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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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Nonsubsampled contourlet transform combined with genetic algorithms for registration of satellite imaging

Nonsubsampled contourlet transform combined with genetic alg...

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international conference on Signals, Circuits and Systems, SCS

作者： Fatiha Meskine Miloud Chikr El Mezouar Nasreddine Taleb Department of Electrotechnics University of Mascara Mascara Algeria Department of Electronics University of Sidi-Bel Abbes Sidi Bel-Abbes Algeria

ISBN: (纸本)9781424443970;9781424443987

Digital image registration is a process by which the most accurate match is determined between two images which may have been taken at the same or different viewpoints. The registration process determines the optimal transformation which will align the two images. This has applications in many fields as diverse as medical images analysis, pattern matching and computer vision for robotics, as well as remotely sensed data processing. A wide range of registration techniques has been developed for many different types of applications and data. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. Therefore, in this work, we describe an efficient algorithm of image registration that uses genetic algorithms within a multi-resolution framework based on the wavelet and the nonsubsampled contourlet transform (NSCT). We combine local search methods with global ones balancing exploration and exploitation, to speed up the search of the best transformation parameters. Experimental results show that the NSCT is a promising method for registration of satellite images compared to the wavelet transform.

关键词： Genetic algorithms Satellites Application software Wavelet transforms Digital images Biomedical imaging image analysis Pattern matching computer vision Robot sensing systems

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Brazilian Republic Presidency Mobile Telephony Consumption Cost Reduction with Outliers Detection

Brazilian Republic Presidency Mobile Telephony Consumption C...

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international conference on image, vision and Computing (ICIVC)

作者： Rodrigo Hagstrom Ricardo Carvalho Rommel Carvalho Heitor Faria Tassio Melo Welder Luz Priscila Solis Department of Computer Science (CIC) University of Brasilia (UnB) Brasilia DF Brazil

ISBN: (纸本)9781538649923

After the Federal Court of Audit's decision on Judgment 1601/2005, the Brazilian Government has sought to reduce expenditure on telephone expenses. In this understanding, the Presidency of the Republic acquired Telecom Expense Management software, which proposes to provide telephone management, focusing on the efficiency in the use of resources and reducing the risk of having particular phone calls paid by the government. However, the software has not yet resulted in the volume of savings expected and required after the issuance of Decree 8.540/2015, which sets goals for reducing the consumption of resources with telephone expenses. The present paper studies the behavior of organization mobile phones of Brazilian Republic Presidency, in order to detect outliers through clustering processes as K-means, FarthestFirst and others, composed by calls registers with characteristics (time, duration and day of the week) that are not related to the corporation use. The results accused that 33 thousand calls were suspect of being private, about 10%, against 0.5% of previous solution and a confirmation that 90% of the suspicious calls obtained by the K-means method could be refunded in a sample presented to some employees of the Presidency of the Republic.

关键词： Software Risk management Telephone sets Standards Anomaly detection

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Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network

Skin Diseases Classification Using Local Binary Pattern and ...

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international conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Nurul Akmalia Poltak Sihombing Suherman Faculty of Computer Science and Information Technology Universitas Sumatera Utara Faculty of Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9781728124766

Skin disease is one of the diseases that are often found in tropical countries like Indonesia. Lack of knowledge about the types and prevention of skin diseases results a person suffering from acute skin diseases. computer technology is expected to help detect disease early so that it can minimize the occurrence of more dangerous diseases. This paper proposes a method for introducing the shape, color, and texture of skin diseases in digital images and classifying the results of image analysis based on the type of disease in human skin. The method used is a combination of Local Binary Pattern (LBP) and Convolutional Neural Network (CNN) methods which can later be used as sensors or vision for skin diseases automatically. The results of this study can help in the early identification of skin diseases, helping parties who want to know the image value of skin diseases by using LBP and classifying it based on the type of disease using CNN. This study shows the level of accuracy of combining LBP with CNN is quite high with an average value of 92%. In addition, this research can also be used as reference material for the development of further research in image processing that uses LBP and classification using CNN.

关键词： Skin Diseases Feature extraction Convolutional neural networks Lesions image color analysis Histograms

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3D Saliva Ferning images to Determine The Women's Fertility Rates 19

3D Saliva Ferning Images to Determine The Women's Fertility ...

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Proceedings of the 3rd international conference on Video and image processing

作者： Cut Maisyarah Karyati Aries Muslim Daryl Diningrat Gunadarma University Depok West Java Indonesia

ISBN: (纸本)9781450376822

In this paper we will discuss the process of processing salivary images which will be represented by salivary fern patterns into 3D shapes and determine the level of fertility visually with existing theories. The author raises this theme because it can make it easier to recognize the pattern of ferns in saliva in determining the level of female fertility in the health field. The work stages in this paper start from the study of literature, data collection, designing 3D applications, making 3D applications and testing. The stages of processing the saliva image used by the author's computer in the process of image improvement, edge detection, cropping and masking, segmentation and 3-dimensional image representation.

关键词： 3D Saliva Ferning Edge Detection computer vision Matlab image processing

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Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using Deep Learning Deployment in Effective Application

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

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computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Md Abrar Hamim Jeba Tahseen Kazi Md Istiyak Hossain Nasrin Akter Umme Fatema Tuj Asha Department of Computer Science Daffodil International University Dhaka Bangladesh

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent.

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The Precise Recognition of Moving Object in Complex Background

The Precise Recognition of Moving Object in Complex Backgrou...

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3rd international conference on Natural Computation (ICNC 2007), vol.2

作者： Weiyao Huang Zhijing Liu Wenjuan Pan School of Computer Science and Technology Xidian University Xi'an China

Moving object detection is currently one of the most active research topics in the domain of computer vision and video processing. In this paper, a simply and fast quadric binaryzation method is proposed to remove environmental noise points effectively. Moreover, based on the stability of drawing the contour from fixed scene, the background subtraction method is combined with the time-stepping method, an effective method which could be used to detect the movement areas. Through the average weighted of two means can realize to enhance the accuracy in distinguishing target. Experiment results have shown that this method gives stable performances and good robustness.

关键词： Morphology image segmentation Object detection Working environment noise image analysis image recognition Surveillance image coding Shape measurement computer science

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A component based architecture to improve testability, targeted FPGA-based vision systems

A component based architecture to improve testability, targe...

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international conference on Communication Software and Networks, ICCSN

作者： Mostafa Pordel Nima Moghaddami Khalilzad Farahnaz Yekeh Lars Asplund Department of Computer Science Umeå University Umea Sweden School of Innovation Design and Engineering Mälardalen University Vasteras Sweden

FPGA has been used in many robotics projects for real-time image processing. It provides reliable systems with low execution time and simplified timing analysis. Many of these systems take a lot of time in development and testing phases. In some cases, it is not possible to test the system in real environments very often, due to accessibility, availability or cost problems. This paper is the result of a case study on vision systems for two robotics projects in which the vision team consisted of seven students working for six months fulltime on developing and implementing different image algorithms. While FPGA has been used for real-time image processing, some steps have been taken in order to reduce the development and testing phases. The main focus of the project is to integrate different testing methods with FPGA development. It includes a component based solution that uses a two-way communication with a PC controller for system evaluation and testing. Once the data is acquired from the vision board, the system stores it and simulates the same environment that has been captured earlier by feeding back the obtained data to FPGA. This approach addresses and implements a debugging methodology for FPGA based solutions which accelerate the development phase. In order to transfer massive information of images, RMII which is an interface for Ethernet communication, has been investigated and implemented. The provided solution makes changes easier, saves time and solves the problems mentioned earlier.

关键词： Field programmable gate arrays Robots Process control Monitoring Switches Clocks Reliability

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Marjana Tahmid Samiul Alam Kalim Akram Kazi Muhammad Asif Ashrafi Dept. of CSE East Delta University Chittagong Bangladesh Dept. of EEE East Delta University Chittagong Bangladesh Dept. of CSE Ulm University Ulm Germany

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and computer vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single image Super Resolution Quality Enhancement

Comparative Study of Implementation of Very Deep Super Resol...

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international conference on Applied Electromagnetics, Signal processing and Communication (AESPC)

作者： Ganesan P M. Ravichandran B.S. Sathish L.M.I.Leo Joseph G. Sajiv R. Murugesan Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Saveetha School of Engineering SIMATS Chennai India Dept. of Electronics and Communication Engineering Narshima Reddy Engineering College Secunderabad India

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results.

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Research on Building Crack Width Monitoring Method Based on computer vision

Research on Building Crack Width Monitoring Method Based on ...

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Signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Houyan Wang Shuoting Xiao Ningbo Youzuo Construction Technology Company Ningbo Zhejiang China Institute of Civil Engineering and Architecture Ural Federal University Ekaterinburg Russia

As construction projects enter a new era, building crack width monitoring has entered a stage of high-quality development. It is necessary to propose image processing propositions that better meet the needs of crack width monitoring for building safety by focusing on the deep learning idea with image segmentation as the core. Based on the dynamic evolution of image processing development, according to the internal logic of the convolutional neural network, a theoretical analysis framework for the development of image segmentation is constructed. It can explain the image segmentation development mechanism jointly generated by the image processing mechanism and the optimization loop mechanism involved in image sampling and segmentation. From the perspective of quality change and practical deduction of image segmentation development, the possibility of moving towards the high-quality development goal of building crack width monitoring is further explored. The purpose of developing building crack width monitoring is to provide crack width estimates that meet expected standards for construction projects, which is committed to continuously improving the quality of crack width and improving the satisfaction of crack width monitoring. Therefore, it is necessary to strengthen the image segmentation control based on the quality of the inner loop of the convolutional neural network. Establishing an interaction and feedback mechanism between image segmentation and perception of the quality of a construction project, as well as establishing an evaluation system for image segmentation and monitoring of the crack width of a building, which will achieve high-quality development of building crack width monitoring, promote construction project safety, and truly meet the needs of construction projects.

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