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

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Architecture-Centric Bottleneck Analysis for Deep Neural Network Applications

Architecture-Centric Bottleneck Analysis for Deep Neural Net...

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International conference on High Performance Computing

作者： Jihyun Ryoo Mengran Fan Xulong Tang Huaipan Jiang Meena Arunachalam Sharada Naveen Mahmut T. Kandemir Pennsylvania State University Intel

ISBN: (纸本)9781728145365

The ever-growing complexity and popularity of machine learning and deep learning applications have motivated an urgent need of effective and efficient support for these applications on contemporary computing systems. In this paper, we thoroughly analyze the various DNN algorithms on three widely used architectures (CPU, GPU, and Xeon Phi). The DNN algorithms we choose for evaluation include i) Unet - for biomedical image segmentation, based on Convolutional Neural Network (CNN), ii) NMT - for neural machine translation based on Recurrent Neural Network (RNN), iii) ResNet-50, and iv) DenseNet - both for image processing based on CNNs. The ultimate goal of this paper is to answer four fundamental questions: i) whether the different DNN networks exhibit similar behavior on a given execution platform? ii) whether, across different platforms, a given DNN network exhibits different behaviors? iii) for the same execution platform and the same DNN network, whether different execution phases have different behaviors? and iv) are the current major general-purpose platforms tuned sufficiently well for different DNN algorithms? Motivated by these questions, we conduct an in-depth investigation of running DNN applications on modern systems. Specifically, we first identify the most time-consuming functions (hotspot functions) across different networks and platforms. Next, we characterize performance bottlenecks and discuss them in detail. Finally, we port selected hotspot functions to a cycle-accurate simulator, and use the results to direct architectural optimizations to better support DNN applications.

关键词： Pipelines Graphics processing units Computer architecture Recurrent neural networks Random access memory Deep learning

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Accelerating image Algorithm Development using Soft Co-Processors on FPGAs 29

Accelerating Image Algorithm Development using Soft Co-Proce...

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29th Irish Signals and systems conference (ISSC)

作者： Deng, Tiantai Crookes, Danny Woods, Roger Siddiqui, Fahad Queens Univ Belfast Sch EEECS Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781538660461

FPGAs can offer high performance with low power and low hardware usage. However, with current software, FPGAs are hard to program, and lengthy re-synthesis times make them unsuitable for the algorithm experimentation which is typical of developing image processing applications. In this paper, we present a system model based on a set of Soft Co-Processors, each of which implements a basic image-level operation (or a common combination of such operations) based on the high-level operators in image Algebra. Both 'debug' (generic but unoptimised) and 'release' (specific and optimised) versions of the Soft Co-Processors can be used. The advantage of debug mode is that no re-synthesis is required during algorithm experimentation. For release mode, a novel macro-based transformation tool enables the creation of a set of reusable HLS skeleton co-processors which require the user only to write a C function to obtain a new, special-purpose Soft Co-Processor. Initial experiments with several algorithms show that the area and speed overheads for using debug (rather than release) mode are both around 25-30%, thus enabling algorithm development to take place on the FPGA itself. For creating function-specific Co-processors using our macro-based tool, the overheads compared with an expert hardware design are around 20%.

关键词： image processing FPGA image Algebra

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CrackSense: A CrowdSourcing Based Urban Road Crack Detection System

CrackSense: A CrowdSourcing Based Urban Road Crack Detection...

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IEEE conference on SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation

作者： Wang, Liang Yang, Congying Yu, Zhiwen Liu, Yimeng Wang, Zhu Guo, Bin Northwestern Polytech Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781728140346

As a common road surface distress, cracks pose a serious threat to road infrastructure and traffic safety in cities today. Consequently, road crack detection is considered as an essential step for effective road maintenance and road structure sustainability. However, due to the high cost incurred by dedicated devices and professional operators, it is impossible for existing systems to achieve universal spatiotemporal coverage across citywide road networks. To fill this gap, in this paper, we present the CrackSense, a mobile crowdsourcing based system to detect urban road crack and estimate its damage degree. Specifically, for the heterogeneous crack data, we put forward a crowdsourcing data quality evaluation and selection mechanism. And then, by utilizing the multi-source sensing data aggregation, we propose tow algorithms, namely RCTR and RCDE, to recognize road crack types, i.e., horizontal crack, vertical crack, and net crack, and estimate the crack damage degree, respectively. We implement the system and develop a smartphone APP for mobile users. By conducting intensive experiments and field study, the results demonstrate the accuracy and effectiveness of our proposed approaches.

关键词： Mobile crowdsourcing road crack detection image processing sensors

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image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems

作者： Can Jin Zhiwei Ye Lingyu Yan Ye Cao Aixin Zhang Lie Ma Xiang Hu Jiwei Hu School of Computer Science Hubei University of Technology Wuhan China Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Wuhan Jingzhi Technology Co. Ltd. Wuhan China Wuhan FiberHome Technical Services Co. Ltd. Wuhan China

image segmentation is the indispensable part in the field of computer vision. There are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Clustering algorithms image segmentation Optimization Convergence Linear programming Standards Gaussian noise

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Security using image processing and deep convolutional neural networks

Security using image processing and deep convolutional neura...

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2018 IEEE International conference on Innovative Research and Development, ICIRD 2018

作者： Kotapalle, Goutham Reddy Kotni, Sachin Cisco Systems Inc. Bengaluru India Walmart Labs Bengaluru India

ISBN: (纸本)9781538656969

Safety has, for a long time, been one big thing everyone is concerned about. Security breach of private locations has become a threat that everyone intends to eliminate. The traditional security systems trigger alarms when they detect a security breach. However, the usage of image processing coupled with deep learning using convolutional neural networks for image identification and classification helps in identifying a breach in an enhanced fashion thereby increasing security furthermore to a great extent. This is due to its capability to extract complex features from the images using accurate and advanced face and body detection algorithms. The rate at which machine learning, especially deep learning, is transitioning is very high. The use of such technology in taking the existing systems and models to the next level would be a great step towards advancements in every field of science and technology. The same goes with computer vision. These two coupled and brought together to be used in the field of security results in achieving a lot more than what is imagined to be possible and this paper aims to do the same. © 2018 IEEE.

关键词： Microcontrollers

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A review on various image fusion algorithms

A review on various image fusion algorithms

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2017 International conference on Recent Innovations in Signal processing and Embedded systems, RISE 2017

作者： Jha, Nivedita Saxena, Aumreesh Ku. Shrivastava, Amit Manoria, Manish PG CSE SIRTS Bhopal MP India CSE SIRTS Bhopal MP India

ISBN: (纸本)9781509047604

The process of image fusion can be defined as the process of combining multiple input images into a single composite image. Our aim is to create a single output image from the collection of input images which contains a better explanation of the view than the one provided by any of the individual input images. The fundamental problem of image fusion is one of determining the best procedure for combining the several input images. The review adopted in this paper is that combining various images with prior information is best handled within a statistical outline. The manuscript presented here is a representative collection of the latest advances in the field of image Fusion (IF) which is offered and a range of methods that are causative to its growth are presented. It describes the spatial and transforms domain fusion techniques such as, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Discrete Cosine Transform (DCT) and wavelet domain techniques and others. © 2017 IEEE.

关键词： Principal component analysis

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image Enhancement algorithms of terahertz images Based on Quantum Probability. Statistics 43

Image Enhancement Algorithms of terahertz Images Based on Qu...

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43rd International conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)

作者： Zhu, Zhongbo Li, Xiaojun Tan, Qinggui Jiang, Wei Liang, Dong CAST Natl Key Lab Sci & Technol Space Microwave Xian Shaanxi Peoples R China

ISBN: (纸本)9781538638095

With the advancement of THz technologies, THz imaging has been progressed significantly and has huge potential applications in medical diagnosis, environmental control, remote sensing, chemical and biological identification. In the past, people mainly focused on overcoming the obstacles in the hardware, such as the limitation of resolution, accuracy and speed in THz imaging system. Improvement of imaging quality is of great significance to the application of terahertz imaging system. In this paper, image enhancement which based on quantum probability statistics is employed to enhance the contrast and get the better profile of THz image. The experimental results are analyzed and discussed. It is shown that our method considered both global and local image information and can improve images quality effectively. The method can effectively suppress the image noise while preserved the object structures well.

关键词： Imaging Signal processing algorithms image enhancement Stationary state Filtering algorithms Probability

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Algorithm of Weed Detection in Crops by Computational vision

Algorithm of Weed Detection in Crops by Computational Vision

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International conference on Electronics, Communications and Computers (CONIELECOMP)

作者： A. J. Irías Tejeda R. Castro Castro Faculty of engineering and architecture Universidad Tecnológica Centroamericana (UNITEC) Tegucigalpa Honduras

This research has been based on the use of precision agriculture tools for the management of weeds in crops. It has focused on the creation of an image-processing algorithm to detect the existence of weeds in a specific site of crops. The main objective has been to obtain a formula so that a weed detection system can be developed through binary classifications. The initial step of image processing is the detection of green plants in order to eliminate all the soil in the image, reducing information that is not necessary. Then, it has focused on the vegetation by segmentation and eliminating unwanted information through medium and morphological filters. Finally, a labeling of objects has been made in the image so that weed detection can be done using a threshold based on the area of detection. This algorithm establishes an accurate monitoring of weeds and can be implemented in automated systems for the eradication of weeds in crops, either through the use of automated sprayers for specific site or a weed-cutting mechanism. In addition, it increases the performance of operational processes in crop management, reducing the time spent searching for weeds throughout a plot of land and focusing weed removal tasks on specific sites for effective control.

关键词： Agriculture Indexes Sensitivity image segmentation Matlab Classification algorithms

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Searching for Efficient Multi-Scale Architectures for Dense image Prediction 32

Searching for Efficient Multi-Scale Architectures for Dense ...

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32nd conference on Neural Information processing systems (NIPS)

作者： Chen, Liang-Chieh Collins, Maxwell D. Zhu, Yukun Papandreou, George Zoph, Barret Schroff, Florian Adam, Hartwig Shlens, Jonathon Google Inc Mountain View CA 94043 USA

The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems.

关键词： Forecasting

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Portable Blood Typing Device Using image Analysis

Portable Blood Typing Device Using Image Analysis

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IEEE International conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Jennifer C. Dela Cruz Ramon G. Garcia Annissa vi C. Diaz Angelika Mae B. Diño Danielle Jane I. Nicdao Christine Shayne S. Venancio Mapúa University

ISBN: (纸本)9781728133379

Blood type can be determined by the presence or absence of antigens in the red blood cells, and can be classified by the ABO (A, B, AB, O) and Rh D (either positive or negative) systems. Knowing one's blood type is one of the most crucial steps before blood transfusion or any medical operations to prevent the risk of receiving incompatible blood that could lead to adverse or even fatal reactions to patients. Although fully automated blood testing instruments are already being used in some major hospitals, its large size and long processing time, limit its ability to be used in emergency situations. Hence, during onsite blood typing, the traditional or the slide method is being used, which is less accurate due to human errors. This paper presents a raspberry pi based image processing system that is capable of determining all eight types of blood using Canny Edge and Contour Detection. All blood types detected by the proposed system matched that of the known blood samples for the controlled testing of all five samples with five trials each sample for the known A+, B+ AB+, O+, A-, B-, AB- and O-. Uncontrolled testing was also performed to compare the results of the ten random blood types identified by the proposed prototype to the results obtained from test tube method. All these ten samples matched the results obtained from the clinical laboratory. This portable and automated device could avoid human errors, without risking accurate results that could be obtain in a short period of time.

关键词： Antigens Blood typing Canny edge Contour detection Blood Typing contour detection antigens HUMAN ERROR image analysis image processing systems

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