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

共 14693 条记录，以下是4181-4190 订阅

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Machine Learning Based Sentiment Analysis on Spanish Financial Tweets 6th

Machine Learning Based Sentiment Analysis on Spanish Financi...

引用

World conference on Information systems and Technologies (WorldCIST)

作者： Garcia-Diaz, Jose Antonio Salas-Zarate, Maria Pilar Hernandez-Alcaraz, Maria Luisa Valencia-Garcia, Rafael Gomez-Berbis, Juan Miguel Univ Murcia Fac Informat Campus Espinardo Murcia 30100 Spain Inst Tecnol Orizaba Div Res & Postgrad Studies Orizaba Mexico Univ Carlos III Madrid Dept Informat Madrid Spain

ISBN: (纸本)9783319777023;9783319777030

Nowadays, financial data on social networks play an important role to predict the stock market. However, the exponential growth of financial information on social networks such as Twitter has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. The Natural Language processing (NLP) and sentiment analysis areas can solve this problem. In this respect, we propose a supervised machine learning method to detect the polarity of financial tweets. The method employs a set of lexico-morphological and semantic features, which were extracted with UMTextStats tool. Furthermore, we have conducted a comparison of the performance of three classification algorithms (J48, Bayes-Net, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 73.2%.

关键词： Sentiment analysis Financial domain Machine learning

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An Optimization Approach of Compressive Sensing Recovery Using Split Quadratic Bregman Iteration with l₀ Smoothed Norm 3

An Optimization Approach of Compressive Sensing Recovery Usi...

引用

3rd IEEE International conference on image processing, Applications and systems (IPAS)

作者： Yang, Guoan You, Yuning Lu, Zhengzhi Yang, Junjie Wang, Yuhao Xi An Jiao Tong Univ Sch Elect & Informat Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728102474

An optimization algorithm for image recovery is a core issue in the field of compressive sensing (CS). This paper deeply studied the CS reconstruction algorithm based on split Bregman iteration with l(1) norm, which enables the l(1) norm to approximate the original l(0) norm during the optimization process. Consequently, we proposed another novel algorithm improving the precision and the convergence speed based on split quadratic Bregman iteration (SQBI) with l(0) norm. Besides, we analyzed its convergence by proving two monotonically decreasing theorems. Inspired by previous researches, we applied smoothed l(0) norm for the optimization problem to replace the traditional to norm in CS. The improvement is made by using a Gaussian function to approximate the l(0) norm, transforming it into a convex optimization problem, and eventually achieved a convergent solution by the steepest descent method. The experimental results show that under the same conditions, compared with other state-of-the-art algorithms, the reconstruction accuracy of the CS reconstruction algorithm based on the SQBI with smoothed l(0) norm is improved significantly, and its convergence rate is also accelerated as well.

关键词： Compressive sensing sparse representation image recovery split qradratic Bregman iteration convex optimization

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Fusion Research of Visible and Infrared images Based on IHS Transform and Regional Variance Wavelet Transform 10

Fusion Research of Visible and Infrared Images Based on IHS ...

引用

10th International conference on Intelligent Human-Machine systems and Cybernetics, IHMSC 2018

作者： Zeng, Xiangjin Tian, Jinwen School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Automation College Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538658369

Aiming at the problem of low contrast between target and background in fusion of infrared and visible images, a fusion method of infrared and visible images based on IHS transform and wavelet region transformation is proposed. The first of the visible light IHS transform the luminance component of infrared image gray processing, then the luminance component of visible light and infrared image of three level wavelet transform to obtain high frequency component and low frequency component respectively, the low frequency sub-band coefficients fusion rules comparing the area difference, and then calculate the weighted wavelet coefficients;in the high frequency sub-band coefficients using regional variance adaptive fusion rules. The experimental results show that compared with other algorithms, the fusion image obtained by this method enhances the contrast between the thermal target and the background, and keeps the details of the background, which is more consistent with human vision perception. © 2018 IEEE.

关键词： image fusion

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Automatic flower and visitor detection system 26

Automatic flower and visitor detection system

引用

26th European Signal processing conference, EUSIPCO 2018

作者： Tran, Dat Thanh Høye, Toke Thomas Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Finland Department of Bioscience Aarhus University Aarhus Denmark Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

ISBN: (纸本)9789082797015

The visit patterns of insects to specific flowers at specific times during the diurnal cycle and across the season play important roles in pollination biology. Thus, the ability to automatically detect flowers and visitors occurring in video sequences greatly reduces the manual human efforts needed to collect such data. Data-dependent approaches, such as supervised machine learning algorithms, have become the core component in several automation systems. In this paper, we describe a flower and visitor detection system using deep Convolutional Neural Networks (CNN). Experiments conducted in image sequences collected during field work in Greenland during June-July 2017 indicate that the system is robust to different shading and illumination conditions, inherent in the images collected in the outdoor environments. © EURASIP 2018.

关键词： Deep neural networks

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Identification of Acute Lymphoblastic Leukemia in Microscopic Blood image Using image processing and Machine Learning algorithms 7

Identification of Acute Lymphoblastic Leukemia in Microscopi...

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7th International conference on Computing, Communications and Informatics (ICACCI)

作者： Rajpurohit, Subhash Patil, Sanket Choudhary, Nitu Gavasane, Shreya Kosamkar, Pranali MIT Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781538653142

Cancer has been plaguing the society for a long time and still there is no certain treatment;especially if detected in later stages. That is why early detection and treatment of cancer is of utmost importance. Acute lymphoblastic leukemia is a type of blood cancer which is known to progress very rapidly and prove fatal if there is a delay in detection. Detection of this type of cancer is carried out manually by observing the blood samples of patient under microscope and conducting various other tests. This process may produce undesirable drawbacks: slowness, non-standardized accuracy since it depends on examiner's / pathologist's capabilities and fatigue due to work overload can cause human errors in detection. A few automated systems for detection of Acute Lymphoblastic Leukemia (ALL) have been proposed which involve extracting features from blood images using MATLAB and implementing different classifiers to produce results, which gave remarkable accuracies though not enough for practical usage. Our proposed system is further improving the classification accuracy. It uses openCV and skimage for image processing to extract relevant features from blood image and not just sheer number of features and further classification is carried out using various classifiers: CNN, FNN, SVM and KNN of which CNN gives the highest accuracy of 98.33%. CNN and FNN are written using TensorFlow framework. The accuracies obtained by other classifiers: FNN, SVM, and KNN are 95.40%, 91.40% and 93.30% respectively.

关键词： Leukemia Lymphoblast Neural Network Support Vector Machine TensorFlow

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A Video processing Based Eye Gaze Recognition Algorithm for Wheelchair Control

A Video Processing Based Eye Gaze Recognition Algorithm for ...

引用

IEEE International conference on Dependable systems, Services and Technologies (DESSERT)

作者： Sudhir Rao Rupanagudi Varsha G. Bhat Shreya K. Gurikar S. Pranava Koundinya Sumedh Kumar M. S. Shreyas R. Shilpa S. Suman N.M. Rachana R. Bademi Manisha Koppisetti Vasanthi Satyananda WorldServe Education Bangalore India Sai Vidya Institute of Technology Bangalore India Gopalan College of Engineering & Management Bangalore India Atria Institute of Technology Bangalore India

Over the past few years, a lot of research has been carried out in eye gaze recognition and its applications. From controlling wheelchairs to selecting options on a screen, utilizing the gaze of an individual has become a long-sought way for performing these tasks and in turn making the life of several differently abled people easy. In this paper a novel methodology to perform iris segmentation and gaze recognition has been introduced and described. The method elaborated utilizes a segmentation algorithm which can successfully extract the iris under varying lighting conditions with the help of machine learning. All experiments were conducted using the MATLAB R2013a software and a speed improvement of almost 3.433 times was achieved as opposed to other popular methods of iris extraction. In terms of accuracy, the algorithm proved to be 86% accurate and was also adopted to control an actual wheelchair.

关键词： Iris recognition Iris Wheelchairs image segmentation Cameras Machine learning algorithms image color analysis

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Low-Cost Wearable Technology Model to Control and Monitor Chronic Diseases for Healthcare 25

Low-Cost Wearable Technology Model to Control and Monitor Ch...

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25th IEEE International conference on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Pumajulca-Salazar, Alcibiades Mac-Choy, Jorge Armas-Aguirre, Jimmy Univ Peruana Ciencias Aplicadas Informat Syst Engn Lima Peru

ISBN: (纸本)9781538654903

In this paper, we propose a low-cost wearable technology model to control and monitor chronic diseases for healthcare. We enhance prior models by focusing on low cost components, and we assess the current hardware and systems applications offered in the market. Our proposed model also emphasizes the application of real-time data analysis for the healthcare organization. The model consists of six components: 1. Data acquisition;2. Data processing;3. Information Results;4. Decision making Support;5. Technology Platform;6. Framework for personal data protection. The model was validated with a group of 12 females over 70 years who were diagnosed with tumors at different stages. The preliminary results show that the mean temperature difference between the armpits and malignant tumors is 0.7 +/- 0.5, while the mean temperature difference between the armpits and benign tumors is 2.1 +/- 0.5.

关键词： Low-Cost Wearable Chronic Disease Tumor algorithms and Complexity Intelligent systems Social Issues and Professional Practice Signal and image processing Control systems

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A novel method for the extraction of primary visual features from an image through intelligent feature descriptors

A novel method for the extraction of primary visual features...

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

作者： Deshpande, Abhinav V. Subashini, M. Monica School of Electronics Engineering V. I. T. University Vellore India

ISBN: (纸本)9781509047604

As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only there cognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as conversion of the original RGB images into HSV Color Space, Adjustment of the Contrast of the Color images as well as applying the Histogram of Oriented Gradients (HOG) algorithm in which the process of extraction and plotting of the HOG features from a given image is performed that is most popular amongst the feature extraction algorithms. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board. © 2017 IEEE.

关键词： Vehicles

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An image processing Pipeline for Long Range UAV Detection 3

An Image Processing Pipeline for Long Range UAV Detection

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conference on Emerging Imaging and Sensing Technologies for Security and Defence iii;and Unmanned Sensors, systems, and Countermeasures

作者： Schumann, Arne Sommer, Lars Mueller, Thomas Voth, Sascha Fraunhofer IOSB Inst Optron Syst Technol & Image Exploitat Fraunhoferstr 1 D-76131 Karlsruhe Germany

ISBN: (纸本)9781510621824

The number of affordable consumer unmanned aerial vehicles (UAVs) available on the market has been growing quickly in recent years. Uncontrolled use of such UAVs in the context of public events like sports events or demonstrations, as well as their use near sensitive areas, such as airports or correctional facilities pose a potential security threat. Automatic early detection of UAVs is thus an important task which can be addressed through multiple modalities, such as visual imagery, radar, audio signals, or UAV control signals. In this work we present an image processing pipeline which is capable of tracking very small point targets in an overview camera, adjusting a tilting unit with a mounted zoom camera (PTZ system) to locations of interest and classifying the spotted object in this more detailed camera view. The overview camera is a high-resolution camera with a wide field of view. Its main purpose is to monitor a wide area and to allow an early detection of candidates, whose motion or appearance warrant a closer investigation. In a subsequent process these candidates are prioritized and successively examined by adapting the orientation of the tilting unit and the zoom level of the attached camera lens, to be able to observe the target in detail and provide appropriate data for the classification stage. The image of the PTZ camera is then used to classify the object into either UAV class or distractor class. For this task we apply the popular SSD detector. Several parameters of the detector have been adapted for the task of UAV detection and classification. We demonstrate the performance of the full pipeline on imagery collected by the system. The data contains actual UAVs as well as distractors, such as birds.

关键词： UAV UAS drone detection classification tracking long range far distance deep learning convolutional neural network (CNN) flying objects

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Distributed processing and Data Fusion In Dynamic systems With Microsystem Sensor Networks 38

Distributed Processing and Data Fusion In Dynamic Systems Wi...

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IEEE 38th International conference on Electronics and Nanotechnology (ELNANO)

作者： Lyshevski, Sergey E. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA

ISBN: (纸本)9781538663837

Distributed processing and control are critical to supports distributed intelligence and autonomy of multi-degree-of-freedom motion systems. Measurements and fusion of spatiotemporal physical quantities imply data-intensive computing and spatial distribution of computing resources to enable control and processing of large data sets from image and inertial sensors. We examine distributed and asynchronous processing nodes which process information independently deriving partial solutions. There are multiple sensing-and-processing nodes in each individual agent. Each node comprises solid-state or MEMS multi-degree-of-freedom sensors with ASICs which process and fuse data. On-node computing supports distributed processing. Adaptive bottom-up organization ensures data aggregation and data management with operation on sub-samples or hashed sets of large source datasets. Cooperative distributed processing is essential in centralized, decentralized and behavioral coordination. In the centralized organization, a central processor may not ensure adequacy. A network of semi-autonomous on-device processing sensors may interact to solve specific tasks and validate solutions. Problem allocation, partitioning, coordination and other tasks are implemented using software- and hardware-supported algorithms and protocols. This paper contributes to design of next generation of systems with distributed multi-node processing capabilities.

关键词： distributed processing inertial sensors MEMS microsystems smart sensors

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