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Laboratory Approbation of the Algorithm for Isolating People's Faces on a Thermal Infrared image in the Case of Their Quasi-Stationary Arrangement in a Room 8

Laboratory Approbation of the Algorithm for Isolating People...

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8th Annual International Conference of the Biologically-Inspired-Cognitive Architectures-Society on Biologically Inspired Cognitive Architectures (BICA)

作者： Alyushin, M., v Alyushin, A. M. Kolobashkina, L., v Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Moscow Russia

Increasing the effectiveness of training and training sessions is possible through the implementation of so-called biological feedback. Such feedback allows the teacher, or the instructor, to continuously monitor the current psycho-emotional and functional state of the students. As a result, it becomes possible to adapt the style, pace, training mode and the volume of the material outlined, depending on the current receptivity and fatigue level of the listeners. The main element of systems that implement biological feedback in practice are remote non-contact technologies. Such technologies allow in a fully automatic mode to register the main most informative human bio-parameters. Among them, in the first place are the parameters characterizing the current state of the cardiovascular system of man, his breathing system, as well as his peripheral nervous system. The bulk of information is obtained by processing in real time the thermal infrared image of a person's face. Unfortunately, existing algorithms for distinguishing a person's face have a sufficiently high computational complexity and insufficient reliability. A typical example in this regard can be a family of algorithms based on the viola-Jones approach. The approach proposed in the work is based on taking into account additional information about the most likely location of a person's face on a thermal image. This approach is most appropriate to use in cases of quasi-stationary location of people in the room. A typical example is the location of students at the tables in the classroom. For such cases it is possible to determine the areas of the most probable location of the trainees' faces, as well as the possible boundaries of their movement. Laboratory tests of the developed program on the basis of the proposed algorithm have confirmed its high productivity, as well as efficiency in identifying students faces in the classroom. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article

关键词： Thermal image of the face Face Selection Algorithm Quasi-Stationary Arrangement of People

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3d synthetic aperture radar image 4

3d synthetic aperture radar image

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4th International Conference on Information Technology and Nanotechnology - Session: image processing and Earth Remote Sensing, IPERS-ITNT 2018

作者： Leukhin, A.N. Rozentsov, A.A. Bezrodnyy, v.I. voronin, A.A. Karasev, D.Yu. Kokovihina, N.A. Mari State University Lenin square 1 Yoskar-Ola424000 Russia Volga State University of Technology Lenin square 3 Yoskar-Ola424000 Russia

Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. The algorithms for the formation of 3D radar images in multiposition interferometric systems for remote sensing of the Earth are considered. Examples of reconstruction of the relief map for systems with one and two transmit antenna are presented. © 2018 CEUR-WS. All rights reserved.

关键词： Remote sensing

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Shape and Relief Evaluation Using the Light Field Camera

Shape and Relief Evaluation Using the Light Field Camera

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International Conference on Innovations and Prospects of Development of Mining Machinery and Electrical Engineering, IPDME 2018

作者： Makhov, v.E. Sytko, I.I. Mozhaisky Military Space Academy Zhdanov Street 13 St. Petersburg197082 Russia Saint Petersburg Mining University 21 Line St. Petersburg199106 Russia

The paper considers an optical system for controlling the shape and micro-relief of products using a single-camera optoelectronic light field recorder. Based on National Instruments computer technologies, image processing algorithms and basic surface measurements have been developed. To accurately determine macro- and micro-relief parameters the authors propose algorithms for morphological analysis of two images obtained from a multi-focus light field file at a given depth of field. It is shown that the method of capturing the light field makes it possible to obtain the accuracy of surface relief by the height of its profile compared to other methods, while significantly reducing control requirements. © Published under licence by IOP Publishing Ltd.

关键词： Optical systems

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A Document Skew Detection Method Using Fast Hough Transform

arXiv

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arXiv 2019年

作者： Bezmaternykh, P.v. Nikolaev, D.P. Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia Institute for Information Transmission Problems of Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow Russia

The majority of document image analysis systems use a document skew detection algorithm to simplify all its further processing stages. A huge amount of such algorithms based on Hough transform (HT) analysis has already been proposed. Despite this, we managed to find only one work where the Fast Hough Transform (FHT) usage was suggested to solve the indicated problem. Unfortunately, no study of that method was provided. In this work, we propose and study a skew detection algorithm for the document images which relies on FHT analysis. To measure this algorithm quality we use the dataset from the problem oriented DISEC‘13 contest and its evaluation methodology. Obtained values for AED, TOP80, and CE criteria are equal to 0.086, 0.056, 68.80 respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Hough transforms

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Effects of annotation granularity in deep learning models for histopathological images

Effects of annotation granularity in deep learning models fo...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Jiangbo Shi Zeyu Gao Haichuan Zhang Pargorn Puttapirat Chunbao Wang Xiangrong Zhang Chen Li National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781728118680

Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. Generally, annotations made in pathology-related datasets have inherited annotation methods from natural scene images. This work investigates different granularity of annotations in histopathological data set including image-wise, bounding box, ellipse-wise, and pixel-wise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows not only that finer-grained annotation can improve the performance of deep learning models, but also help they extract more accurate phenotypic information from histopathological slides. The accurate and spatially precise acquisitions of phenotypic information can improve the reliability of the model prediction. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions a

关键词： histopathological image annotation granularity deep learning classification semantic segmentation ANNOTATIONS Machine Learning Taxonomy Slide Pathologists Slide Dataset slump

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An Error-Based Block Adjustment Method for Multi-Angle Satellite imagery Without Ground Control Points

An Error-Based Block Adjustment Method for Multi-Angle Satel...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Niangang Jiao Feng wang Hongjian You Kun Hu Mudan Yang Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Beijing China

Geo-positioning accuracy improvement is one of the most important step of remote sensing image preprocessing. Traditional methods require a large number of ground control points (GCPs) which consuming lots of manpower and financial resources. With the resolution up to 0.8m, the original geo-positioning accuracy of the Chinese Gaofen (GF-2) multi-angle imagery is about 90m which means a limited application in geometric processing. In this paper, we propose a new method to improve the geometric performance of the multi-angle satellite imagery based on the geometric error sources of this experimental dataset without GCPs. Under the condition of weak intersection of our test dataset, we use a DEM-assisted approach to acquire a more accurate initial position accuracy of all tie points, and all extracted data is clustered by the Density based spatial clustering of applications with noise (DBSCAN) algorithm in order to eliminate points or impages with large positioning error automatically. Then, the error-based block adjustment model are proposed and investigated to improved the geometric performance of the experimental dataset. Based on our proposed method, 142 multi-angle GF-2 satellite images covering the western Beijing area are experimented and the root mean square error (RMSE) of the geometric accuracy is improved up to about 12m in plane and 6m in height, which shows a significantly improvement in geo-positioning accuracy of these multi-angle GF-2 remote sensing imagery.

关键词： Satellites image resolution Mathematical model Unified modeling language Remote sensing Clustering algorithms Cameras

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Compressive beamforming and directional sound reconstruction using the Kronecker array transform

Compressive beamforming and directional sound reconstruction...

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Asilomar Conference on Signals, systems & Computers

作者： Marcelo A. Daher Carlos A. Prete vítor H. Nascimento Bruno S. Masiero Dept. of Electronic Systems Engineering University of São Paulo São Paulo Brazil Dept. of Communications University of Campinas Campinas Brazil

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

The Kronecker array transform (KAT) was introduced to reduce the computational burden in acoustic image estimation and other 2-dimensional array processing applications. The KAT can be applied only when the planar microphone array is separable. In this paper we study the performance of separable arrays using compressive beamforming algorithms to estimate the direction of arrival (DOA) of far-field sources and to recover the signal arriving from a particular direction. Our analysis extends previous studies based on mutual coherence to separable arrays, and shows that non-redundant separable arrays have better mutual coherence than can be obtained using 2D random arrays. We also derive the space discretization that yields the minimum coherence, and study the influence of frequency and the spatial resolution on the coherence. In order to verify the results, we present the performance of a separable array in two problems: DOA estimation and signal recovery using sparse reconstruction, and compare its performance with classical beamforming techniques. The sparsity-based approach shows better performance in DOA estimation and great improvement in time-domain signal recovery.

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Real-time Depth Map Processor for Offset Aperture based Single Camera System 18

Real-time Depth Map Processor for Offset Aperture based Sing...

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23rd Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Kim, Hyeji Lim, Jinyeon Lee, Yeongmin Yun, Woojin Kim, Young-Gyu Choi, Wonseok Khan, Asim Khan, Muhammad Umar Karim Homidov, Said Park, Hyun Sang Kyung, Chong-Min Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Ctr Integrated Smart Sensors Daejeon South Korea Kongju Natl Univ Div Elect Engn Cheonan South Korea

ISBN: (纸本)9781509006021

This paper presents a Offset Aperture (OA) based single camera system and proposes a optimized vision processor, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. The proposed design was fabricated in 110nm CMOS image sensor technology and supports 32-level depth resolution on 1920 x 1080 full HD image with 30fps, consuming 280.53mW from 1.5v supply and a mere 2.8% of bad classification. The low-complexity algorithms are employed to eliminate the DRAM access, thereby the proposed OA architecture can be directly embedded with the CMOS image sensor and commercial image processing chip.

关键词： Cameras Apertures Hardware High definition video Real-time systems Computer architecture Power demand

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LABA: Logical Layout Analysis of Book Page images in Arabic Using Multiple Support vector Machines 2

LABA: Logical Layout Analysis of Book Page Images in Arabic ...

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2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition (IEEE ASAR)

作者： Qin, Wenda Elanwar, Randa Betke, Margrit Boston Univ Dept Comp Sci 111 Cummington St Boston MA 02215 USA Elect Res Inst Cairo Egypt Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538614594

Logical layout analysis, which determines the function of a document region, for example, whether it is a title, paragraph, or caption, is an indispensable part in a document understanding system. Rule-based algorithms have long been used for such systems. The datasets available have been small, and so the generalization of the performance of these systems is difficult to assess. In this paper, we present LABA, a supervised machine learning system based on multiple support vector machines for conducting a logical Layout Analysis of scanned pages of Books in Arabic. Our system labels the function (class) of a document(scanned book pages) region, based on its position on the page and other features. We evaluated LABA with the benchmark "BCE-Arabic-v1" dataset, which contains scanned pages of illustrated Arabic books. We obtained high recall and precision values, and found that the F-measure of LABA is higher for all classes except the "noise" class compared to a neural network method that was based on prior work.

关键词： Document image processing text analysis multi-classifier system Arabic text documents logical layout analysis functional layout analysis

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Deep learning in ophthalmology: The technical and clinical considerations

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PROGRESS IN RETINAL AND EYE RESEARCH 2019年 72卷 100759-000页

作者： Ting, Daniel S. W. Peng, Lily varadarajan, Avinash v. Keane, Pearse A. Burlina, Philippe M. Chiang, Michael F. Schmetterer, Leopold Pasquale, Louis R. Bressler, Neil M. Webster, Dale R. Abramoff, Michael Wong, Tien Y. Natl Univ Singapore Duke NUS Med Sch Singapore Natl Eye Ctr Singapore Eye Res Inst Singapore Singapore Google AI Healthcare Mountain View CA USA Moorfields Eye Hosp London England Johns Hopkins Univ Sch Med Wilmer Eye Inst Baltimore MD 21205 USA Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Malone Ctr Engn Healthcare Baltimore MD USA Oregon Hlth & Sci Univ Casey Eye Inst Dept Ophthalmol & Med Informat Portland OR 97201 USA Oregon Hlth & Sci Univ Casey Eye Inst Dept Med Informat & Clin Epidemiol Portland OR 97201 USA Nanyang Technol Univ Lee Kong Chian Sch Med Dept Ophthalmol Singapore Singapore Med Univ Vienna Dept Clin Pharmacol Vienna Austria Med Univ Vienna Ctr Med Phys & Biomed Engn Vienna Austria Icahn Sch Med Mt Sinai Dept Ophthalmol New York NY 10029 USA Univ Iowa Hlth Care Dept Ophthalmol & Visual Sci Iowa City IA USA

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the intemet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screeni

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