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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是121-130 订阅

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Foveated image processing for Faster Object Detection and Recognition in Embedded systems Using Deep Convolutional Neural Networks 8th

Foveated Image Processing for Faster Object Detection and Re...

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8th International conference on Biomimetic and Biohybrid systems

作者： Jaramillo-Avila, Uziel Anderson, Sean R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9783030247409;9783030247416

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. this presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416x416 to 96x96 pixels, on an embedded GPU - an NVIDIA Jetson TX2 with 256 CUDA cores. the results show that it is possible to achieve a 4x speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416 x 416 and 128 x 128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling.

关键词： Pixels

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Adaptive algorithm of conforming image matching 5

Adaptive algorithm of conforming image matching

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Data Science Session at the 5th International conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Fursov, V.A. Goshin, Ye.V. Pugachev, K.G. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

this paper presents an adaptive algorithm of conforming image matching based on the principle of conformity. the algorithm consists of several main stages. At the first stage, we find the corresponding points using a minimum value of conformity as the measure of points’ similarity. We define a conformity function as the sum of all possible combinations of squared differences of pixel intensity values on the fragments that are matched. then, we perform an adaptive procedure of errors correction considering an intensity gradient distribution. An important feature of the algorithm is the finding of error points using a criterion of maximum value of samples’ conformity for every fragment of the disparity map. the results of experiments on the "Teddy" test images are shown. © 2019 CEUR Workshop Proceedings.

关键词： Adaptive algorithms

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Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems 4

Small plot identification from video streams for high-throug...

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conference on Autonomous Air and Ground Sensing systems for Agricultural Optimization and Phenotyping IV held at SPIE Defense + Commercial Sensing conference

作者： Wang, Xu Amos, Cameron Lucas, Mark Williams, Grant Poland, Jesse Kansas State Univ Dept Plant Pathol 1712 Claflin Rd4024 Throckmorton PSC Manhattan KS 66506 USA Kansas State Univ Dept Biol & Agr Engn 1016 Seaton Hall920 N 17th St Manhattan KS 66506 USA Kansas State Univ Dept Aviat Technol Polytech Campus2310 Centennial Rd Salina KS 67401 USA

ISBN: (数字)9781510626829

ISBN: (纸本)9781510626829

In breeding and genetics programs, thousands of small plots in the size of a few square meters each having a unique genetic entry, are used to evaluate huge numbers of candidates and large mapping populations. Low-altitude remote sensing with unmanned aerial systems (UAS) can generate high geospatial resolution measurements of plants and enable high temporal resolution measurements through multiple crop growth stages. However, to identify individual plot from aerial images robustly and automatically becomes a key challenge in high throughput phenotyping (HTP) using UAS. In this case study, we captured super high-resolution video clips of wheat canopies by a UAS at low altitude. We proposed an image processing pipeline for identifying individual plot from video frames. Preliminary results indicate the methods can highly accelerate the process of linking genotypes to individual plot images and can be fully automated. this research provides a proof-of-concept and has broad implications of novel phenomics application of UAS that is scalable to tens-of-thousands of plots in crop breeding and genetic studies.

关键词： high-throughput phenotyping unmanned aerial system image processing wheat breeding

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Solution of overdetermined systems of equations using the conforming subsystem selection 5

Solution of overdetermined systems of equations using the co...

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5th International conference on Information Technology and Nanotechnology, ITNT 2019

作者： Fursov, V.A. Kotov, A.P. Goshin, Ye V. Samara National Research University Moskovskoye shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya Street 151 Samara443001 Russia

Within this research, we consider an overdetermined system of equations generated on a small number of observations. the degrees of freedom of the overdetermined system and the number of observations are roughly equal. We propose a modification of the Conformed Estimates Method (CEM). the method improvement consists of the usage of an auxiliary system, which is formed from the initial system by reducing the columns. the estimates can be obtained on the higher-level auxiliary subsystem, which is the freest from noise. We consider our method in the problem of computing Rational Polynomial Coefficients (RPCs) from well-established Ground Control Points (GCPs). © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Visual-inertial odometry algorithms on the base of thermal camera 5

Visual-inertial odometry algorithms on the base of thermal c...

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Data Science Session at the 5th International conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Alekseev, A.P. Goshin, E.V. Davydov, N.S. Ivliev, N.A. Nikonorov, A.V. Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia Samara National Research University Moskovskoe Shosse 34А Samara443086 Russia

A trajectory building based on a camera data is one of the most popular tasks in the field of machine vision. In particular, this task appears when it is necessary to navigate in the absence of signals from global navigation systems such as GLONASS and GPS. In this work, study of existing methods of visual odometry for the flight trajectory restoration by shooting an infrared camera of the thermal range were conducted. To improve the accuracy, it is proposed to use the data from inertial sensors. As a result, it is shown that the proposed solution allows to successfully solve the problem of trajectory reconstruction. © 2019 CEUR Workshop Proceedings.

关键词： Infrared devices

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Determining the proximity of groups in social networks based on text analysis using big data 5

Determining the proximity of groups in social networks based...

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Data Science Session at the 5th International conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Mukhin, A.S. Rytsarev, I.A. Paringer, R.A. Kupriyanov, A.V. Kirsh, D.V. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

the article is devoted to the definition of such groups in social networks. the object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. the existing algorithms of text analysis, mainly of large volume, were investigated and applied. © 2019 CEUR Workshop Proceedings.

关键词： Big data

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Brain Tumor Classification Using Efficient Deep Features of MRI Scans and Support Vector Machine

Brain Tumor Classification Using Efficient Deep Features of ...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Ahmad Neyaz Khan Habibeh Nazarian Noorbakhsh Amiri Golilarz Abdoljalil Addeh Jian Ping Li Ghufran Ahmad Khan MITS A.P. India Yasuj Branch Islamic Azad University Yasuj Iran School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (数字)9781665405058

ISBN: (纸本)9781665405065

Brain cancer is a life-threatening disease and hampers the normal operation of the human body. For correct diagnosis and methodical treatment planning, it is essential to detect the brain tumor in initial stages of development. this study proposes an intelligent diagnostic method for early detection of brain tumor. In the developed method, the deep features of magnetic resonance imaging (MRI) scans are used as the input of support vector machine (SVM). In the first step of the proposed method, Grab cut method is applied for segmenting tumor region, then the segmented images are fed to convolutional neural network (CNN) for deep feature extraction. Following feature extraction module, minimum Redundancy Maximum Relevance (mRMR) algorithm is used to select the most efficient deep features. Finally, the selected deep features are fed into SVM in the classification module. In addition, we applied the black widow optimization algorithm (BWOA) for optimal tuning of hyperparameters of CNN and SVM. the developed method applied on BraTS 2015 datasets and the obtained results showed that the developed method is effective and may be employed in computer-aided diagnosis systems to classify the type of tumor.

关键词： Tumors Feature extraction Support vector machines Magnetic resonance imaging Classification algorithms Optimization image segmentation

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Mobile-cloud data processing system on digital images

Mobile-cloud data processing system on digital images

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IEEE International conference on Industrial Informatics (INDIN)

作者： Alexey Samoylov Yuri Borodyansky Andrei Kostyuk Ivan Polovko Institute of Computer Technology and Information Security Southern Federal University Taganrog Russian Federation

the quality of measurements in modern photogrammetric complexes essentially depends on the selected scale of the survey, the location of the imaging cameras, the object's own shape, and also the limitations imposed by the survey conditions. therefore, to ensure the required quality metrics in each specific case, it is necessary to solve the task of selecting the number, parameters and location of cameras for a given class of objects. the reason for the limited possibilities lies in the rigid fixing of the bundle of technical devices and the rules for processing images from them. At the same time, the creation of photogrammetric complexes by default implies the availability of specialized software, and the development of methods for computer image processing, the performance of graphics processors and personal devices (laptops, tablets, etc.) makes it possible to expand the functionality of this provision. Existing program components of photogrammetric complexes represent a bunch of means of preliminary processing, pattern recognition and solving countable problems in their various variations. Despite the existence of such a basic structure, software packages are built heuristically, and the set and sequence of interaction of the facilities are static. the article proposes a mobile cloud system that allows minimizing hardware requirements by implementing photogrammetry algorithms for image processing and analysis in the form of cloud services.

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Multi-scale Neural Style Transfer Based on Deep Semantic Matching 4th

Multi-scale Neural Style Transfer Based on Deep Semantic Mat...

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4th International conference on Cognitive systems and Information processing (ICCSIP)

作者： Yu, Jiachen Jin, Li Chen, Jiayi Tian, Zhiqiang Lan, Xuguang Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9789811379864;9789811379857

Existing Neural Style Transfer (NST) algorithms do not migrate styles well to a reasonable location where the output image can render the correct spatial structure of the object being painted. We propose a deep semantic matching-based multi-scale (DSM-MS) neural style transfer method, which can achieve the reasonable transfer of styles guided by the prior spatial segmentation and illumination information of input images. First, according to real drawing process, before an artist decides how to paint a stroke, he/she needs to observe and then understand subjects, segmenting space into different regions, objects and structures and analyzing the illumination conditions on each object. To simulate the two visual cognition processes, we define a deep semantic space (DSS) and propose a method for calculating DSSs using manual image segmentation, automatic illumination estimation and convolutional neural network (CNN). Second, we define a loss function, named deep semantic loss, which uses DSS to guide reasonable style transfer. third, we propose a multi-scale optimization strategy for improving the efficiency of our method. Finally, we achieve an interdisciplinary application of our method for the first time-painterly rendering 3D scenes by neural style transfer. the experimental results show that our method can synthesize images in better original structures, with more reasonable placement of each styles and visual aesthetic feeling.

关键词： Neural style transfer image segmentation Illumination estimation Patch matching

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Wireless Capsule Endoscopic image Enhancement Method Based on Histogram Correction and Unsharp Masking in Wavelet Domain

Wireless Capsule Endoscopic Image Enhancement Method Based o...

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IEEE International New Circuits and systems conference

作者： Mingzhu Long Xiang Xie Guolin Li Zhihua Wang Institute of Microelectronics Tsinghua University Beijing China Graduate School at Shenzhen Tsinghua University Shenzhen China Tsinghua University Beijing China

Due to the limited illumination and complex environment in the gastrointestinal (GI) tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. In order to solve this problem, a method based on histogram correction and unsharp masking in the wavelet domain is proposed in this paper, to improve the contrast and reveal details of endoscopic images. the HSI color space is adopted for color naturalness, and a logarithm based histogram correction method (LHCM) is proposed to improve the contrast and avoid aritifacts. Details are further enhanced by unsharp masking. Experimental results show that performance of the proposed method is better compared with the state-of-art methods. It can be applied to general images and have good effects as well.

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