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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Amey Kulkarni Ali Jafari Colin Shea Tinoosh Mohsenin Department of Computer Science & Electrical Engineering University of Maryland Baltimore County

ISBN: (纸本)9781509023578

The four V's in Big data sets, Volume, Velocity, Variety, and Veracity, provides challenges in many different aspects of real-time systems. Out of these areas securing big data sets, reduction in processing time and communication bandwidth are of utmost importance. In this paper we adopt Compressive Sensing (CS) based framework to address all three issues. We implement compressive Sensing using Deterministic Random Matrix (DRM) on Artix-7 FPGA, and CS reconstruction using Orthogonal Matching Pursuit (OMP) algorithm on Virtex-7 FPGA. The results show that our implementations for CS sampling and reconstruction are 183x and 2.7x respectively faster when compared to previously published work. We also perform case study of two different applications i.e. multi-channel Seizure Detection and image processing to demonstrate the efficiency of our proposed CS-based framework. CS-based framework allows us to reduce communication transfers up to 75% while achieving satisfactory range of quality. The results show that our proposed framework is 290x faster and has 7.9x less resource utilization as compared to previously published AES based encryption.

关键词： image reconstruction Big data Field programmable gate arrays Compressed sensing Matching pursuit algorithms Kernel Encryption

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Design of an embedded system for automatically determination of the varieties of wheat seeds grown in Turkey based on image analysis

Design of an embedded system for automatically determination...

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International Conference on Agro-Geoinformatics

作者： Sercan Aygün Bilgi Görkem Yazgaç Mürvet Kırcı Ece Olcay Güneş Department of Electronics and Communication Engineering Istanbul Technical University Istanbul Turkey

Use of certified seeds, increases the quality and quantity of yield. Typically, for the certification, the analysis and classification process is made by experts using visual characteristics of the seeds. These conventional methods are very time consuming, very tedious, costly, and depend on the person. In the determination of the seed properties process, first, identification of the seed types, varieties and identification of diseased and structural deformed seeds operations are performed. Wheat is one of the world's most important grains, as well as being the main source of food is an important industrial raw material. There are many works in the literature about determination of varieties of wheat seeds. Also, it is very important to confirm the variety of the wheat before planting. Because each variety needs its own condition for taking good yield. Previous work of our research group was based on classification of seed images by using image analysis in the computer environment. Classification of images has been a keep-up-to date method for recent years. Same like images that have close textures and colors are to be classified using some scientific approaches via image processing techniques. Depending on our previous work, same approach that run on MATLAB is to be realized in an embedded system which is independent from the computer. This Linux running embedded card stores data to be classified in an external memory. Classification algorithm is constructed by using Linux based image processing libraries. Rest of the paper is going to introduce scientific methods to classify images together with literature review. After, algorithmic details and some results are going to be presented related to system performance metrics and accuracy rates of algorithm based on computer-free embedded card.

关键词： Embedded systems image analysis Feature extraction Classification algorithms Agriculture Hardware

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A Robust Methodology for Performance Analysis on Hybrid Embedded Multicore Architectures

A Robust Methodology for Performance Analysis on Hybrid Embe...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S Guyancourt France SATIE Université Paris Saclay Orsay France

ISBN: (纸本)9781509035328

Today's vehicles increasingly embed software intelligence in order to be safer for the driver, and to achieve autonomous driving in a close future. To answer the computational needs of these algorithms, system-on-chip (SoC) suppliers propose heterogeneous architectures. With such complex SoCs, embedding applications in vehicle becomes more and more complex for car manufacturers. Indeed, it is not trivial to find the best suited SoC for a given application, and to define load balancing strategies when working with heterogeneous architectures. These difficulties can be overcome by using performance prediction, based on computing architectures models. To build these models, we provide a set of test vectors which automatically extract key characteristics of tested architectures. Our methodology is able to perform a complete computing architecture model, by using 3 different levels of tests, each one characterizing a specific situation representative of real applications. We aim to obtain performance prediction for different applications, for any embedded SoCs based on models performed with this methodology. In this paper, we describe our characterization methodology, and show results obtained with embedded SoCs used for automotive applications.

关键词： Computer architecture Kernel Graphics processing units Benchmark testing image processing Vehicles

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A Review of Automatic Methods Based on image processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear images

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JOURNAL OF MEDICAL systems 2016年第1期40卷 17-17页

作者： Panicker, Rani Oomman Soman, Biju Saini, Gagan Rajan, Jeny Natl Inst Technol Karnataka Mangalore India Coll Engn Trikaripur Kerala India Sree Chitra Tirunal Inst Med Sci & Technol AMCHSS Trivandrum Kerala India

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming;a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

关键词： Automatic methods image processing Microscopic images Tuberculosis screening

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Mitosis detection using convolutional neural network based features

Mitosis detection using convolutional neural network based f...

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International Symposium on Computational Intelligence and Informatics

作者： Abdulkadir Albayrak Gokhan Bilgin Department of Computer Engineering Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781509039104

Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis detection for cancer diagnosis and grading by histopathological images. The proposed framework is tested on the MITOS data set provided for a contest on mitosis detection in breast cancer histological images released for research purposes in International Conference on Pattern Recognition (ICPR'2014). By using provided histopathological images, cellular structures are initially found by combined clustering based segmentation and blob analysis after preprocessing step. Then, obtained cellular image patches are cropped automatically from the histopathological images for feature extraction stage. CNN, which is a prominent deep learning method on image processing tasks, is utilized for extracting discriminative features. Due to the high dimensional output of the CNN, combination of PCA and LDA dimension reduction methods are performed respectively for regularization and dimension reduction process. Afterwards, a robust kernel based classifier, support vector machine (SVM), is used for final classification of mitotic and non-mitotic cells. The test results on MITOS data set prove that the proposed framework achieved promising results for mitosis detection on histopathological images.

关键词： Feature extraction image segmentation Breast cancer Neural networks Clustering algorithms Kernel

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Hybrid modeling of an OffLine Arabic Handwriting Recognition System AHRS

Hybrid modeling of an OffLine Arabic Handwriting Recognition...

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International Conference on Engineering & MIS (ICEMIS)

作者： Ons Meddeb Mohsen Maraoui Shadi Aljawarneh Universite de Monastir Monastir TN Jordan University of Science and Technology Irbid JO

ISBN: (纸本)9781509055807

Handwriting Recognition HR is the old dream of all those who need to enter data into a computer. In this article, we present a state of the art in the field of handwriting recognition by focusing primarily on Arabic handwritten script which we present an overview of the Offline Automatic Arabic Handwriting Recognition systems AHRS and the techniques and strategies used. Then, to try to resolve the problems inherent in Arabic handwriting, within the general framework of Arabic Handwriting Recognition and to meet the need to test a new method of learning, we model a new Offline Arabic Handwriting Recognition System AHRS which we detail the architecture of our system and the contributions offered at each stage: preprocessing, segmentation, features extraction, classification and post-processing.

关键词： Handwriting recognition Hidden Markov models Feature extraction Filtering algorithms image segmentation Transforms Data models

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YUV空间中基于稀疏自动编码器的无监督特征学习

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电子与信息学报 2016年第1期38卷 29-37页

作者：李祖贺樊养余王凤琴西北工业大学电子信息学院西安710072 郑州轻工业学院计算机与通信工程学院郑州450002

现有无监督特征学习算法通常在RGB色彩空间进行特征提取,而图像和视频压缩编码标准则广泛采用YUV色彩空间。为了利用人类视觉特性和避免色彩空间转换所消耗的计算量,该文提出一种基于稀疏自动编码器在YUV色彩空间进行无监督特征学习的... 详细信息

现有无监督特征学习算法通常在RGB色彩空间进行特征提取,而图像和视频压缩编码标准则广泛采用YUV色彩空间。为了利用人类视觉特性和避免色彩空间转换所消耗的计算量,该文提出一种基于稀疏自动编码器在YUV色彩空间进行无监督特征学习的方法。首先在YUV空间随机采集图像子块并进行白化处理,然后利用稀疏自动编码器进行无监督局部特征学习。在预处理阶段,针对YUV空间亮度和色度通道相互独立的特性,提出一种将亮度和色度进行分离的白化措施。最后用学习到的局部特征在大尺寸图像上进行卷积操作从而获得全局特征,并送入图像分类系统进行性能测试。实验结果表明:只要对亮度分量进行适当的白化处理,在YUV空间中的无监督特征学习就能够获得相当于甚至优于RGB空间的彩色图像分类性能。

关键词：图像分类无监督特征学习稀疏自动编码器卷积神经网络深度学习

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A New Descriptor for Retrieving 3D Objects Applied in Congestive Heart Failure Diagnosis

A New Descriptor for Retrieving 3D Objects Applied in Conges...

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Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： Helton H. Bíscaro Hellyan Oliveira Leila C. C. Bergamasco Fátima L. S. Nunes Laboratory of Computer Applications for HealthCare University of Sâo Paulo São Paulo Brazil

ISBN: (纸本)9781509035694;9781509035687

Content-Based image Retrieval (CBIR) aims to retrieve similar graphical objects from large databases based on their contents. CBIR requires definition of descriptors, algorithms that condense information from the object in order to represent it usually as a real number or a vector in Rn. This article presents the Spectral Descriptor, a new descriptor designed for retrieving three-dimensional geometric objects applied to aid the diagnosis of Congestive Heart Failure (CHF). Our descriptor is based on techniques of compressive sensing and rewrites the coordinates of 3D objects vertices on a basis on which they have a sparse representation. Tests with surfaces reconstructed from heart MRI images, specifically from left ventricle, show that the descriptor has presented a good performance, reaching an average precision of approximately 85% for CHF and 71% for non-CHF cases, maintaining high levels of precision. Results also showed that the Spectral Descriptor can decrease the high dimensionality of features vectors in CBIR systems.

关键词： Three-dimensional displays Feature extraction Heart Sparse matrices image reconstruction Matrix decomposition Databases

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Implementation of high dynamic range rendering on acute leukemia slide images using contrast stretching

Implementation of high dynamic range rendering on acute leuk...

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International Conference on Electronic Design (ICED)

作者： Leow Bin Toh M.Y. Mashor P. Ehkan H. Rosline A.K. Junoh N.H. Harun School of Computer and Communication Engineering Electronic & Biomedical Intelligent Systems (EBItS) Research Group School of Mechatronics Engineering Department of Hematology Universiti Sains Malaysia Kubang Kerian Kelantan Malaysia. Universiti Malaysia Perlis Arau Perlis MY School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia.

ISBN: (纸本)9781509021611

Acute leukemia is one of the critical disease that requires immediate treatment due to the rapid progression and accumulation of the cancerous cells. In recent years, image processing techniques had been explored to enhance the diagnosis of acute leukemia. However, microscopic image captured from the light microscope usually has poor quality due to the capability of the camera and improper operation by human operator. High Dynamic Range (HDR) imaging technique has been explored to solve the problem by increasing the dynamic range of the images captured. This paper presents a HDR rendering technique by using contrast stretching technique to enhance the morphological features of blast cells. The technique called Partial contrast stretching had been used to render HDR image. The results showed that the proposed method had enhanced the overall contrast and morphological features of the blast cells in the acute leukemia slide images.

关键词： image color analysis Dynamic range Histograms Heuristic algorithms Cameras Mathematical model

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Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform

Real-Time Top-View People Counting Based on a Kinect and NVI...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Guangqin Li Peng Ren Xinrong Lyu He Zhang College of Information and Control Engineering China University of Petroleum (East China) Qingdao China

ISBN: (纸本)9781509059119

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.

关键词： Head Real-time systems Cameras Universal Serial Bus Graphics processing units Signal processing algorithms image resolution

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