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4th International Scientific Conference on Arctic: History and Modernity

作者： Milyaev, N. Kaznin, A. Sushko, O. Skotarenko, O. Federal University Named after M.V. Lomonosov Murmansk Arctic State University Murmansk Russia

The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image analysis and pattern recognition is enhanced by image preprocessing. However, the existing binarization algorithms are not universal for images with different distortions and loss of information. The accuracy of binarization algorithms depends on many factors, such as shadows, uneven lighting, low contrast, noise, etc. images with different characteristics of light and noise are simulated in order to model various lighting conditions on information from digital cameras of robotic systems. The paper investigates global and adaptive image binarization algorithms. The binarized images were obtained using these algorithms and the results of binarized images recognition are compared by an optical character recognition system. An analysis of the comparison results showed that for images made in poor lighting conditions or images with low contrast, or images with high noise levels, adaptive binarization algorithms are better suited. However, in most cases it is not possible to obtain fully correctly recognized *** paper proposes a new binarization method based on artificial neural networks. The process of creating an artificial neural network is shown, include the parameters for determining the class of a pixel, the adjustment of weights, the architecture of an artificial neural network. A comparison of the proposed artificial neural network with existing image binarization algorithms demonstrate that in most cases the artificial neural network has the result of image processing at the level of adaptive algorithms or higher. The proposed method of images binarization based on the image color characteristics analysis allows to solve image recognition tasks by robotized systems. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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IMACS: A Framework for Performance Evaluation of image Approximation in a Closed-loop System 8

IMACS: A Framework for Performance Evaluation of Image Appro...

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8th Mediterranean Conference on Embedded Computing (MECO)

作者： Mohamed, Sajid De, Sayandip Bimpisidis, Konstantinos Nathan, vishak Goswami, Dip Corporaal, Henk Basten, Twan Eindhoven Univ Technol Elect Syst Grp Eindhoven Netherlands TNO ESI Eindhoven Netherlands

ISBN: (纸本)9781728117393

image processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in v-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, v-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NvIDIA Drive PX2 platform and communicates to v-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example.

关键词： Task analysis IP networks Cameras Pipelines Computational modeling Graphics processing units"

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Multiple objects detection and tracking in passive scanning millimeter-wave imaging systems 12

Multiple objects detection and tracking in passive scanning ...

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Conference on Millimetre Wave and Terahertz Sensors and Technology XII

作者： Klochko, vladimir K. Strotov, valery v. Smirnov, Sergey A. Ryazan State Radio Engn Univ 59-1 Gagarin St Ryazan 390005 Russia

ISBN: (数字)9781510630321

ISBN: (纸本)9781510630321

This paper describes algorithms for detection and evaluation of trajectory parameters of small, spatially moving objects by passive optical and radio thermal vision system. The algorithms are based on spatial and temporal image processing. During spatial processing, a system of equations representing a sufficient condition for the conjugation of direction vectors to objects in stereo pairs is solved. During temporal processing vectors of the directions on accessory to objects in a sequence of the periods of supervision are distributed. The results of the algorithms theoretical and experimental examination are given. They are showing the advantage of the joint application of the two approaches.

关键词： passive scanning vision system moving objects detection trajectory parameters estimation tracking

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Skin cancer classification computer system development with deep learning 10

Skin cancer classification computer system development with ...

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Workshop on Information Technology and Scientific Computing in the Framework of the 10th International Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech systems, ITTMM 2020

作者： Demidova, Anastasia v. Kulyabov, Dmitry S. Shchetinin, Eugeny Yu. Department of Applied Probability and Informatics Peoples' Friendship University of Russia 6 Miklukho-Maklaya St. Moscow117198 Russia Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie St. Dubna Moscow region141980 Russia Department of Data Analysis Decision Making and Financial Technologies Financial University under the Government of the Russian Federation 49 Leningradsky pr. Moscow111234 Russia

Melanoma is a deadly form of skin cancer that is often undiagnosed or misdiagnosed as a benign skin lesion. Its early detection is extremely important, since the life of patients with melanoma depends on accurate and early diagnosis of the disease. However, doctors often rely on personal experience and assess each patient's injuries based on a personal examination. Clinical studies allow us to get the accuracy of the diagnosis of melatoma from 65 to 80 percents, which was a good result for some time. However, modern research claims that the use of dermoscopic images in diagnosis significantly increases the accuracy of diagnosis of skin lesions. The visual differences between melanoma and benign skin lesions can be very small, making diagnosis difficult even for an expert doctor. Recent advances in the use of artificial intelligence methods in the analysis of medical images have made it possible to consider the development of intelligent medical diagnostic systems based on visualization as a very promising direction that will help the doctor in making more effective decisions about the health of patients and making a diagnosis at an early stage and in adverse conditions. In this paper, we propose an approach to solving the problem of classification of skin diseases, namely, melanoma at an early stage, based on deep learning. In particular, a solution to the problem of classification of a dermoscopic image containing either malignant or benign skin lesions is proposed. For this purpose, the deep neural network architecture was developed and applied to image processing. Computer experiments on the ISIC data set have shown that the proposed approach provides 92% accuracy on the test sample, which is significantly higher than other algorithms in this data set have shown. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： image classification

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DSL-Based Acceleration of Automotive Environment Perception and Mapping algorithms for Embedded CPUs, GPUs, and FPGAs 32nd

DSL-Based Acceleration of Automotive Environment Perception ...

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32nd International Conference on Computer Architecture (ARCS)

作者： Fickenscher, Joerg Hannig, Frank Teich, Juergen Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Comp Sci Hardware Software Codesign Erlangen Germany

ISBN: (纸本)9783030186562;9783030186555

The availability and sophistication of Advanced Driver Assistance System (ADASs) are becoming increasingly important for customers when making purchasing decisions and thus also for the manufacturers of such systems. The increased demands on functionality have also increased the demands in computing power and today's standard processors in automotive Electronic Control Unit (ECUs) struggle to provide enough computing power for those tasks. Here, heterogeneous systems, for example consisting of Central processing Unit (CPUs), embedded Graphics processing Unit (GPUs), and Field-Programmable Gate Array (FPGAs) provide a remedy. These heterogeneous systems, however, increase the development effort and the development costs enormously. In this paper, we analyze the extent to which it is possible to automatically generate code with the help of a Domain-Specific Language (DSL) for typical algorithms in the field of environment perception and environment mapping. We show that with the Heterogeneous image processing Acceleration (Hipacc) framework it is possible to generate program code for CPUs, GPUs, and FPGAs. After that, we compare for selected algorithms the execution times of the automatically generated code with hand-written variants from the literature.

关键词： Advanced Driver Assistance systems Domain-specific languages Code generation

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image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust

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JOURNAL OF INFRASTRUCTURE systems 2020年第2期26卷

作者： Albatayneh, Omar Forslof, Lars Ksaibati, Khaled Univ Wyoming Dept Civil & Architectural Engn 1000 E Univ Ave Laramie WY 82070 USA Egnahemsgatan 5 S-82735 Ljusdal Sweden Univ Wyoming Civil Engn 1000 E Univ AveDept 3295 Laramie WY 82071 USA Univ Wyoming Dept Civil & Architectural Engn Wyoming Technol Transfer Ctr 1000 E Univ AveDept 3295 Laramie WY 82071 USA

In gravel roads management systems (GRMS), the need for a holistic approach for detecting the dust amounts on gravel roads has enabled the development of a solution that works based on one of the subdisciplines of artificial intelligence (AI). Recently, machine learning is one of the most widely used algorithms to train data to optimize systems. The advances in machine learning has enabled us to develop a complex application. This paper demonstrates the ability of using one of the most popular machine learning frameworks TensorFlow to build an image classifier. This classifier has the ability to classify the dust amounts on gravel roads into four major levels (None, Low, Medium, and High). This classifier is based on the aspect of optimizing one of the deep neural networks models Inception-v3 model. This model contains a pretrained package used to extract and recognize dust patterns from dust images automatically. In this paper, a data set of 4,000 images of gravel roads were collected. For training, 80% of the data set was used, and 20% was used for testing. Furthermore, a prediction accuracy plot was generated, and it was found that this classifier achieves a prediction accuracy of 72%.

关键词： Machine learning Deep learning Convolutional neural network Inception-v3 image processing TensorFlow Feature extraction Gravel roads

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Watermarking algorithms for JPEG 2000 lossy compressed images 5

Watermarking algorithms for JPEG 2000 lossy compressed image...

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5th Information Technology and Nanotechnology 2019: image processing and Earth Remote Sensing, ITNT 2019

作者： Fedoseev, v. Androsova, T. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute RAS Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a property of semi-fragility to the image quality. It means that the hidden information is preserved after high-quality compression, and is destroyed in the case of significant degradation. Experimental investigations confirm this property for both algorithms. They also show that the introduced embedding distortions in terms of PSNR and PSNR-HvS are in almost linear dependence on the quantization parameter. It allows us to control the quality at an acceptable level when information embedding. © 2019 CEUR-WS. All rights reserved.

关键词： Embeddings

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Development and Analysis Methods of images Quality Improvement with violation of Contrast

Development and Analysis Methods of Images Quality Improveme...

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Wave Electronics and its Application in Information and Telecommunication systems (WECONF)

作者： M. I. Baldin N. A. Kozyrev I. A. Gorshunov v. S. Kolomoitcev ITMO University St. Petersburg Russia Saint-Petersburg state university of economics St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation St. Peterburg Russia

ISBN: (数字)9781728149448

ISBN: (纸本)9781728149455

Method is proposed to improve the visual quality of images based on algorithms based on histogram equalization. The work examined widely used algorithms of histograms equalization, described their advantages and disadvantages. The classification of images with contrast violation has been introduced. A focus of the work is the development of a new method of image processing with different types of contrast violations. This approach provides improved images quality, regardless of classification and characteristics.

关键词： equalization histogram contrast image Equalization histograms images imagery (Psychotherapy) image quality Taxonomy Quality Improvement algorithms

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Detection of Schistosoma haematobium using lensless imaging and flow cytometry, a proof of principle study 20

Detection of Schistosoma haematobium using lensless imaging ...

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Optical Diagnostics and Sensing XX: Toward Point-of-Care Diagnostics 2020

作者： Agbana, Temitope Nijman, Patrick Hoeber, Max van Grootheest, Derk van Diepen, Angela van Lieshout, Lisette Diehl, Jan-Carel verhaegen, Michel vdovine, Gleb Delft University of Technology Delft Center for Systems and Controls Delft2628CD Netherlands Delft University of Technology Sustainable Design Engineering Ide Delft2628CE Netherlands Leiden University Medical Center Department of Parasitology Leiden2300RC Netherlands

ISBN: (数字)9781510632585

ISBN: (纸本)9781510632578

We present a simple method for the diagnosis of urinary schistosomiasis using an in-line lensless holographic microscope combined with flow cytometry technique. Using simple image processing algorithms and binary image classifier, our system provides automated detection of Schistosoma haematobium eggs in infected urine samples. Registered hologram is reconstructed by applying backpropagation from sensor to sample plane and reconstructed image is automatically analysed for the presence of S. haematobium eggs. Designed for use in a resource-poor laboratory setting, our proposed method has been implemented using a Raspberry Pi computer. From pre-clinical test performed with human urine samples spiked with S. haematobium eggs (approximately 200 eggs per 12 ml of urine), we achieved a sensitivity and specificity of 50.6% and 98.6% respectively. Our proposed method requires no complex sample preparation methods making the system simple to operate and useable in point-of-care diagnosis of urinary *** method can be optimized to complement existing diagnostic procedures for the detection of S. haematobium eggs and can be deployed to inaccessible remote areas. © 2020 SPIE.

关键词： Flow cytometry

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Intelligent systems for monitoring and controlling chip formation when cutting difficult-to-machine materials 13

Intelligent systems for monitoring and controlling chip form...

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13th International Conference on Mechanical Engineering, Automation and Control systems (MEACS)

作者： Maksarov, v. v. Makhov, v. E. St Petersburg Min Univ 21 Line St Petersburg 199106 Russia Mozhaisky Mil Space Acad Zhdanov St 13 St Petersburg 197082 Russia

This research addresses topical issues of creating industrial monitoring systems for chips, which are formed when materials are cut, based on light field-based optical detectors. A proposed scheme uses a light field (LF) method to register formed chips with an optical detector. An algorithms and monitoring methods for key geometric parameters of chip shapes are developed based on the analysis of images obtained from LF cameras. They feature digital image capturing and morphological analysis. For practical implementation of developed algorithms, National Instruments software platform is used. This research shows that the result accuracy in determining geometric characteristics of chips using the proposed solutions makes it possible to run diagnostics for cutting and material processing equipment.

关键词： Intelligent systems

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