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computer Vision-Based Detection and recognition Model of Objects for any Autonomous Systems in Low Illumination Condition 2

Computer Vision-Based Detection and Recognition Model of Obj...

引用

2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Ashok, P. Kavinilavu, A. Ananthi, S. Suganthi, P. Iniyan, E.U. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India 5Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665437899

The theories and methods it studies have received extensive attention in many disciplines and fields, promoting the development of artificial intelligence systems and expanding the possibilities of computer applications. image processing.is an important field of pattern recognition methods. The widely used character recognition (OCR) is a typicalapplication of pattern recognition in image processing. The pattern recognition process can be seen as a mapping process from the sample space to the category space. If you divide an n-dimensional feature space with n features asparameters into different regions, each region corresponds to a class of pattern classes. Among them, feature selection and extraction is an important part of pattern recognition. Ifthe selected features can fully reflect the essential features of the class, the classifier is easier to design. According to the presence or absence of standard samples, pattern recognition can be divided into surveillance identification methods and non-supervisory identification methods. The supervisory identification method is to design a classifierunder the condition that the class to which the training sample belongs is known, and the method for recognizing the sample is performed by the classifier. © 2022 ieee.

关键词： Character recognition

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A Paradigm Shift towards computer Vision

A Paradigm Shift towards Computer Vision

引用

2023 ieee International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Chaithra, N. Jha, Janhvi Sayal, Anu Gupta, Veethika Gupta, Ashulekha Karnataka Bangalore India Taylor's University Department of Mathematics Malaysia Doon University School of Social Sciences Department of Economics Uttarakhand India Department of Management Uttarakhand Dehradun India

ISBN: (纸本)9781665474924

In today's world, machine learning, artificial intelligence, IoT, deep learning and several other techniques have become the need of the moment. One such division of artificial intelligence is computer vision. The main goal of computer vision development is to create paradigms for extracting data and information from images. It has various applications in the fields of industry, agriculture, automations, healthcare, e-commerce and much more. The study examines the most recent events and conceptual frameworks governing the progress of computer vision, with a focus on pattern recognition and image processing. using a variety of applications from the t field. This article attempts to discuss the most current results and applications in computer vision. © 2023 ieee.

关键词： computer vision

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RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photoplethysmography

RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photopleth...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Botina-Monsalve, D. Benezeth, Y. Miteran, J. Univ Bourgogne Franche Comte ImViA EA7535 Besancon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The acquisition of remote photoplethysmography (rPPG) signals is important in multiple applications. Recently, deep-learning-based approaches such as 3D convolutional networks (3DCNNs) have outperformed traditional hand-crafted methods. However, despite their robust modeling ability, it is well known that large 3DCNN models have high computational costs and may be unsuitable for real-time applications. In this paper, we propose a study of the 3DCNN architecture, finding the best compromise between heart rate measurement precision and inference time. The fast inference is obtained decreasing the input size while the precision performance is obtained introducing a new time and frequency-based loss function by adding the signal-to-noise-ratio component to the regular Pearson's correlation loss function. In addition, changing the input color space from RGB to YUV also improved heart rate measurement precision. Using the VIPL-HR database, we retained the HR mean absolute error at 3.99 bpm which is comparable to 3.87 bpm of the state-of-the-art, while the GPU and CPU inference process improved around 88% from 51.77 ms to 2.32 ms in GPU and from 241.57 ms to 28.65 ms in CPU. The resulting network is called Real-Time rPPG (RTrPPG). We release the RTrPPG source code to encourage reproducibility(1).

关键词： Time-frequency analysis Three-dimensional displays image color analysis Computational modeling Graphics processing.units Photoplethysmography Real-time systems

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The Physical Fitness Video Tracking System of Football Players Based on Artificial Intelligence Algorithm

The Physical Fitness Video Tracking System of Football Playe...

引用

2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Zhang, Shubing Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Intelligent monitoring technology has become a new research direction in the field of computer vision in recent years. The computer vision system is the video data received from the camera, analyzed and learned through computer vision, and on this basis, the video recognition and visual monitoring, mainly involving pattern recognition and image processing. Today, many researchers, workers and universities in China and abroad are carrying out research on visual inspection and monitoring technology. This paper studies the football player physical fitness video tracking system based on artificial intelligence algorithm, and understands the relevant knowledge theory of the football player video tracking system on the basis of the literature, and then designs the football player video tracking system with artificial intelligence algorithm. The algorithm cited in the design is tested, and the test results show that the recognition rate of the improved algorithm in this paper is above 80%. © 2022 ieee.

关键词： computer vision

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

引用

1st ieee International conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 ieee.

关键词： Medical imaging

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences image restoration Faces

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General Category Network: Handwritten Mathematical Expression recognition with Coarse-Grained recognition Task 48

General Category Network: Handwritten Mathematical Expressio...

引用

48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Zhang, Xinyu Ying, Han Tao, Ye Xing, Youlu Feng, Guihuan Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University Software Institute Nanjing210023 China Nanjing University School of Computer Science and Technology Nanjing210023 China

ISBN: (纸本)9781728163277

Handwritten Mathematical Expression recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and the complex mathematical syntax. The encoder-decoder architecture has been widely used in recent HMER methods. Several works introduce HMER-related tasks that enhance the performance of HMER. We propose the General Category recognition Task (GCRT) and design the General Category Network(GCN) to perform HMER and GCRT in parallel. GCRT alleviates the symbol confusion problem and also helps the model generate results conforming to mathematical syntax. Compared with SOTA methods, the experimental results show that Expression recognition Rates(ExpRate) of our method are increased by 1.12%, 2.45% and 1.84% on CROHME 2014, 2016 and 2019 test set respectively. In addition, current works explore the effect of a single auxiliary task on HMER. We investigated the effect of multiple tasks on HMER. We performed many experiments and drew multiple valuable *** will publish all codes in the future. © 2023 ieee.

关键词： Character recognition

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Shadow processing.with color model adjustment and texture analysis 2

Shadow processing with color model adjustment and texture an...

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2nd International Workshop on Intelligent Systems, IWIS 2022

作者： Uddin, Md. Azhar Khanam, Tahmina Khan, Muhammad Ibrahim Deb, Kaushik Jo, Kang-Hyun Chittagong Bangladesh University of Ulsan Dept. of Ee and Information Systems Ulsan680-749 Korea Republic of

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

Object detection is a fundamental task in computer vision. In most cases, this motive is often corrupted by the shadows in an image. These scenarios consequence a great need of shadow processing. Along with this, the method of detecting and removing shadow is used to improve computer vision applications such as image segmentation, object recognition and tracking. The prime objective of this paper is to detect and remove shadow from an image by analyzing color models and background texture pattern. Initially, shadow boundaries are detected from a given foreground region by adjusting color models. Then the similarity between texture features of shadow and neighboring non-shadow region is measured. Finally, based on these similarities, texture pattern of non-shadow region is projected onto the shadow region to get a shadow free image. However, it is noteworthy that Local Binary pattern (LBP) is used here to measure the texture feature as it is simple and efficient. In addition, this simple methodology has achieved a good detection rate of 87.81 % and presented a high PSNR (22.41), SSIM (0.9432) value and low RMSE (3.48) value after shadow removal. © 2022 ieee.

关键词： Color

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Cultivated land segmentation in RGB remote sensing images: nonuniform regularization with kernel space and graph cut

Cultivated land segmentation in RGB remote sensing images: n...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Wu, Wangsheng College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

To improve the application efficiency of RGB remote sensing images in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and improved graph cut was proposed. Firstly, extracting texture and color features of remote sensing images using Local Binary pattern algorithm (LBP), Gabor filters, and RGB, HSV color space, respectively. Next, introducing a kernel method to map data from low-dimension to high-dimension, and construct a kernel space-based non-uniform regularization sparse representation model to classify and segment images in pixel level. Finally, an innovative graph cut algorithm is enhanced by incorporating a Gaussian distribution to redefine the penalty term for homogeneous regions and introducing a new color gradient measure to define the penalty term for boundaries. This approach effectively removes scatter and restricts the segmentation boundary. The average classification accuracy and average F1 score of the classifier proposed in this paper are about 2% and 3% higher than those of recent regularized subspace classifiers, respectively. Compared with the Graph cut algorithm, the proposed improved graph cut algorithm has an average mIoU improvement of about 9%. The average accuracy of the whole segmentation algorithm is 95.43%, and the average mIoU is 88.56%. Compared with the comparison algorithm, the proposed algorithm has higher segmentation accuracy, which proves that the proposed algorithm can adapt to the cultivated land segmentation scene of remote sensing images and is effective. © 2024 SPIE.

关键词： RGB color model

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Research on intelligent insect species identification based on computer vision 7

Research on intelligent insect species identification based ...

引用

7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Huang, Bin Nanchang Institute of Technology Jiangxi Nanchang330044 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

image-based insect species identification is a comprehensive application of computer vision technology, image processing.technology and pattern recognition technology to realize insect species identification. It is of great significance to find and identify crop insects in time and quickly, so as to take corresponding measures to prevent and control pests in time and reduce the losses caused by pests. In this paper, intelligent insect species identification based on computer vision is studied. For insect images, using feature fusion technology will obtain more comprehensive insect image features, which will greatly improve the recognition accuracy and improve the recognition efficiency of the system. In the learning process, firstly, a certain number of insect images are selected as training samples, and the feature vectors of insects are extracted as the input vectors of SVM(support vector machine) training, and the input vectors are learned to get the best classification model. The experimental results show that the average recognition rate of insects by feature fusion is 94.747%. The experimental results show that the research results of this paper provide theoretical and practical basis for intelligent insect species recognition technology based on computer vision. © 2023 SPIE. All rights reserved.

关键词： Support vector machines

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