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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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Automatic summarization of Instagram social network posts by combining semantic and statistical approaches

Automatic summarization of Instagram social network posts by...

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International conference on Pattern Recognition and image Analysis (IPRIA)

作者： Zainab Tabanmehr Ehsan Akhtarkavan Department of Computer Engineering Khatam University Tehran Iran

The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of “automatic text summarization”. Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80 % ) of the proposed system.

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Research on statistical Algorithm of Microalgae Growth Status Based on Computer Vision 4

Research on Statistical Algorithm of Microalgae Growth Statu...

引用

4th IEEE Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2021

作者： Chen, Zichuan Cui, Shigang Zhang, Yongli He, Lin Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin300222 China

ISBN: (纸本)9781728185347

The automation of the growth state statistics of microalgae is the premise of the automation of microalgae culture. To reduce the dependence of statistical process on equipment, a statistical method of microalgae growth based on image processing was proposed. The microalgae cell is displayed prominently in the microalgae image preprocessed by Otsu method, and then the contour of the microalgae cell is extracted by edge detection method, and its coordinates are obtained, the connected region of the contour is used to obtain the number and radius of the microalgae in the image, and the growth state statistics is realized. The main difference between this method and the existing methods is that it does not depend on the equipment and only needs the optical microscopic image of microalgae to carry out the identification statistics. The average recognition rate of the algorithm is 97.23% for Haematococcus pluvialis images in various environments and growing states, which has certain application value. © 2021 IEEE.

关键词： Microalgae

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A Framework for Video Based Sign Language Interpretation Using Machine Learning and statistical methods

A Framework for Video Based Sign Language Interpretation Usi...

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Swathi D Susmetha K Sreeji. S S. Mangairkarasi B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The Sign Language Recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. The project consists of several key components and steps: Video processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time.

关键词： Hands Deep learning Sign language Translation Tracking Text recognition Speech recognition Streaming media Real-time systems Graphical user interfaces

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An improved simple feature set for face presentation attack detection 30

An improved simple feature set for face presentation attack ...

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30th International conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2022

作者： Denisova, Anna Samara National Research University Moskovskoye shosse 34 Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" Russian Academy of Sciences Molodogvardeiskaya st. 151 Samara443001 Russia

ISBN: (纸本)9788086943336

Presentation attacks are weak points of facial biometrical authentication systems. Although several presentation attack detection methods were developed, the best of them require a sufficient amount of training data and rely on computationally intensive deep learning based features. Thus, most of them have difficulties with adaptation to new types of presentation attacks or new cameras. In this paper, we introduce a method for face presentation attack detection with low requirements for training data and high efficiency for a wide range of spoofing attacks. The method includes feature extraction and binary classification stages. We use a combination of simple statistical and texture features and describe the experimental results of feature adjustment and selection. We validate the proposed method using WMCA dataset. The experiments showed that the proposed features decrease the average classification error in comparison with the RDWT-Haralick-SVM method and demonstrate the best performance among non-CNN-based methods. © 2022 University of West Bohemia. All rights reserved.

关键词： Feature extraction

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Learning the image Prior by Unrolling an Optimization Method

Learning the Image Prior by Unrolling an Optimization Method

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European Signal processing conference (EUSIPCO)

作者： Silvia Bonettini Giorgia Franchini Danilo Pezzi Marco Prato Department of Informatics Physics and Mathematics University of Modena and Reggio Emilia Modena Italy

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Nowadays neural networks are omnipresent thanks to the amazing adaptability they possess, despite their poor interpretability and the difficulties they give when manipulating the parameters. On the other side, we have the classical variational approach, where the restoration is obtained as the solution of a given optimization problem. The bilevel approach is connected to both approaches and consists first in devising a parametric formulation of the variational problem, then in optimizing these parameters with respect to a given dataset of training data. In this work we analyze the classical bilevel approach in combination with unrolling techniques, where the parameters of the variational problem are trained with respect to the results obtained after a fixed number of iterations of an optimization method applied to it. This results in a large scale optimization problem which can be solved by means of stochastic methods; as we observed in our numerical experiments, the stochastic approach can produce medium accuracy results in very few epochs. Moreover, our experiments also show that the unrolling approach leads to results which are comparable with those of the original bilevel method in terms of accuracy.

关键词： Neural networks Optimization methods stochastic processes Training data Europe Signal processing Minimization

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Single MR image Super-Resolution via Mixed Self-Similarity Attention Network

Single MR Image Super-Resolution via Mixed Self-Similarity A...

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conference on Medical Imaging - image processing

作者： Hu, Xiaowan Wang, Haoqian Luo, Yi Sun, Zhongzhi Peng, Yanbin Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Peking Univ Shenzhen Hosp Shenzhen Peoples R China

ISBN: (纸本)9781510640221

The single-image super-resolution (SISR) network based on deep learning is dedicated to learning the mapping between low-resolution (LR) images and high-resolution (HR) images. The optimal parameters of these networks often require extensive training on large-scale external image databases. For medical magnetic resonance (MR) images, there is a lack of large data sets containing high-quality images. Some deep networks that perform well on natural images cannot be fully trained on MR images, which limits the super-resolution (SR) performance. In traditional methods, the non-local self-similarity has been verified as useful statistical prior information for image restoration. The inherent feature correlation not only exists between pixels, but some patches also tend to be repeated at different positions within and across scales of MR images. Therefore, in this paper, we propose a mixed self-similarity attention network (MSAN) to explore the long-range dependencies of different regions fully. In the feature map of the entire input MR image, the prior information of self-similarity is divided into two scales: point-similarity and patch-similarity. We use points and patches that are highly similar to the current area to restore a more detailed structural texture. The internal correlation items can be used as an essential supplement to the limited external training dataset. Besides, the large number of less informative background in MR images will interfere with practical self-similarity information. A dual attention mechanism combining first-order attention and second-order attention gives more weight to salient features and suppresses the activation of useless features. Comprehensive experiments demonstrate that the proposed achieves significantly superior results on MR images SR while outperforming state-of-the-art methods by a large margin quantitatively and visually.

关键词： Magnetic Resonance image Super-Resolution Self-Similarity statistical Prior

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Deep learning methods for image decomposition of cervical cells 28

Deep learning methods for image decomposition of cervical ce...

引用

28th European Signal processing conference, EUSIPCO 2020

作者： Mahyari, Tayebeh Lotfi Dansereau, Richard M. Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9789082797053

One way to solve under-determined image decomposition is to use statistical information about the type of data to be decomposed. This information can be obtained by a deep learning where convolutional neural networks (CNN) are a subset recently used widely in image processing. In this paper, we have designed a two-stage CNN that takes cytology images of overlapped cervical cells and attempts to separate the cell images. In the first stage, we designed a CNN to segment overlapping cells. In the second stage, we designed a CNN that uses this segmentation and the original image to separate the regions. We implemented a CNN similar to U-Net for image segmentation and implemented a new network for the image separation. To train and test the proposed networks, we simulated 50000 cervical cell cytology images by overlaying individual images of real cervical cells using the Beer-Lambert law. Of these 50000 images, we used 49000 images for training and evaluated the method with 1000 test images. Results on these synthetic images give more than 97% segmentation accuracy and gives decomposition SSIM scores of more than 0.99 and PSNR score of more than 30 dB. Despite these positive results, the permutation problem that commonly effects signal separation occasionally occurred resulting in some cell structure mis-separation (for example, one cell given two nucleoli and the other given none). In addition, when the segmentation was poor from the first stage, the resulting separation was poor. © 2021 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： image segmentation

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statistical learning and inverse problems: a stochastic gradient approach 22

Statistical learning and inverse problems: a stochastic grad...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Yuri R. Fonseca Yuri F. Saporito Decision Risk and Operations Columbia University New York NY School of Applied Mathematics Getulio Vargas Foundation Rio de Janeiro Brazil

ISBN: (纸本)9781713871088

Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of statistical Inverse Problem (SIP) and demonstrate how stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk. We also propose a modification for the SGD algorithm where we leverage machine learning methods to smooth the stochastic gradients and improve empirical performance. We exemplify the algorithm in a setting of great interest nowadays: the Functional Linear Regression model. In this case we consider a synthetic data example and a classification problem for predicting the main activity of bitcoin addresses based on their balances.

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Multi-focus Fusion Using image Matting and Geometric Mean of DCT-Variance 1

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5th International conference on Computer Vision and image processing, CVIP 2020

作者： Roy, Manali Mukhopadhyay, Susanta Dhanbad India

ISBN: (数字)9789811610868

ISBN: (纸本)9789811610851

Optical lenses installed in most imaging devices suffer from the limited depth of field due to which objects get imaged with varying sharpness and details, thereby losing essential information. To cope with the problem, an effective multi-focus fusion technique is proposed in this paper based on a focus measure obtained from the statistical properties of an image and its DCT-bandpass filtered versions. The focus information obtained is eventually converted into a decision trimap using fundamental image processing operations. This trimap largely discriminates between the focussed and defocussed pixels and expedites the fusion process by acting as an input to a robust image matting framework to obtain a final decision map. Experimental results illustrate the superiority of the proposed approach over other competing multi-focus methods in terms of visual quality and fusion metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image fusion

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ICP algorithm based on stochastic approach 44

ICP algorithm based on stochastic approach

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conference Applications of Digital image processing XLIV

作者： Voronin, Sergei Makovetskii, Artyom Kober, Vitaly Voronin, Aleksei Chelyabinsk State Univ Dept Math 129 Br Kashirinykh Chelyabinsk 454001 Russia CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

3D reconstruction has been widely applied in medical images, industrial inspection, self-driving cars, and indoor modeling. The 3D model is built by the steps of data collection, point cloud registration, surface reconstruction, and texture mapping. In the process of data collection, due to the limited visibility of the scanning system, the scanner needs to scan multiple angles and then splice the data to obtain a complete point cloud model. The point clouds from different angles must be merged into a unified coordinate system, which is known as point cloud registration. The result of point cloud registration can directly affect the accuracy of the point cloud model;thus, point cloud registration is a key step in the construction of the point cloud model. The ICP (Iterative Closest Points) algorithm is the most known technique of the point cloud registration. The variational ICP problem can be solved not only by deterministic but also by stochastic methods. One of them is Grey Wolf Optimizer (GWO) algorithm. Recently, GWO has been applied to rough point clouds alignment. In the proposed paper, we apply the GWO approach to the realization of the point-to-point ICP algorithms. Computer simulation results are presented to illustrate the performance of the proposed algorithm.

关键词： surface reconstruction point cloud registration iterative closest points (ICP) variational functionals orthogonal transformations grey wolf optimization algorithm

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