoretical metrics, e.g. better coverage of query discs, uniform distance to all six neighbors or lower number of passed cells. This paper provides a comparison of effectiveness of orthogonal and hexagonal hierarchies and traditional algorithms for FRNN disc queries. To do this, we propose a novel hierarchical method based on the hexagonal space-filling curve called the Node-Gosper curve which defines the hierarchical location of points in the hexagonal grids and their linear order. This method is tested with similar structure based on the orthogonal Z-order curve to show the real features of both approaches in practice.

关键词： Three-dimensional displays Vegetation Two dimensional displays Fractals Task analysis Shape Standards

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Determine Focus Based on Eye Gazing Direction 3

Determine Focus Based on Eye Gazing Direction

引用

3rd international conference on Science in Information Technology (ICSITech)

作者： Setiawan, Wawan Nursalman, Muhammad Munir Anugrah, Ricko Devian Univ Pendidikan Indonesia Bandung Indonesia

ISBN: (纸本)9781509058662

Focus is consciously processing for an available information, both little and big information. The sense of sight is one of the source information which can measure whether a person give an attention (focus) to something or not. As an example, if student doesn't observe to teacher in class, so the student doesn't pay an attention (focus) to the teacher. With computer vision along with component which connected analysis method, and also image scaling, focus from some people can be determined. To determine focus or not, it should be known the direction of eyesight from the person. This can be done in two ways. First way, to find the eye midpoint connected with component analysis method. Second way, to find the direction of eye sight based on eye midpoint which had been found before with image scaling method. The result, accuracy of eyesight with average point is 17.9%. When the accuracy level is higher, then probability to find the focus or not is also higher based on specific objects.

关键词： eyes tracking computer vision connected component analysis figure scale

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Human Skin Darkness Comparison Using Digital image Analysis

Human Skin Darkness Comparison Using Digital Image Analysis

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： D Pour Yousefian Barfeh M C Navarro Department of Computer Science Adamson University

People generally wish to have a lighter or a darker skin. Based on this wish, they regularly compare their skins regarding darkness while there is no simple and accessible system to perform that. This research proposes an algorithm and implements the algorithm in a simple and accessible computerized system that is capable to compare skins of two persons regarding darkness. The system uses digital image processing that accepts an image containing forearms of two persons who are under comparison, then displays some message that expresses the darker skin and the lighter skin. The system uses different techniques such as greyscaling through average method, partitioning and sampling. The system was implemented on an ordinary PC having Linux Ubuntu as operating system, imageLab as library, and C++ as programming language. The results regarding accuracy are presented in different tables. Based on the results, the system has minimum accuracy of 98%.

关键词： image Human Skin Darkness Comparison Using Digital image Analysis

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Using deep convolutional neural networks with adaptive activation functions for medical CT brain image Classification

Using deep convolutional neural networks with adaptive activ...

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Iranian conference of Biomedical Engineering (ICBME)

作者： Roxana Zahedinasab Hadis Mohseni Computer Engineering Department Shahid Bahonar University of Kerman Kerman Iran

ISBN: (纸本)9781538679531;9781538679524

Recently, imaging has become an essential component in many fields of medical research. Analysis of the diverse medical image types requires sophisticated visualization and processing tools. Deep neural networks have introduced themselves as one of the most important branches of machine learning and have been successfully used in many fields of pattern recognition and medical imaging applications. Among the different networks, convolutional neural networks which are biologically inspired variants of multilayer perceptions are widely used in the medical imaging field. In these networks, activation function plays a significant role especially when the data come in different scales. There is a hope to improve the performance of these networks by using adaptive activation functions which adapts their parameters to the input data. In this paper, we have used a modified version of a successful convolutional neural network tuned for medical image classification and investigated the effect of applying three types of adaptive activation functions on that. These activation functions combine basic activation functions in linear (mixed) and nonlinear (gated and hierarchical) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset). The experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.

关键词： Computed tomography Convolutional neural networks Biological neural networks Neurons Medical diagnostic imaging

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A Survey of Fingertip Character Identification in Open-Air Using image processing and HCI

A Survey of Fingertip Character Identification in Open-Air U...

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international conference for Convergence of Technology (I2CT)

作者： Deepti A. Barhate Krunal P. Rane Computer Department MPSTME Shirpur India Electronics and Telecommunication Institute of Technology and Research center Nashik India

Finger spelling recognition using finger motion in blaring area is quiet challenging as communicator are far away from each other they cannot communicate properly as well as this system is also be useful in character recognition on screen or on mobile displays which may help older people, children and computer illiterate people by just making spelling in air using their fingers they can see the word on screen or communicate with each other in blaring area. This system not only just eliminate the barrier of the communication in noisy places but also improve the human computer interaction. this paper is Review on above mentioned system. A 3D camera is used for detecting finger motion and outlines are recognized with one of the characters and numbers in air. Proposed method trials the color of the fingertip and mines the color using filter which gives best results in blaring area also. We will use red colored tape to finger of user to enhance the process captured by finger motion capturing, as it is easy to capture the red color of path than the color of the moving finger. For identification of character or number we are using OCR technology. In view of a situation where the person wants to write a small text or an e-mail and if typing by keyboard or touch was the only choice, they would need to wait for others for help so this system may be useful for them. The major objective of the proposed system is to deliver an commercial result that can change the achieved finger's motion of alphabets to text in a editor and help as effective means of HCI. The major application of this system will be at traffic control system, airports, enterprises, virtual environment, 3D gaming and so on. This paper presents the study of the methodologies useful for identifying motion in real time application

关键词： Thumb image color analysis Character recognition Videos Gesture recognition Optical character recognition software

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Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

Feature Extraction using Convolution Neural Networks (CNN) a...

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Manjunath Jogin Mohana M S Madhulika G D Divya R K Meghana S Apoorva R. v. College of Engineering Bengaluru India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

The image classification is one of the preliminary processes, which humans learn as infants. The fundamentals of image classification lie in identifying basic shapes and geometry of objects around us. It is a process which involves the following tasks of pre-processing the image (normalization), image segmentation, extraction of key features and identification of the class. The current image classification techniques are much faster in run time and more accurate than ever before, they can be used for extensive applications including, security features, face recognition for authentication and authorization, traffic identification, medical diagnosis and other fields. The idea of image classification can be solved by different approaches. But the machine learning algorithms are the best among them. These algorithms are based on the idea proposed years ago, but couldn't be implemented due to lack of computational power. With the idea of deep learning, the models are trained better and are able to identify different levels of image representation. The convolutional neural networks revolutionized this field by learning the basic shapes in the first layers and evolving to learn features of the image in the deeper layers, resulting in more accurate image classification. The idea of Convolutional neural network was inspired by the hierarchical representation of neurons by Hubel and Wiesel in 1962, their work was based on the study of stimuli of the visual cortex in cat. It was a fundamental breakthrough in the field of computer vision in understanding the working of visual cortex in humans and animals. In this paper feature of an images is extracted using convolution neural network using the concept of deep learning. Further classification algorithms are implemented for various applications.

关键词： Training Neurons Classification algorithms Support vector machines Biological neural networks Feature extraction image classification

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Topic Selection of Final Year Project for computer Science Programme

Topic Selection of Final Year Project for Computer Science P...

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2018 3rd international conference on Education and Management Science(ICEMS 2018)

作者： Peng-bo BO Xiao-miao ZHANG Harbin Institute of Technology

Final Year Project（FYP） is an essential component in various programmes of college education. Topic selection for Final Year Project is one of the most important factors which have significant impacts on student training. In this paper, we discuss topic selection of Final Year Project in the discipline of computer science and computer software. We provide several typical topics in which an application software is designed and developed which make use of knowledges in image processing, computer graphics and data visualization. Software developing toolkits employed in these projects are discussed. The main points of these projects are explained in details and results are shown by pictures as well.

关键词： Final year project Student training College education computer graphics

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Utilization of Digital image processing In Process of Quality Control of The Primary Packaging of Drug Using Color Normalization Method 3rd

Utilization of Digital Image Processing In Process of Qualit...

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3rd IAES international conference on Electrical Engineering, computer Science and Informatics (EECSI)

作者： Erwanto, Danang Prasetyowati, Sri Arttini Dwi Susila, Eka Nuryanto Budi Univ Islam Sultan Agung Semarang Fak Teknol Ind Tekn Elektro Semarang Indonesia Univ Islam Sultan Agung Semarang Fak Teknol Ind Semarang Indonesia

In the process of quality control, accuracy is required so that the improper drug packaging is not included into the next production process. The automatic inspection system using digital image processing can be applied to replace the manual inspection system done by humans. The image captured from the vision sensor is RGB image which is then converted into grayscale. The process of converting RGB image into grayscale image is performed using the color normalization method to spread the data of RGB colors at each pixel. From the software of image processing using the color normalization method that have been created, it shows grayscale images on the drug object which have degrees of gray higher than the grayscale image section of the background when the degree of the R, G or B color of drug is higher than the degree of the R, G, B color on the background of packaging. The determination of threshold value indicates that the binary image of the drug is white and a binary image of the background of drug packaging is black.

关键词： Color Normalization Object of Drug Thresholding Object Counting Outside Corners Inside Corners

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An Adaptive Light Algorithm for Extracting Track Information and Its Control

An Adaptive Light Algorithm for Extracting Track Information...

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第三届机电控制技术与交通运输国际学术会议

作者： Dong Liu Yujun Wang School of Computer and Information Science Southwest University

On the path-tracking problem of autonomous vehicle and the extraction threshold of track,which is affected by the light, An unpowered intelligent vehicle and the corresponding control algorithm are designed in the paper. First, the vehicle's mechanism is designed. Then, some structures for mounting the camera and the steering gear are optimized. Next, the camera named LQ1286 is used to detect the path information in which the algorithm is adapted for image binarization processing that extracting the white line for track recognition. Following, PID algorithm is designed to adjust the angle of the steering servo mounted on the vehicle which is driven forward by external fan wind. Thus the patrol is completed intelligently. Experimental results show that the design of intelligent vehicle is able to complete the desired objectives.

关键词： Unpowered vehicle path tracking PID algorithm

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