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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是101-110 订阅

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Development of low-cost real-time driver drowsiness detection system using eye centre tracking and dynamic thresholding 18th

Development of low-cost real-time driver drowsiness detectio...

引用

Joint conferences on 18th International conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Khan, Fuzail Sharma, Sandeep Department of Electronics and Communication Engineering National Institute of Technology Karnataka Surathkal575025 India

ISBN: (纸本)9783030166564

One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the concentration and vigilance of the driver thereby increasing the risk of inherent human error leading to injuries and fatalities. Hence, our primary motive being - to reduce road accidents using a non-intrusive image processing based alert system. In this regard, we have built a system that detects driver drowsiness by real time tracking and monitoring the pattern of the driver’s eyes. the stand alone system consists of 3 interconnected components - a processor, a camera and an alarm. After initial facial detection, the eyes are located, extracted and continuously monitored to check whether they are open or closed on the basis of a pixel-by-pixel method. When the eyes are seen to be closed for a certain amount of time, drowsiness is said to be detected and an alarm is issued accordingly to alert the driver and hence, prevent a casualty. © Springer Nature Switzerland AG 2020.

关键词： Real time systems

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Grape Downy Mildew On-line Detection Based on Accurate image processing Method Preformed on Embedded Ambient Intelligence System 4th

Grape Downy Mildew On-line Detection Based on Accurate Image...

引用

4th International conference on Intelligent, Interactive systems and Applications, IISA2019

作者： Xu, Peifeng Jiang, Qiyou Zhao, Zhongying Yang, Ning Zhang, Rongbiao School of Electrical and Information Engineering Jiangsu University Zhenjiang212013 China Jiangsu Vocational College of Agriculture and Forestry Jurong212400 China

ISBN: (纸本)9783030343866

In this paper, an accurate and intelligent Grape Downy Mildew (GDM) detection method based on common image processing and artificial neural network (ANNs) is proposed. In view of the structure of grape leaves and the distribution characteristics of GDM on leaves, firstly, image processing is mainly to solve the extraction of downy mildew areas on leaves, which is mainly composed of gray-scale processing, gray-scale linear transformation and binarization, and the effects of different processing algorithms and parameters on the results are discussed. Secondly, an ANNs is used to reduce the interference of grape leaf vein on the spot area statistics. Finally, the hardware and software platform based on ARM9 integrated processor, Linux and QT is used to realize the system integration. Compared with the traditional detection method, the accuracy of this detection method can reach 97%, which is close to the accuracy of the human eye, which can be used as an ambient intelligence system in the vineyard inspection, automatic completion of grape GDM detection and grading. © 2020, Springer Nature Switzerland AG.

关键词： image processing

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Differential method of multidimensional signals compression based on the adapted parameterized interpolation algorithm 6

Differential method of multidimensional signals compression ...

引用

6th International conference Information Technology and Nanotechnology. Session image processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Maksimov, Aleksey Gashnikov, Mikhail Samara National Research University Samara Russia Samara National Research University Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Samara Russia

In this paper, parameterized algorithms of multidimensional signal interpolation are adapted for use as part of differential compression methods. these methods are based on the efficient coding of quantized differences between the initial and interpolated signal samples during sequential signal scanning. the proposed interpolators are based on the classification of signal samples and the use of various interpolation formulas within the classes. the sample classifier and its training procedure and a set of interpolating functions for the compression method are described. the results of experimental research on real multidimensional signals confirm that the use of an adapted parameterized interpolator leads to an increase in the efficiency of the differential compression method. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： image enhancement

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image-Based World-perceiving Knowledge Graph (WpKG) with Imprecision 18th

Image-Based World-perceiving Knowledge Graph (WpKG) with Imp...

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18th International conference on Information processing and Management of Uncertainty in Knowledge-Based systems, IPMU 2020

作者： Rezaei, Navid Reformat, Marek Z. Yager, Ronald R. University of Alberta EdmontonT6G 1H9 Canada University of Social Sciences Lódź90-113 Poland Iona College New RochelleNY10801 United States

ISBN: (纸本)9783030501457

Knowledge graphs are a data format that enables the representation of semantics. Most of the available graphs focus on the representation of facts, their features, and relations between them. However, from the point of view of possible applications of semantically rich data formats in intelligent, real-world scenarios, there is a need for knowledge graphs that describe contextual information regarding realistic and casual relations between items in the real world. In this paper, we present a methodology of generating knowledge graphs addressing such a need. We call them World-perceiving Knowledge Graphs – WpKG. the process of their construction is based on analyzing images. We apply deep learning image processing methods to extract scene graphs. We combine these graphs, and process the obtained graph to determine importance of relations between items detected on the images. the generated WpKG is used as a basis for constructing possibility graphs. We illustrate the process and show some snippets of the generated knowledge and possibility graphs. © 2020, Springer Nature Switzerland AG.

关键词： Knowledge graph

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EczemaNet: A Deep CNN-based Eczema Diseases Classification

EczemaNet: A Deep CNN-based Eczema Diseases Classification

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International image processing, Applications and systems conference (ipas)

作者： Masum Shah Junayed Abu Noman Md Sakib Nipa Anjum Md Baharul Islam Afsana Ahsan Jeny Daffodil International University Dhaka Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh American University of Malta Bormla BML Malta

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as image processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. there is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.

关键词： image segmentation Machine learning algorithms conferences Transforms Medical services Skin Diseases

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CRIST900: A fully-labeled natural image dataset for multi-operator content aware image retargeting 18th

CRIST900: A fully-labeled natural image dataset for multi-op...

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Joint conferences on 18th International conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Abhayadev, M. Santha, T. Department of Computer Science Dr G R Damodaran College of Science CoimbatoreTamilnadu641014 India

ISBN: (纸本)9783030166564

A fully-labeled image dataset provides an exclusive resource for reproducible analysis, investigation inquiries and data analyses in different research computational fields like machine learning, computer vision and deep learning machine intelligence. this research paper present a large scale fully-labeled natural image dataset for Multi-Operator content aware image retargeting techniques. the image dataset is feely available for image processing research field. the current research natural image dataset entitled CRIST900, it include 900 natural images and uses for content aware image retargeting. the proposed CRIST is an image retargeting Multi-Operator method called Content Retargeting image reSizing Technique. the proposed image resizing method has three phases, image object boundary identification, the image objects feature importance visual saliency map generation and Multi-Operator techniques for efficient retargeting of image objects. the Multi-Operator retargeted image quality assessment was done by different subjective and objective image quality assessment matrices. the experimental result shows that the proposed approach can attain better result than the traditional methods and content aware state-of-the-art image retargeting techniques. the research dataset is publicly available at https://***/view/abhayadevmalayil/home. © Springer Nature Switzerland AG 2020.

关键词： image quality

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Pedestrian Detection and Classification for Autonomous Train

Pedestrian Detection and Classification for Autonomous Train

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International image processing, Applications and systems conference (ipas)

作者： Ankur Mahtani Wael Ben-Messaoud Abdelmalik Taleb-Ahmed Smail Niar Clément Strauss FCS Railenium Famars France IEMN Valenciennes France

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) the creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.

关键词： Support vector machines Location awareness Embedded systems Rail transportation Real-time systems Classification algorithms Monitoring

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Coconut Disease Prediction System Using image processing and Deep Learning Techniques

Coconut Disease Prediction System Using Image Processing and...

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International image processing, Applications and systems conference (ipas)

作者： Dhapitha Nesarajan Lokini Kunalan Mithun Logeswaran Sanvitha Kasthuriarachchi Dilani Lungalage Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Coconut production is the most important and one of the main sources of income in the Sri Lankan economy. the recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. this paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. the outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector.

关键词： Support vector machines image processing Production Prediction algorithms Mobile applications Monitoring Diseases

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Automotive Interior Sensing - Towards a Synergetic Approach between Anomaly Detection and Action Recognition Strategies

Automotive Interior Sensing - Towards a Synergetic Approach ...

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International image processing, Applications and systems conference (ipas)

作者： Pedro Augusto Jaime S. Cardoso Joaquim Fonseca Faculty of Engineering of the University of Porto INESCTEC and University of Porto Bosch Car Multimedia

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

With the appearance of Shared Autonomous Vehicles there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviors, more specifically, violence between passengers. Traditional action recognition algorithms build models around known interactions but activities can be so diverse, that having a dataset that incorporates most use cases is unattainable. While action recognition models are normally trained on all the defined activities and directly output a score that classifies the likelihood of violence, video anomaly detection algorithms present themselves as an alternative approach to build a good discriminative model since usually only non-violent examples are needed. this work focuses on anomaly detection and action recognition algorithms trained, validated and tested on a subset of human behavior video sequences from Bosch's internal datasets. the anomaly detection network architecture defines how to properly reconstruct normal frame sequences so that during testing, each sequence can be classified as normal or abnormal based on its reconstruction error. With these errors, regularity scores are inferred showing the predicted regularity of each frame. the resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/violent behaviors and aid in understanding the meaning of such human interactions.

关键词： Deep learning Classification algorithms Automobiles Anomaly detection image reconstruction Vehicles Testing

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Bayesian image Analysis in Fourier Space Using Data-Driven Priors (DD-BIFS) 18th

Bayesian Image Analysis in Fourier Space Using Data-Driven P...

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18th International conference on Information processing and Management of Uncertainty in Knowledge-Based systems, IPMU 2020

作者： Kornak, John Boylan, Ross Young, Karl Wolf, Amy Cobigo, Yann Rosen, Howard University of California San FranciscoCA94143 United States

ISBN: (纸本)9783030501525

Statistical image analysis is an extensive field that includes problems such as noise-reduction, de-blurring, feature enhancement, and object detection/identification, to name a few. Bayesian image analysis can improve image quality, by balancing a priori expectations of image characteristics, with a model for the noise process via Bayes theorem. We have previously given a reformulation of the conventional Bayesian image analysis paradigm in Fourier space, i.e. the prior distribution (the prior) and likelihood are given in terms of spatial frequency signals. By specifying the Bayesian model in Fourier space, spatially correlated priors, that are relatively difficult to model and compute in conventional image space, can be efficiently modeled as a set of independent processes across Fourier space. the originally inter-correlated and high-dimensional problem in image space is thereby broken down into a series of (trivially parallelizable) independent one-dimensional problems. In this paper we adapt this Fourier space process into a data-driven framework in which the Fourier space priors are built empirically from a database of images and then used to enhance future images. We will describe the data-driven Bayesian image analysis in Fourier space (DD-BIFS) modeling approach, illustrate it’s computational efficiency and speed. Finally, we give specific applications of DD-BIFS to improve the quality of arterial-spin-labeling (ASL) perfusion images via a database of human brain positron emission tomography (PET) images. © 2020, Springer Nature Switzerland AG.

关键词： Quality control

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