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

共 1671 条记录，以下是971-980 订阅

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Medical image Segmentation via Optimized K-Means 3

Medical Image Segmentation via Optimized K-Means

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3rd international conference on Current Trends in computer, Electrical, Electronics and Communication, CTCEEC 2017

作者： Alzurbi, Reem Anushya, A. Hamed, Ebtisam Alsharar, Eng. Abdelnour Angela Vincy, B.S. College of Computer Engineering Science University of Hail Saudi Arabia AlSha'ar Communication Engineering Colloge of Prince Colloge of Prince for Information Technology Albalqa Applied University Saudi Arabia Jeppiaar Mamammalan Engineering College Chennai India

ISBN: (纸本)9781538632420

The main purpose of this paper is to identify the suitable algorithm for image segmentation. Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods by various researches for medical image segmentation have been presented in this paper. In the course of the comparative analysis, it is evidently demonstrated that the traditional k-means is not appropriate for segmenting the medical images. But K-means gives the best results for image segmentation with smaller number of K-values. Accordingly K-means can be mixtured with snake optimization method and Normalized Cut algorithm and the proposed algorithm is labeled as optimized K-means. © 2017 IEEE.

关键词： image segmentation

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An experimental evaluation of binary feature descriptors 3rd

An experimental evaluation of binary feature descriptors

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3rd international conference on Advanced Intelligent Systems and Informatics, AISI 2017

作者： Alshazly, Hammam A. Hassaballah, M. Ali, Abdelmgeid A. Wang, G. Department of Mathematics Faculty of Science South Valley University Qena83523 Egypt Department of Computer Science Faculty of Computers and Information South Valley University Luxor Egypt Department of Computer Science Faculty of Computers and Information Minia University Al Minia61519 Egypt Department of EECS School of Engineering The University of Kansas LawrenceKS66045 United States

ISBN: (纸本)9783319648606

Efficient and compact representation of local image patches in the form of features descriptors that are distinctive/robust as well as fast to compute and match is an essential and inevitable step for many computer vision applications. One category of these representations is the binary descriptors which have been shown to be successful alternatives providing similar performance to their floating-point counterparts while being efficient to compute and store. In this paper, a comprehensive performance evaluation of the current state-of-the-art binary descriptors;namely, BRIEF, ORB, BRISK, FREAK, and LATCH is presented in the context of image matching. This performance evaluation highlights several points regarding the performance characteristics of binary descriptors under various geometric and photometric transformations of images. © 2018, Springer international Publishing AG.

关键词： Digital arithmetic

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Hand gesture based quadcopter control using image processing and adaptive machine learning 3

Hand gesture based quadcopter control using image processing...

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3rd IEEE international conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Dixit, Krishna R. Verma, Tarun Subramanya, U.S. Umadevi, V. BMS College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781538624401

Accurately recognizing gesture in real time is a complex problem to solve. It can be addressed to some extent using colored gloves and markers, but models so built do not generalize well. Another option is to utilize boundary based gesture recognition, but these are more complex to build and prediction is time consuming. Through this work we try to resolve both these issues by construction of a straightforward machine learning architecture which accurately implements gesture recognition in real time and can also be utilized to build a generalized model. The solution we have developed is based on the premise that an architecture which can contextually recognize gestures and can also adaptively identify variations to the same can be used for solving problems ranging from body language detection, translating sign language to devising an intuitive method for controlling UAVs (Unmanned Aerial Vehicles). © 2018 IEEE.

关键词： Machine learning

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Dealing with Topological Information Within a Fully Convolutional Neural Network 19th

Dealing with Topological Information Within a Fully Convolut...

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19th international conference on Advanced Concepts for Intelligent vision Systems (ACIVS)

作者： Decenciere, Etienne Velasco-Forero, Santiago Min, Fu Chen, Juanjuan Burdin, Helene Gauthier, Gervais Lay, Bruno Bornschloegl, Thomas Baldeweck, Therese PSL Res Univ MINES ParisTech Ctr Math Morphol Fontainebleau France LOreal Res & Innovat 550 Jinyu Rd Shanghai Peoples R China ADCIS SA 3 Rue Martin Luther King F-14280 St Contest France LOreal Res & Innovat 1 Ave Eugene Schueller F-93601 Aulnay Sous Bois France

ISBN: (纸本)9783030014490;9783030014483

A fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information. A solution is proposed in this paper to solve this problem, based on pre-processing with a geodesic operator. It is applied to the segmentation of histological images of pigmented reconstructed epidermis acquired via Whole Slide Imaging.

关键词： Histological image segmentation Convolutional neural network Geodesic operators Mathematical morphology

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Detection for Pulmonary Nodules using RGB Channel Superposition Method in Deep Learning Framework 3

Detection for Pulmonary Nodules using RGB Channel Superposit...

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3rd international conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)

作者： Meng, Yishuang Yi, Ping Guo, Xuejun Gu, Wen Liu, Xin Wang, Wei Zhu, Ting Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Sch Med Dept Resp Med Shanghai 200240 Peoples R China Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)9781538681879

The detection of pulmonary nodules is a very important research field in computer-aided diagnosis. In order to help doctors to identify pulmonary nodules more conveniently, especially for some small pulmonary nodules, a method based on RGB channel superposition to detect pulmonary nodules is proposed in this paper. We put the same ROI (region of interest) from three sequential lung CT slices into RGB channels to gain a pseudo-color image for deep learning. AlexNet and GoogLeNet is used as the deep learning network We use 10000 patches of healthy tissues and 12000 patches of pulmonary nodules in LIDC-IDRI dataset for training and get a prediction model. The model is tested on 176 patients' CT images and gain the sensitive of 95.0% at 5.62 false positives per scan. The experimental results show that the proposed method can improve the detection rate of pulmonary nodules compared with some traditional feature extraction methods.

关键词： cancer computerised tomography diagnostic radiography feature extraction image classification image colour analysis image segmentation learning (artificial intelligence) lung medical image processing neural nets diagnostic radiography Lung image colour analysis X-Ray Computed Tomography Medical image processing Feature extraction Neural network image classification nodule image segmentation red-green-blue colour model nodules nodules Cancer ct of lungs

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Meme opinion categorization by using optical character recognition (OCR) and naïve bayes algorithm 3

Meme opinion categorization by using optical character recog...

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3rd international conference on Informatics and Computing, ICIC 2018

作者： Amalia, Amalia Sharif, Arner Haisar, Fikri Gunawan, Dani Nasution, Benny B. Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia Politeknik Negeri Medan Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9781538669204

Generally, a meme is an image which is produced by the society which is used to comment a certain event, followed with a particular template from the decent online images. The distribution of meme becomes the phenomenon and very popular in the last few years. The problem is, some of these memes contain negative contents to harm others. One type of meme image that trends in social media is aimed at government, either to support the performance of the government or to insinuate and dislike of a government. Therefore, Political view by the citizen can be identified by viral memes on the internet. The aim of this research is classifying the types of a meme by applying image processing and OCR Tesseract which are combined with Naïve Bayes Algorithm. OCR Tesseract is required to recognize text in an image, meanwhile Naive Bayes algorithm which is used to find the highest probability to classify the testing dataset into the correct category. This research uses a meme as the dataset. The result is meme which is successfully classified. The accuracy depends on the OCR result which utilizes tesseract engine. © 2018 IEEE.

关键词： Sentiment analysis

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image processing pipeline for web-based real-time 3D visualization of teravoxel volumes 3rd

Image processing pipeline for web-based real-time 3D visuali...

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3rd international conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th international conference on Swarm Intelligence, ICSI 2018

作者： Ashwini, Akanksha Kwon, Jaerock Kettering University FlintMI48504 United States

ISBN: (纸本)9783319938028

With high-throughput and high-resolution imaging technologies such as Knife-Edge Scanning Microscopy (KESM), it is possible to acquire teravoxel sized three-dimensional neuronal and microvascular images of the whole mouse brain with sub-micrometer resolution. It is imperative to be able to visualize and share these teravoxel volumes efficiently, to facilitate group efforts from research communities. However, due to the immense size of the data sets, sharing and managing them have always been a big challenge. This paper describes an image processing pipeline for a real-time 3D visualization framework that allows research groups to work in collaboration. The proposed work can visualize and share terabyte-sized three-dimensional images for study and analysis of mammalian brain morphology. Although the image processing pipeline used a KESM data set to show the feasibility of it, the proposed pipeline can also be used for other larger data sets. We believe that this novel framework for Web-based real-time 3D visualization can facilitate data sharing of teravoxel volumes across research communities. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Morphology

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Following a bright object through a simple computer-controlled system 3

Following a bright object through a simple computer-controll...

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3rd international conference on Recent Trends and Applications in computer Science and Information Technology, RTA-CSIT 2018

作者： Progri, Genti Maranga, Petrika University "A. Xhuvani" Elbasan Albania Polytechnic University of Tirana Tiranë Albania

This material will describe a simple object tracker system through the processing of images brought by a camera. The follow-up system described and accomplished in this material can serve to track any object in a camera image, so it is an almost universally follow-up system. The schematic system presented follows a way how by following the movement of the sun, the solar panel with the photo element is positioned in the direction where the active surface for the light flow gets the maximum value. The result of this panel positioning is that the generated current is maximized by the fact that the energy that falls on the photoelectric panel receives the maximum value. © 2018 CEUR-WS.

关键词： Cameras

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A review on Deep Learning in thyroid ultrasound computer-Assisted Diagnosis systems 3

A review on Deep Learning in thyroid ultrasound Computer-Ass...

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3rd IEEE international conference on image processing, Applications and Systems (IPAS)

作者： Khachnaoui, Hajer Guetari, Ramzi Khlifa, Nawres Univ Tuins El Manar Inst Super Technol Med Tunis Lab Biophys & Technol Med Tunis 1006 Tunisia Univ Tuins El Manar Inst Super Informat Tunis Lab LIMTIC Ariana 2080 Tunisia

ISBN: (纸本)9781728102474

Ultrasound is one of the most used imaging techniques for assessing and evaluating thyroid lesions. Indeed, it shows a good performance in terms of discrimination between benign and malignant thyroid nodules. Diagnosis by ultrasound is, however, not as easy as it seems and depends strongly on the experience of the radiologists. To help physician and radiologists to better diagnose, many computer-Assisted Diagnosis (CAD) systems have been developed. These systems are based on image processing techniques and on machine learning. They represent effective and useful tools allowing doctors to have a second opinion far from human subjectivity. Among the machine learning techniques, the Deep Learning has recently made rapid progress in interpreting medical imaging and has demonstrated an impressive efficiency. Various CAD systems treating ultrasound images of the thyroid have widely use it since then. This paper reviews the most recent research works on the CAD systems for analyzing ultrasound images of the thyroid to diagnose the benign or malignant nature of the thyroid nodules. The CAD systems studied in this paper are based on the Deep Learning. We present a brief description of the CAD systems based on the Deep Leaning. Specifically, we describe the data collection and the CNN implementation. We report also the results obtained in these studies and highlight the limitations of such studies. This literature review is aimed at researchers but also at physician who are interested in CAD tools in ultrasound images of the thyroid gland and can represent a state of the art for all those interested in the classification of medical imaging.

关键词： Deep Learning CAD system Thyroid nodule Ultrasound image

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Real-Time Embedded Intelligence System: Emotion Recognition on Raspberry Pi with Intel NCS 27th

Real-Time Embedded Intelligence System: Emotion Recognition ...

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27th international conference on Artificial Neural Networks (ICANN)

作者： Xing, Y. Kirkland, P. Di Caterina, G. Soraghan, J. Matich, G. Univ Strathclyde Glasgow Lanark Scotland Leonardo MW Ltd Sigma HouseChristopher Martin Rd Basildon SS14 3EL Essex England

ISBN: (纸本)9783030014186

Convolutional Neural Networks (CNNs) have exhibited certain human-like performance on computer vision related tasks. Over the past few years since they have outperformed conventional algorithms in a range of image processing problems. However, to utilise a CNN model with millions of free parameters on a source limited embedded system is a challenging problem. The Intel Neural Compute Stick (NCS) provides a possible route for running large-scale neural networks on a low cost, low power, portable unit. In this paper, we propose a CNN based Raspberry Pi system that can run a pre-trained inference model in real time with an average power consumption of 6.2 W. The Intel Movidius NCS, which avoids requirements of expensive processing units e.g. GPU, FPGA. The system is demonstrated using a facial image-based emotion recogniser. A fine-tuned CNN model is designed and trained to perform inference on each captured frame within the processing modules of NCS.

关键词： CNN Embedded system Low power system SWaP profile

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