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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

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Aircraft Segmentation from Remote Sensing images using Modified Deeply Supervised Salient Object Detection with Short Connections 2

Aircraft Segmentation from Remote Sensing Images using Modif...

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2nd international conference on Mathematics and computers in Science and Engineering (MACISE)

作者： Meeboonmak, Natnicha Cooharojananone, Nagul Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand

ISBN: (纸本)9781728166957

image segmentation is a process of clustering the pixels into some groups. It is essential and useful in image processing and computer vision task. The segmentation result may affect the outcome of many types of work, for example, image recognition and image detection. One of the most exciting works of image segmentation is aircraft segmentation from remote sensing images. This work is very challenging due to many factors, such as the complexity of the background, the shadow, and the various type of aircraft. To handle these problems, in this paper we implement the deeply supervised salient object detection with short connections (DSS) with our dataset which is divided into two categories: simple case and dificult case, and we propose some idea of post-process which can increase some accuracy by using pre-trained and adding L2-normalization in some layer of DSS architecture to create the mask that can reduce the noise from background effectively. From the experimental result, using L2-normalization with our post-processing can increase the performance by 0.61% and 2.14% in the simple case and difficult case respectively, compared with using only the DSS method. So our method performs well when the background is complicated, which always occurs in reality.

关键词： Aircraft image segmentation Deeply supervised network Short connection L2-normalization

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Expression of Concern for: image Captioning using Deep Learning

Expression of Concern for: Image Captioning using Deep Learn...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Aryan Raj Himanshu Kumar V. Arul School of Computing Science And Engineering Galgotias University Uttar Pradesh

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437905

With the improvement of Deep learning, the combination of computer vision and natural language processing has drawn in a ton of consideration. over the most recent couple of years. Picture subtitles address the means to figure out how to utilize at least one sentence to comprehend the visual substance of the picture. The method involved in creating a significant depiction of an undeniable degree of picture Semantics requires not just the impression of an article and a spot, yet the capacity to investigate the nature, elements, and connections between these items. Even though photograph subtitling is troublesome work, numerous analysts have tracked down critical enhancements. In this paper, we portray exhaustively the three inscriptions for symbolism utilizing profound neural organizations: CNN-RNN-based, CNN-CNN-based. What's a more dynamic-based structure. Then, At that point, we have available the fill-in that addresses three techniques in succession, clarifying the insightful measurements and sum up the principle benefits and burdens.

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Evaluations of Shallow Groundwater Recharges and Water Use Practices at Robit Watershed 9th

Evaluations of Shallow Groundwater Recharges and Water Use P...

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9th EAI international conference on Advancement of Science and Technology, ICAST 2021

作者： Takele, Dagnew Y. Tilahun, Seifu A. Zimale, Fasikaw A. Schmitter, Petra Bihonegn, Bayu G. Eshetie, Daniel G. Water Supply and Sanitation Core Process Amhara Design and Supervision Works Enterprise Bahir Dar Ethiopia Faculty of Civil and Water Resource Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia International Water Management Institute P.O. 11081 Yangon Myanmar Department of Hydraulic and Water Resource Engineering Kombolcha Institute of Technology Wollo University Dessie Ethiopia Department of Hydraulic and Water Resource Engineering Institute of Technology University of Gondar Gondar Ethiopia

ISBN: (纸本)9783030937119

Groundwater resources have a fundamental importance to satisfy the rapidly increasing agricultural, livestock and domesticwater requirements within the region especially in Robit watershed. Hence the quantification of this water resource is important for the efficient and sustainable water resource management. In this study spatial variability of irrigation water requirement, water use (abstractions) and groundwater recharge were estimated for Robit watershed within the eastern part of Lake Tana. Satellite image of Planet Scope on 2nd February 2017 was used to estimate area and type of crops cultivated in the watershed. CROPWAT model has been used to calculate the particular evapotranspiration and water requirement for irrigation using local climatic data. This was calculated for the dominant irrigated crops of Khat (Catha edulis Forsk), hop (Rhamnus prinoides), coffee, tomato and green pepper within the study area. Calibrated QSWAT model was applied to estimate net ground water recharges using local climatic data, soil map, crop management data and derived land use map from satellite image processing. The assessment showed that the entire amount of water applied for irrigation, domestic and livestock purposes was estimated as 1.35 Mm3/year, 0.02 Mm3/year and 0.03 Mm3/year respectively. Net groundwater recharge was estimated as 3.18 Mm3/year within the watershed. The estimation from the water abstraction survey showed that the total volume of water abstracted within the watershed was estimated as 1.40 Mm3/year. From the assessment it can be clearly seen that only 44% of ground water resource is extracted annually within the area and there is some potential to expand irrigation areas and the current water usage for various purposes in the future. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Surveys

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Automatic Recognition of Medicinal Plants 2nd

Automatic Recognition of Medicinal Plants

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2nd international conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2020

作者： Krishna Vinesha, P. Lakshmi Priyanka, P. Lakshmanan, L. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789811575105

A fully robotic strategy for recognizing therapeutic plants using PC vision and artificial intelligence methods has been demonstrated. The leaves of 25 distinctive therapeutic plant types were gathered and captured using an advanced cell in a research environment. Countless highlights have been separated from each sheet, for example, length, width, border and area, number of vertices, shading, border and frame territory. Then, some highlights determined by these properties have been elaborated. The best results were obtained by a CNN algorithm that uses a 10 times cross-approval strategy. By an accuracy of 98.3%, the CNN algorithm accomplishes superior than any other AI approach, such as the nearest K-neighbor, Naïve Bayes, KNN and neural systems. Those results are enriching and later work will be equipped to use a huge data set and elite processing offices to explore the exposure of deep learning neural systems to recognize restoration plants used in essential medicinal services. It is expected that an electronic or portable PC framework for the programmed recognition of restoration plants will guide the population of the neighborhood to enhance their knowledge of therapeutic plants, to help taxonomists develop recognizable test methods for increasingly competent species. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image recognition

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A Survey on image Style Transfer Approaches Using Deep Learning 2

A Survey on Image Style Transfer Approaches Using Deep Learn...

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2019 2nd international conference on computer Information Science and Artificial Intelligence, CISAI 2019

作者： Zhao, Changshen Department of Science University of Technology Qingdao Shandong266033 China

image style transfer has been an important branch of computer vision and image processing. Inspired by the development of deep learning, applications of Convolutional Neural Networks (CNNs) in recomposing content and style of two separated images were proven to be effective by the recent works of image style transfer. This paper provides a survey of image style transfer, which focuses on methods which using deep learning. In addition, this paper also provides information of methods without deep learning, and seeks their points of innovation. In this paper, all references are published ranging from 2001 to 2017, which presents an overview of progress in this realm over the past two decades. © Published under licence by IOP Publishing Ltd.

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Feature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency 2

Feature extraction Hue, Saturation, Value (HSV) and Gray Lev...

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2nd international conference and Exhibition on Sciences and Technology, ICEST 2020

作者： Lesiangi, F.S. Mauko, A.Y. Djahi, B.S. Department of Computer Science Faculty of Science and Engineering University of Nusa Cendana Kota Kupang Indonesia

South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The results of testing variants using 10 fold get an accuracy rate for GLCM texture features of 55%, for HSV color features of 62.5% and a combination of color and texture features of 91.67%. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Feature extraction

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A Hybrid Differential Algorithm for image Enhancement

A Hybrid Differential Algorithm for Image Enhancement

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2020 international conference on computer vision, image and Deep Learning, CVIDL 2020

作者： Qing-Li, Chen Guo, Huang Hong-Yin, Q.I.N. Leshan Normal University School of Computer Science Leshan614004 China

ISBN: (纸本)9781728194813

In order to improve the enhancement effect of fractional differential algorithm, according to the fact that 2nd integer order differential is beneficial to enhance image edge and texture details, and fractional-order differential (G-L differential) method is helpful to enhance and extract texture the texture details in image smoothing area, an hybrid fractional image enhancement algorithm combing fractional and integer order differential is proposed in this paper. This method overcomes the limitation that the fractional differential order can only be 0-1 in image enhancement, and expands the differential order to more than 2. And the enhancement effect of texture and edge is superior to other differential methods. © 2020 IEEE.

关键词： image enhancement

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Research on Design and Application of Brand vision Inspection and Sorting System Based on image processing 2

Research on Design and Application of Brand Vision Inspectio...

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2nd international conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2020

作者： Wu, Caixia Wuxi Vocational College of Science and Technology Jiangsu214028 China

ISBN: (纸本)9781728196381

Visual inspection technology has been more and more widely used in industrial production, which is crucial to improve production efficiency and production intelligence. In order to monitor the sorting equipment more effectively, it is necessary to carry out accurate real-time detection of the operation parameters of the important actuators and functional components in the sorting process. Although computer vision technology does not touch the measuring object, it can accurately obtain a lot of information from the acquired images, and then analyze and process the information to obtain specific information such as object size, surface defects, chromaticity, etc. In the sorting operation of traditional assembly line, manual sorting is the main method. The workers' repeated sorting for a long time can not meet the requirements, which greatly affects the efficiency and accuracy of work. This paper proposes to add visual detection system based on image processing on the basis of the original sorting system, so as to change the whole control system into a closed-loop from the structure, effectively solve the problems in the process of loading bags, and improve the accuracy of sorting. © 2020 IEEE.

关键词： Production efficiency

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Application of Quantum Generative Adversarial Learning in Quantum image processing 2

Application of Quantum Generative Adversarial Learning in Qu...

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2nd international conference on Information Technology and computer Application, ITCA 2020

作者： Ren, Wanghao Li, Zhiming Li, Hailing Li, Yang Zhang, Chunwei Fu, Xiaoqian University of Jinan School of Information Science and Engineering Jinan250022 China Shandong College of Electronic Technology Jinan250022 China

ISBN: (纸本)9780738111414

Quantum machine learning is a hot topic in the quantum community recently. The combination of quantum machine learning and image processing is also one of the researchers' concerns. With the promotion of deep learning to process images, the field of image processing has also shown amazing potential. The combination of quantum computing and artificial intelligence can not only exert the computing power of quantum computing but also find more applications in the field of image processing. This article combines quantum generative adversarial learning with the field of image processing. A quantum generative adversarial network is designed to load and learn classical image data. Numerical simulations intuitively show that quantum algorithms can also effectively process images. In our scheme, using N qubits can load 2N classical bits of image data. © 2020 IEEE.

关键词： Generative adversarial networks

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Application of sequential processing of computer vision methods for solving the problem of detecting the edges of a honeycomb block 2

Application of sequential processing of computer vision meth...

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2nd international Scientific conference on Applied Physics, Information Technologies and Engineering (APITECH)

作者： Kubrikov, M., V Paulin, I. A. Saramud, M., V Kubrikova, A. S. Reshetnev Siberian State Univ Sci & Technol 31 Krasnoyarsky Rabochy Av Krasnoyarsk 660037 Russia Siberian Fed Univ 79 Svobodny Pr Krasnoyarsk 660041 Russia

The article describes the application of the Hough transform to a honeycomb block image. The problem of cutting a mold from a honeycomb block is described. A number of image transformations are considered to increase the efficiency of the Hough algorithm. A method for obtaining a binary image using a simple threshold, a method for obtaining a binary image using Otsu binarization, and the Canny Edge Detection algorithm are considered. The method of binary skeleton (skeletonization) is considered, in which the skeleton is obtained using 2 main morphological operations: Dilation and Erosion. As a result of a number of experiments, the optimal sequence of processing the original image was revealed, which allows obtaining the coordinates of the maximum number of faces. This result allows one to choose the optimal places for cutting a honeycomb block, which will improve the quality of the resulting shapes.

关键词： Binary images

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