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Application and Technical Analysis of computer vision Technology in Natural Resource Survey 1

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4th international conference on Artificial Intelligence in China, AIC 2022

作者： Wang, Yuehong Luo, Hao Tianjin Natural Resources Investigation and Registration Center Tianjin300201 China

ISBN: (数字)9789819912568

ISBN: (纸本)9789819912551

Natural resources are the material basis for human survival and national economic and social development. Natural resources survey is related to major national decision-making and deployment, fully supports and serves the economic and social development and ecological civilization construction in the new era, and systematically grasps the quantity, quality, spatial distribution, development and utilization, ecological status, and dynamic changes of national natural resources and landmark substrates. computer vision technology uses computers to process, analyze and understand images to identify targets and objects in various patterns. the natural resources are photographed and imaged by industrial unmanned cameras or remote sensing satellites, and the characteristics are compared according to image processing, to realize the investigation and analysis of natural resources, improve the efficiency of natural resource management investigation, and provide a reference for planning decision-making and implementation. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Research on Ship Target recognition based on Infrared image Method 23

Research on Ship Target Recognition based on Infrared Image ...

引用

4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Cao, Yibo Cheng, Wei Wang, Xuming Huang, Yuxin School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan40063 China Tianjin Navigation Instruments Research Institute Tianjin300131 China National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9798400700705

Infrared thermal imaging technology has been widely applied in the field of target detection at present, which lays the foundation of research on situational awareness for the Maritime Autonomous Surface Ship. Compared with the traditional image recognition technology with defects such as low recognition accuracy, strong light dependence and poor anti-interference ability, infrared thermal imaging technology is not only adaptive to different light intensity environments, but also has the advantages of high concealment, strong detection capability, long detection distance and high detection sensitivity. In this paper, it proposes an improved method of Canny segmentation algorithm based on maximum inter-class variance method on the basis of infrared imaging, where the image is preprocessed by wavelet transform, it is achieved the moving target detection, to restrain noise interference. And then, the edge blurring is effectively processed by using the mask image of Canny edge detection as inputs of pattern recognition. the optimal threshold is determined by the improved Otus algorithm, which can stabilize the optical flow field of the background, to realize effective segmentation of ship images to obtain the high definition moving target. Finally, the availability of the algorithm has been verified by taking the tourist ferry under the Yangtze River as the object. the results showed that the improved algorithm can stabilize the optical flow field of the background, and the application effect is improved, which can provide technical support for the research and application of intelligent ship target perception. © 2023 ACM.

关键词： image segmentation

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Mental Workload Classification with One-Dimensional CNN Using fNIRS Signal 10th

Mental Workload Classification with One-Dimensional CNN Usin...

引用

10th Biennial international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Kumar, Ashish Karmakar, Subashis Agarwal, Isha Pal, Tandra Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur West Bengal India

ISBN: (纸本)9783031451690;9783031451706

As one of the promising technology, Brain-computer Interface assists people by exploring various aspects of brain functionality. Analyzing and decoding brain signals to classify mental workload can help in the diagnosis and treatment of various neurodegenerative diseases and neurological disorders like learning disability. In this pursuit, functional near-infrared spectroscopy has emerged as a promising non-invasive technique that uses blood flow patterns to analyze brain signals. In this study open-access functional near-infrared spectroscopy datasets are used for mental workload classification. We have considered it as a two class problem and proposed a one-dimensional convolutional neural network to distinguish between mental task and rest, and compared the performance with two other machine learning methods, support vector machine and deep neural network. the proposed one-dimensional convolutional neural network model eliminates the requirement for complex brain image processing providing a computationally efficient alternative and comparable accuracy.

关键词： Brain-computer interface (BCI) fNIRS Cognitive Load Mental Task Convolutional Neural Network (CNN)

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Research on image preprocessing and target detection of vehicle-machine cooperative system

Research on image preprocessing and target detection of vehi...

引用

computer vision, image and Deep Learning (CVIDL), international conference on

作者： Jiaxin Song Li Bing Zhaoxiang Dong Huiting Han Yongchao Hou Yulan Wei School of Engineering Huzhou University Huzhou China

In the present cooperative competition of robot, the robot needs to use its own camera module to collect real-time images and identify the enemy robot according to the collected images. However, due to the interference of some external factors in the arena, the contrast and clarity of the images collected by the robot are poor, which is not conducive to the accurate recognition of the robot. therefore, this paper will preprocess the images collected by the robot, and reduce the interference caused by off-site factors through image filtering, image enhancement, and grayscale processing. then use the method based on HSV color detection to achieve accurate identification of enemy robots. Finally, the accurate coordinates of the enemy robot can be obtained by using the target contour method to meet the accuracy requirements. through the above methods, the robot can quickly and accurately obtain the position and shape of the enemy robot.

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Metal and Metal Oxide Nanoparticle image Analysis Using Machine Learning Algorithm 5th

Metal and Metal Oxide Nanoparticle Image Analysis Using Mach...

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5th EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Bannigidad, Parashuram Potraj, Namita Gurubasavaraj, Prabhuodeyara Department of Computer Science Rani Channamma University Karnataka Belagavi India Department of Chemistry Rani Channamma University Karnataka Belagavi India

ISBN: (纸本)9783031283239

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. the advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. the proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. the classification is done by using PNN and K-NN classifiers. the K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Synthesis (chemical)

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A Large Volume Natural Tamil Character Dataset 6th

A Large Volume Natural Tamil Character Dataset

引用

6th international conference on computer vision and image processing, CVIP 2021

作者： Arun, M. Arivazhagan, S. Ahila Priyadharshini, R. Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

ISBN: (纸本)9783031113451

this paper focuses on recognizing the individual Tamil characters in images of natural scenes. Even though natural character dataset are available publicly for different languages, no specific standardized dataset is available for natural Tamil characters. In this work, we present a publicly available Tamil character dataset "Mepco-TamNatChar" for natural Tamil character recognition. this research work presents the preliminary results on natural Tamil character recognition using spatial and machine learnt features. We report an accuracy of 87.50 ± 1.42% for a train test ratio of 50:50 using machine learnt features. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Comparison of Dark Channel Prior and Contrast Limited Histogram Equalization for the Enhancement of Underwater Fish image 4

Comparison of Dark Channel Prior and Contrast Limited Histog...

引用

4th IEEE international conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Ahmad Hijazi, Mohd Hanafi Mei, Leong Jing Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

the application of artificial intelligence (AI) in aquaculture may improve the efficiency of fish farming management. computer vision is one of the fields in AI beneficial for aquaculture. However, the underwater image quality is usually low due to light scattering through the water. therefore, image enhancement is necessary before any further processing can be done. there are numerous image enhancement techniques for underwater images reported in the literature. In this paper, the comparison of the two most common image enhancement techniques for underwater images, the Dark Channel Prior (DCP) and Histogram Equalization (HE), is presented. the strength and weaknesses of each technique pertaining to the underwater images are also described. © 2022 IEEE.

关键词： image enhancement

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Clustering Running Titles to Understand the Printing of Early Modern Books 18th

Clustering Running Titles to Understand the Printing of Earl...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Vogler, Nikolai Goyal, Kartik Lemley, Samuel, V Schuldt, D. J. Warren, Christopher N. GSell, Max Berg-Kirkpatrick, Taylor Univ Calif San Diego La Jolla CA 92093 USA Georgia Inst Technol Atlanta GA 30332 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Simmons Univ Boston MA 02115 USA

ISBN: (纸本)9783031705427;9783031705434

We propose a novel computational approach to automatically analyze the physical process behind printing of early modern letterpress books via clustering the running titles found at the top of their pages. Specifically, we design and compare custom neural and feature-based kernels for computing pairwise visual similarity of a scanned document's running titles and cluster the titles in order to track any deviations from the expected pattern of a book's printing. Unlike body text which must be reset for every page, the running titles are one of the static type elements in a skeleton forme i.e. the frame used to print each side of a sheet of paper, and were often re-used during a book's printing. To evaluate the effectiveness of our approach, we manually annotate the running title clusters on about 1600 pages across 8 early modern books of varying size and formats. Our method can detect potential deviation from the expected patterns of such skeleton formes, which helps bibliographers understand the phenomena associated with a text's transmission, such as censorship. We also validate our results against a manual bibliographic analysis of a counterfeit early edition of thomas Hobbes' Leviathan (1651) [27]. (Code and data available at https://***/nvog/clustering-running-titles)

关键词： Historical document analysis Document provenance Document image processing Document clustering

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A Novel Attention-based Aggregation Function to Combine vision and Language 25

A Novel Attention-based Aggregation Function to Combine Visi...

引用

25th international conference on pattern recognition (ICPR)

作者： Stefanini, Matteo Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781728188089

the joint understanding of vision and language has been recently gaining a lot of attention in both the computer vision and Natural Language processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering. As both images and text can be encoded as sets or sequences of elements - like regions and words - proper reduction functions are needed to transform a set of encoded elements into a single response, like a classification or similarity score. In this paper, we propose a novel fully-attentive reduction method for vision and language. Specifically, our approach computes a set of scores for each element of each modality employing a novel variant of cross-attention, and performs a learnable and cross-modal reduction, which can be used for both classification and ranking. We test our approach on image-text matching and visual question answering, building fair comparisons with other reduction choices, on both COCO and VQA 2.0 datasets. Experimentally, we demonstrate that our approach leads to a performance increase on both tasks. Further, we conduct ablation studies to validate the role of each component of the approach.

关键词： computer vision

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Markerless Visual Servoing Using Virtual Face Tag and image Moment Invariants 4

Markerless Visual Servoing Using Virtual Face Tag and Image ...

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4th international conference on Robotics and computer vision, ICRCV 2022

作者： Zubair, Arif Fu, Yili Sohail, Amjad Khadija, Anwar Harbin Institute of Technology State Key Laboratory of Robotics and Systems Mechanical Engineering Department Harbin City China Space and Upper Atmosphere Research Commission Divisional Head TandD Hr Directorate General Karachi Pakistan Ned University of Engineering and Technology Industrial and Manufacturing Department Karachi Pakistan

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

A novel normalized-centered image moment invariant-based visual servoing scheme is presented. A fresh combination of the moments invariants is proposed for planar symmetrical objects that are parallel to the image plane at the desired position. the output of the face detection module in the image plane, that is a rectangle around the face is used as a virtual target, treated as a planar symmetrical object which always remains parallel to the image plane. the proposed method completely decouples the DoF and is 2D translation, rotation, and scaling invariant. It was experimentally validated on a real robotic arm using a haar cascade-based face detection system to accomplish visual servoing tasks for both 2D printed faces and actual 3D faces. Unlike traditional deep learning based visual servoing schemes, the proposed markerless visual servoing technique does not require a 3D model of the face or a reference image/pose at the desired position and it can also work with unfamiliar faces without prior training. © 2022 IEEE.

关键词： Face recognition

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