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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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image processing and image pattern recognition A Programming Tutorial 1

Image Processing and Image Pattern Recognition A Programming...

引用

1st IEEE international conference on Artificial Intelligence for Industries (AI4I)

作者： Chakraborty, Amit Appl Data Tech Bangalore Karnataka India

ISBN: (纸本)9781538692097

image recognition is a major area of application of machine learning - evolving at a rapid pace with a number of programming platforms available to developers. While each platform has its own uniqueness, the methodology of image recognition consists of a sequence of image processing tasks, development of a classifier algorithm, training and testing followed by deployment. This tutorial will delve into the programming aspects of image processing including thresholding, contouring and template matching. In order to provide practical hands on programming this tutorial will closely look at three real life applications of image pattern recognition namely ALPR using Tesseract OCR and will touch upon using CNN for character detection. The tutorial will explain the algorithm, implementation of pseudocode through Python using two major platforms: OpenCV and Tensorflow.

关键词： image recognition

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Deep Convolutional Network Based Saliency Prediction for Retrieval of Natural images

Deep Convolutional Network Based Saliency Prediction for Ret...

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1st international conference on Intelligent Computing and Optimization (ICO)

作者： Nandagopalan, Shanmugam Kumar, Pandralli K. Vemana Inst Technol Dept Informat Sci & Engn Bangalore Karnataka India VTU Res Resource Ctr Dept MCA Belagavi India

ISBN: (纸本)9783030009793;9783030009786

Content Based image Retrieval (CBIR) is an important area in the field of image processing and analysis. A novel method is proposed in this paper in order to retrieve visually similar images. The method uses the visual attention model to extract the saliency map of a given image with the help of SalNet algorithm. It is based on deep learning methods which have shown that many difficult computer vision problems can be solved by machine learning algorithms and more specifically by Deep Convolution Neural Networks (DCNNs). Using this model, first the saliency region or segment is detected from the images and then the traditional visual features such as color histogram, texture, Histograms of Oriented Gradients (HOG), etc. are computed and are stored in the feature database. Using saliency detection will make our retrieval process easier and accurate as the salient regions in the image is automatically detected. Hence, we can retrieve the most visually similar images with respect to a given query image, because saliency regions exactly map what humans visually perceive. The experimental dataset contains 1000 images including horses, elephants, food, African people, etc. from WANG database. Our results show that the proposed method is efficient and accurate compared to other previously existed models.

关键词： CBIR DCNN SalNet Deep learning Saliency

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Pellet sphericity and size analysis by image processing technique 1

Pellet sphericity and size analysis by image processing tech...

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1st international conference on Applied machine learning, ICAML 2019

作者： Nanda, Anuja Patra, Akshaya Kumar Panigrahi, Santisudha Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9781728139081

Preparation of iron ore fines as feed to blast furnace involves production of spheres of typically 9-16mm diameter through pelletization. The process combines agglomeration and thermal treatment to convert the raw ore into pellets appropriate for use in a blast furnace. The use of pellets of varying shape and size has attracted much interest. The permeability of blast furnace is dependant on the size and shape of the pellets. The productivity of pelletization is based on generation of pellets of size range of 9-16mm. The pelletization process is governed by the physical characteristic of pellets and therefore it is necessary to investigate accurately the shape and size of the pellets in pelletization process. It is desirable to produce nearly spherical pellets with a desired diameter range. In the process of pelletization the pellets may be produced of different sizes. This may be accepted if it is within certain tolerance limit. Therefore, it is essential to measure the size and provide a quantitative value for the shape of the pellets. The size can be measured by many methods such as using a scale or caliper and solid displacement method etc. However, it is very difficult to measure the size and shape of green pellets without physically handling them. Since they are wet and they change their size and shape during measurement. Some researchers have worked using imaging techniques for pellet size analysis [3-4]. This motivated to develop such a system. An attempt is made here to measure the shape and size of the pellets using 2D-digital image processing techniques. Online image processing method is also presented for calculating the sphericity and the size of a single/multiple pellet by software and its toolboxes. The technique can suitably derive the size distribution of a number of pellets. The technique is validated by conducting a number of experiments. © 2019 IEEE.

关键词： Size distribution

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Global Minimum Depth in Edwards-Anderson Model 20th

Global Minimum Depth in Edwards-Anderson Model

引用

20th international conference on Engineering Applications of Neural Networks (EANN) /1st international Workshop on Pervasive Intelligence (PEINT)

作者： Karandashev, Iakov Kryzhanovsky, Boris RAS Sci Res Inst Syst Anal Ctr Opt Neural Technol Nakhimovskiy Prosp 36B1 Moscow 117218 Russia RUDN Univ Peoples Friendship Univ Russia 6 Miklukho Maklaya St Moscow 117198 Russia

ISBN: (纸本)9783030202576;9783030202569

In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exact polynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit N -> infinity. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machine learning and image processing.

关键词： Spectrum Local minimum Global minimum Spin system Spin glass system Minimization Exact polynomial algorithm Edwards-Anderson model Planar Ising model

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A study of English Neologisms Through Large-Scale Probabilistic Indexing of Bentham’s Manuscripts 2nd

A Study of English Neologisms Through Large-Scale Probabilis...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Toselli, Alejandro H. Romero, Verónica Vidal, Enrique Sánchez, Joan Andreu Seaward, Louise Schofield, Philip PRHLT Universitat Politècnica de València Camí de Vera s/n València46022 Spain Bentham Project UCL Laws 4-8 Endsleigh Gardens LondonWC1H 0EG United Kingdom

ISBN: (纸本)9783030307530

Probabilistic indexes (PI) are obtained from untranscribed handwritten text images by means of recently introduced lexicon-free, query-by-string, probabilistic keyword spotting techniques. PIs have proven to be a powerful tool that allow efficient, free textual searching in very large collections of handwritten historical documents. PIs convey uncertain information about the textual contents of the document images. However, text uncertainty is accurately modeled by the associated lexical probability distributions, which can be conveniently exploited in many applications. As an example of these applications, here we study the dating of a number of English neologisms in the large collection of Bentham’s manuscripts, which encompass images. The statistical techniques used for neologism dating are theoretically motivated and experiments on this collection are reported. Among other interesting contributions of this study, it provides sound evidence that some commonly assumed neologism introduction dates need to be revised. © 2019, Springer Nature Switzerland AG.

关键词： Probability distributions

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Face recognition system Using Deep Neural Network with Convolutional Neural Networks 3

Face recognition system Using Deep Neural Network with Convo...

引用

3rd international conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Fernando, Erick Andwiyan, Denny Fitria Murad, Dina Touriano, Derist Irsan, Muhamad Information System Department School of Information Systems Bina Nusantara University Indonesia School of Information Technology Indonesia Information System Department Binus Online Learning Bina Nusantara University Indonesia Information Technology Department Faculty of Computer Science and Engineering University of Adi Wangsa Indonesia

Face recognition has long been a hot topic and challenging research point in areas such as image processing, pattern recognition, and machine vision. The face is a biometric feature with the intrinsic nature of a human. So that the face has self-stability, deep individual differences and can be an ideal basis for verification of an identity. In this research use, Deep learning Network method uses to perform detection or face recognition. In this study, we present a framework that can be used to detect faces. This research is also able to present a DNN model that is used to study data sources from the data stream in sequence. The most important part of this study is able to adjust the capacity of the model from the simple one. This research uses experimental design method. The first step is a collection of face image data. Then the architecture design starts from the determination of the depth of the network, layout layers, and the selection of layer types that will be used to get the model based on input dataset and label name index. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Deep learning Algorithm for Predictive Maintenance of Rotating machines Through the Analysis of the Orbits Shape of the Rotor Shaft 1st

Deep Learning Algorithm for Predictive Maintenance of Rotati...

引用

1st international conference on Smart Innovation, Ergonomics and Applied Human Factors, SEAHF 2019

作者： Caponetto, R. Rizzo, F. Russotti, L. Xibilia, M.G. University of Catania DIEEI Catania Italy TEORESI S.p.A. Catania Italy Department of Engineering University of Messina Messina Italy

ISBN: (纸本)9783030229634

The prediction of failures in rotating machines is an important issue in industries to improve safety, to reduce the cost of maintenance and to prevent accidents. In this paper a predictive maintenance algorithm, based on the analysis of the orbits shape of the rotor shaft is proposed. It is based on an autonomous image pattern recognition algorithm, implemented by using a Convolutional Neural Network (CNN). The CNN has been designed, by using a suitable database, to recognize the orbits shape, allowing both fault detection and classification. © 2019, Springer Nature Switzerland AG.

关键词： Maintenance

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Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer 1st

Deep learning with lung segmentation and bone shadow exclusi...

引用

1st international conference on Computer Science, Engineering and Education Applications, ICCSEEA2018

作者： Gordienko, Yu. Gang, Peng Hui, Jiang Zeng, Wei Kochura, Yu. Alienin, O. Rokovyi, O. stirenko, S. National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Huizhou University Huizhou City China

ISBN: (纸本)9783319910079

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03). © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Graphics processing unit

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A Survey on Extreme learning machine and Evolution of Its Variants 2nd

A Survey on Extreme Learning Machine and Evolution of Its Va...

引用

2nd international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

作者： Ghosh, Subhasmita Mukherjee, Himadri Obaidullah, Sk Md. Santosh, K.C. Das, Nibaran Roy, Kaushik Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science West Bengal State University Kolkata India Department of Computer Science The University of South Dakota VermillionSD United States Department of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789811391804

Extreme learning machine (ELM) is most popular emerging learning algorithm that modify classical ‘Generalized’ single hidden layer feed forward network. Though some traditional gradient based learning algorithm like variant Levenberg-Marguardt (LM) and Back propagation (BP) are widely utilized for training in multi layer FFNN but some drawbacks of this mechanism are the most prime issue to promote ELM. It imparts efficient learning solutions for different practices of classification and regression under supervise learning. ELM sharply deals with the messes arise from the gradient based learning algorithm like stopping criteria, learning rate, learning epoch, local minimum etc. But due to some fallibility the different concepts of variants of ELM are presented. This paper clarifies about Extreme learning machine along with different types of variants. © 2019, Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Multiple Object Detection in 360° Videos for Robust Tracking 8th

Multiple Object Detection in 360° Videos for Robust Trackin...

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8th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Kumar, V. Vineeth Naik, Shanthika Sarvani, Polisetty L. Pattanshetti, Shreya M. Mudenagudi, Uma Maralappanavar, Meena Patil, Priyadarshini Tabib, Ramesh A. Vandrotti, Basavaraja S. KLE Technol Univ Hubballi India Samsung R&D Inst Bangalore Karnataka India

ISBN: (纸本)9783030348724;9783030348717

In this paper, we propose an efficient way to detect objects in 360 degrees videos in order to boost the performance of tracking on the same. Though extensive work has been done in the field of 2D video processing, the domain of 360. video processing has not been explored much yet, as it poses difficulties such as (1) unavailability of the annotated dataset (2) severe geometric distortions at panoramic poles of the image and (3) high resolution of the media which requires high computation capable machinery. The state-of-the-art detection algorithm involves the use of CNN (Convolution Neural Networks) trained on a large dataset. Faster RCNN, SSD, YOLO, YOLO9000, YOLOv3 etc. are some of the detection algorithms that use CNN. Among these, though YOLOv3 might not be the most accurate, it is the fastest, and this trade-off between speed and accuracy is acceptable. We improvise upon this algorithm, to make it suitable for the 360 degrees dataset. We propose YOLO360, a CNN network to detect objects in 360 degrees videos and thus increase the tracking precision and accuracy. This is achieved by performing transfer learning on YOLOv3 with the manually annotated dataset.

关键词： Computer vision Equirectangular frames 360 degrees images Detection Tracking Transfer learning

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