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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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A Heterogeneous Implementation of the Sobel Edge Detection Filter Using OpenCL

A Heterogeneous Implementation of the Sobel Edge Detection F...

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International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Theodora Sanida Argyrios Sideris Minas Dasygenis Department of Electrical & Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9781728166872

ISBN: (纸本)9781728166889

Today, edge detection is a cornerstone technique as edges are essential in many applications, such as image processing and biometric imaging. One popular algorithm for edge detection is the Sobel. Many researchers have focused on accelerating the Sobel filtering, but to the best of our knowledge we are the first to propose a 5×5 convolution kernel implementation using OpenCL. In this work, we implement the Sobel filter, one of the most effective and popular edge detection algorithms in image processing, in the OpenCL programming language. From the implementation of the Sobel algorithm we compare the performance of the CPU and GPU through OpenCL, in typical images ranging from 64×64 to 4096×4096 pixels. The Sobel operator uses a pair of 3×3 horizontal and vertical convolution kernels for edge detection functions. We apply 3×3 and 5×5 convolution kernels using OpenCL and compare them. The results have shown that for all image sizes, the GPU speed up ranges from 11,18 to 15,46 times with 3×3 convolution kernels, while speed up is from 10,05 to 13,46 times for the 5×5 convolution kernels. Finally, the results of our implementation are compared to other existing implementations and found to achieve better performance.

关键词： Kernel Convolution image edge detection Graphics processing units image resolution Computer languages Computer architecture

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A Study on Smart Parking Solutions Using IoT

A Study on Smart Parking Solutions Using IoT

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International conference on Intelligent Sustainable systems (ICISS)

作者： Chandana, M. Aniruddh, M. C. Fathima, Anam Chandan, G. Kumari, Chaya H. A. Vidyavardhaka Coll Engn CSE Dept Mysuru India

ISBN: (纸本)9781538677995

With the evolution of Internet of things and the popularity of concepts of smart cities, it is now possible to achieve these concepts and implement them in reality. Lots of efforts are being put in to increase the productivity and reliability of infrastructures in urban city space. Key problems of the urban cities are traffic congestion, limited availability of parking spaces and time wasted in search of these limited parking spaces. These problems can be addressed using IoT systems. In this paper we have conducted a survey on the existing parking systems and have explained the main methodology used in each of the existing systems mentioned. Besides the main findings from the survey, with our engineering insights we have also come up with the solution that can efficiently be used for smart parking to overcome the disadvantages that we have discovered in the existing systems. Lastly, we describe the architecture and functionality of our solution using IoT.

关键词： Internet of Things image processing Smart Parking Smart City Cloud of Things Open CV algorithms GSM module RFID tags HSV images Cameras Arduino boards

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Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using image Filtering algorithms

Identifying Asthma genetic signature patterns by mining Gene...

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IEEE International conference on Imaging systems and Techniques (IST) . IEEE International School on Imaging

作者： Hachim, Mahmood Yaseen Mahboub, Bassam Hamid, Qutayba Hamoudi, Rifat Univ Sharjah Coll Med Sharjah Inst Med Res Sharjah U Arab Emirates Rashid Hosp Dubai U Arab Emirates UCL Div Surg & Intervent Sci London England

ISBN: (纸本)9781728138688

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets.

关键词： microarray image analysis transcriptomics normalization coefficient of variance

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Deep Learning Based Vehicle Detection with images Taken from Unmanned Air Vehicle

Deep Learning Based Vehicle Detection with Images Taken from...

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Innovations in Intelligent systems and Applications conference

作者： Mustafa Böyük Ramazan Duvar Oğuzhan Urhan Uçak Elektrik-Elektronik Bölümü Kocaeli Üniversitesi Kocaeli Türkiye Elektronik ve Haberleşme Mühendisliği Kocaeli Üniversitesi Kocaeli Türkiye

ISBN: (数字)9781728191362

ISBN: (纸本)9781728191379

Especially in recent years, unmanned aerial vehicles (UAV) have been used in many platforms for military, commercial and entertainment purposes. Thanks to high computational graphics cards, many computer vision applications based on deep learning can work in real time on unmanned aerial vehicles. In this study, fast and high-performance object detection algorithms Faster R-CNN, YOLOv3-Tiny and SSD algorithms are used to detect vehicles in image taken from UAVs. Thus, the positions of vehicles driving in traffic are found automatically.

关键词： Unmanned aerial vehicles Real-time systems Object detection Deep learning Vehicle detection Proposals Graphics processing units

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image Super Resolution with Sparse Data Using ANFIS Interpolation

Image Super Resolution with Sparse Data Using ANFIS Interpol...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Muhammad Ismail Jing Yang Changjing Shang Qiang Shen Dept. of Computer Science Faculty of Business and Physical Sciences Aberystwyth University Wales UK

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

image super resolution is one of the most popular topics in the field of image processing. However, most of the existing super resolution algorithms are designed for the situation where sufficient training data is available. This paper proposes a new image super resolution approach that is able to handle the situation with sparse training data, using the recently developed ANFIS (Adaptive Network based Fuzzy Inference System) interpolation technique. In particular, the training image data set is divided into different subsets. For subsets with sufficient training data, the ANFIS models are trained using standard ANFIS learning procedure, while for those with insufficient data, the models are obtained through ANFIS interpolation. In the literature, little work exists for image super resolution on sparse data. Therefore, in the experimental evaluations of this paper, the proposed approach is compared with existing super resolution methods with full data, demonstrating that this work is able to produce highly promising results.

关键词： Training data Interpolation Training image resolution Testing Dictionaries Standards

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Classification of Durian Fruits based on Ripening with Machine Learning Techniques

Classification of Durian Fruits based on Ripening with Machi...

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International conference on Intelligent Sustainable systems (ICISS)

作者： Muthulakshmi. A P. N. Renjith Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Eating fruits is good for health and an excellent source of essential vitamins and minerals. But fruits are unfit for intake if they are not fully ripened. Ripening of fruits is an important phase in Pomology based on which the fruits are categorized. In this context, various machine learning techniques were proposed to detect the classification of fruits. This paper provides a special architecture to identify the features of different classes of Durian fruits. Durian, popularly given the name, "king of fruits", is one of the rarest and sweetest fruits grown in South Asia. The model of image processing helps to divide the classification into two parts of the feature extraction part and classification part of the fruit used. In this article, the system is evaluated using the datasets of the Durian Fruit. The datasets is divided into two parts as training and testing. By applying the Edge Detection and color extraction the features of the durian are properly measured. Finally, the performance is measured using Non-Destructive Machine Learning techniques such as SVM, GNB, Random Forest. The results obtained provide the best accuracy of 89.3 % using the SVM technique and 84.3% using Random Forest.

关键词： image color analysis Support vector machines image edge detection Feature extraction Classification algorithms Machine learning algorithms Random forests

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MFCC coefficient and ANN classifier applied to roadway classification 31

MFCC coefficient and ANN classifier applied to roadway class...

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31st IEEE International conference on Microelectronics (ICM)

作者： Atibi, Mohamed Boussaa, Mohamed Atouf, Issam Bennis, Abdellatif Tabaa, Mohamed Hassan II Univ Casablanca Fac Sci Ben Msik LTI Lab Casablanca Morocco EMSI Casablanca LPRI Lab Casablanca Morocco

ISBN: (纸本)9781728140582

Currently, Intelligent driver assistance systems provide more satisfactory solutions to the field of road safety. These are based on the use of powerful image or signal processing algorithms in terms of speed, accuracy of calculation and decision making. This document describes an intelligent roadway classification system in real-time in 4 classes: asphalt, gravel, snow and stone road. This system relies on the use and combination of two algorithms;the Mel Frequency Cepstrum Coefficient algorithm that extracts the characteristics of each type of road from the sound due to friction of the vehicle wheels with the road, and Multilayer Perceptron algorithm, which receives its characteristics to its inputs and classifies them. This document also describes the stages of the process of learning and testing. The robustness of this system is shown by the obtained satisfactory experimental results that combine between the respective advantages of the MFCC descriptor and MLP classifier.

关键词： Intelligent driver systems roadway classification Mel Frequency Cepstrum Coefficient artificial neuron network

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Effects of image Enhancement Techniques on CNNs Based algorithms for Segmentation of Blood Vessels: A Review

Effects of Image Enhancement Techniques on CNNs Based Algori...

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International conference on Advances in Big Data, Computing and Data Communication systems (icABCD)

作者： Olubunmi Omobola Sule Serestina viriri Abdultaofeek Abayomi UKZN Westville Campus Durban South Africa Mangosuthu University of Technology Durban South Africa

ISBN: (数字)9781728167701

ISBN: (纸本)9781728167718

image quality can either boost or reduce segmentation sensitivity and predictive accuracy of micro vessels from retinal fundus images. The main cause of difficulty in segmentation task particularly in medical imaging such as retinal fundus images, can be majorly attributed to image degradation as a result of noise, low contrast, uneven illumination, obscurity and so on. image quality degradation leads to loss of information and can affect both the classification and segmentation accuracy of Convolutional Neural Networks (CNNs) as well as diagnosis and analysis. Regardless of how good a CNNs model is, blur and noise can reduce the pixels classification accuracy by exhibiting partial biasness. To this end, this paper aims at reviewing the existing CNNs based method for the segmentation of blood vessels from the retinal fundus image. The review focuses majorly on the image enhancement technique(s) used to address the image quality degradation at pre-processing, processing and postprocessing stages and examines the effects on the CNNs based method performance. The review will aid a critical analysis of the effects of these enhancement techniques, particularly on sensitivity and Area under the ROC curve (AUC) metrics. The percentage of these two metrics are good and dependable yardstick to a successful model in terms of the blood vessels segmentation performance evaluation.

关键词： image segmentation Retina Biomedical imaging Blood vessels Sensitivity image enhancement

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Proximity Contours: vision based Detection and Tracking of Objects in Manufacturing Plants using Industrial Control systems 17

Proximity Contours: Vision based Detection and Tracking of O...

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17th IEEE International conference on Industrial Informatics (INDIN)

作者： Shishira, S. Rao, vidyadhar Sudarsan, Sithu D. ABB Corp Res Ctr Bengaluru India

ISBN: (纸本)9781728129273

In a typical manufacturing setup, field sensors are used to detect the presence of moving material on the production line. Sensory information is interfaced with a centralized control systems for continuous and long distance monitoring. However, the field sensors are prone to failures and may cause frequent down-times which in turn causes reduction in productivity. In this paper, a practical and comprehensive visual inspection system is proposed to automatically control the plant operations. The proposed approach improves productivity and solves several challenges including the speed of execution to support real-time operations, number and choice of image processing stages as well as associated techniques and algorithms. The presented solution inspects a production process deployed in metal industry and detects the moving rods on a conveyor platform. The shape of the moving rods is characterized using a combination of GrabCut segmentation, and clustering-based image thresholding methods. To detect and track the movement of rods, proximity contours are identified using low-level pixel information of only two images: one when the conveyor is fully occupied with the rods and the other when the conveyor is empty. Further, this visual sensory information is used to automatically generate control logic for tracking and monitoring a conveyor platform and can be smoothly integrated with industrial control system enabling real-time closed loop operations.

关键词： image Segmentation Material Detection Closed-loop systems Industrial Control systems Industry 4.0

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The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

The efficiency of deep learning algorithms for detecting ana...

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Information Technology and Nanotechnology (ITNT), International conference on

作者： Konstantin Dobratulin Andrey Gaidel Aleksandr Kapishnikov Anna Ivleva Irina Aupova Pavel Zelter Faculty of Informatics Samara National Research University Samara Russia Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and Photonics" RAS Samara Russia SamSMU MOH Russia Samara Russia Laboratory of Analysis and Modeling of Complex Systems ICCS RAS Samara Russia FSBEI HE SamSMU MOH Russia Samara Russia

ISBN: (数字)9781728170411

ISBN: (纸本)9781728170428

In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by agroupoforthodontists.

关键词： Deep learning image segmentation Head Neural networks Convolutional neural networks Task analysis Biomedical imaging

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