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Trip-GhostNet for hyperspectral image classification 2

Trip-GhostNet for hyperspectral image classification

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2nd international conference on computer vision and Data Mining, ICVDM 2021

作者： Zhang, Zitong Hu, Xiaoqing Wang, Siqing Kang, Lei Ma, Qiaoyu Beijing100083 China Beijing100083 China

The classification of hyperspectral remote sensing images (HSI) is an important task in the processing and application of hyperspectral images. Convolutional neural network (CNN) has significant advantages in the extraction and fusion of hyperspectral image spectrum and spatial features, so it has become a common method in the field of HSI classification. However, the spectral domain feature redundancy of hyperspectral images is very high, and the spatial domain feature structure is complex, which makes CNN more time-consuming and higher memory requirements in the feature extraction process. Based on this problem, lightweight networks with fewer parameters have gradually become the main application method in the field of HSI classification. In order to reduce the computational complexity of deep feature extraction and take into account the shallow features, a high-order nonlinear Ghost module is proposed on the basis of the original Ghost linear transformation module. Furthermore, in view of the independent characteristics of each dimension of HSI, a Trip-GhostNet is proposed, which simultaneously extracts and fuses features from three dimensions in a lightweight manner. According to the distribution characteristics of HSI, the influence of attention embedding methods in the high-order Ghost module of each branch on the feature extraction is compared and analyzed. The results show that the proposed model can reduce model calculations and improve classification accuracy, and is suitable for HSI classification problems. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Feature extraction

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Research on Inverted Pendulum Control System Based on vision Sensor

Research on Inverted Pendulum Control System Based on Vision...

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ICMLCA 2021;2nd international conference on Machine Learning and computer Application

作者： Dongyang Zhang Jinxu Wang Hualiang Zhang Lifeng Yu

Aiming at the inapplicability of the inverted pendulum system of encoder sensor in the bad environment, this paper studies the inverted pendulum control system based on visual sensor, and puts forward a scheme of achieving the deflection angle of an inverted pendulum by a visual sensor. The deflection angle of the pendulum is calculated by processing each frame image achieved by the visual sensor. That is, based on the correlation function of OpenCV open source library, the deviation angle of swing rod is identified. Finally, the experiment successfully verifies that the scheme proposed in this paper is feasible and effective.

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Comparative Analysis using Transfer Learning Models VGG16, Resnet 50 and Xception to Predict Pneumonia

Comparative Analysis using Transfer Learning Models VGG16, R...

引用

international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Anil B. Desai D. R. Gangodkar Bhaskar Pant Kumud Pant Department of Computer Science & Engineering Graphic Era Deemed to be University & Graphic Era Hill University Dehradun U.K India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun U.K India Department of Biotechnology Graphic Era Deemed to be University Dehradun U.K India

ISBN: (纸本)9781665474177

Pneumonia is common illness affecting 450 million persons every year and approximately 7% of the world population getting affected among all age groups. The existence of X-ray images is an age-old method in diagnosis of different diseases. Pneumonia is one such pulmonary disease, which can be predicted by manually examining the chest X-ray (CXR) images, but the manual inspection may have its own limitations. Developments in computer vision Techniques using image processing lack in predicting the disease accurately. Improvements based on deep learning Neural Network models also have challenges diagnosing disease to the nearest accuracy. Recent developments based on transfer learning techniques have shown promising results. However, many researchers have published their results after experimenting with the individual models and also using ensemble techniques with varying degree of accuracy. In this paper we try to make comparative analysis using deep learning models Visual Geometry Group (VGG16), Residual Networks (Resnet50) and Extreme Inception (Xception) based on transfer learning techniques for different parameter setup on different datasets and by applying data augmentation techniques for the same cycle of epochs. The analysis carried out would help to understand the models under consideration for their capability to learn and predict new image sets.

关键词： Deep learning Analytical models Visualization Pulmonary diseases Computational modeling Transfer learning Predictive models

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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

引用

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.

关键词： Surveys

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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...

引用

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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