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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Objects Segmentation in Augmented Reality Environment 5

Objects Segmentation in Augmented Reality Environment

引用

5th International conference on Computational Linguistics and Intelligent systems (COLINS)

作者： Sidenko, Ievgen Misiuk, Tetiana Kondratenko, Galyna Kondratenko, Yuriy Petro Mohyla Black Sea Natl Univ 68th Desantnykiv Str10 UA-54003 Mykolaiv Ukraine

The paper discusses objects (images) segmentation algorithms that are applicable in mobile applications with augmented reality. An example of image processing with virtual objects by different algorithms (the MeanShift algorithm, the GrabCut algorithm, the k- means algorithm) is considered. Different libraries, tools, and environments to implement segmentation algorithms were analyzed, such as Scikit-image, Pixellib, OpenCV, Point Cloud Library. The application was created for mobile devices running iOS 10 and higher. The GrabCut algorithm turned out to be the best algorithm for image processing. The processing result was the closest to the expected one. Although the algorithm has some errors. Despite the fact that the area that was contoured turned out to be the clearest and most complete in comparison with other algorithms, this area also includes areas of the image that do not belong to the objects under study.

关键词： Augmented reality mobile applications image segmentation virtual scene

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Deep Active Learning image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 23

Deep Active Learning Image Classification Algorithm Based on...

引用

Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Yuliang Pang Ying Liu Yu Hao Yanchao Gong Daxiang Li Zhijie Xu Center for Image and Information Processing Xi'an University of Posts and Telecommunications China Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing Xi'an University of Posts and Telecommunications China International Joint-Research Center for Wireless Communication and Information Processing University of Huddersfield China

ISBN: (纸本)9798400707674

The success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. This paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. This makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. This algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. The proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance.

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Advanced Digitization for Ancient Chinese Guqin Scores Based on Mask R-CNN Algorithm

Advanced Digitization for Ancient Chinese Guqin Scores Based...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wei, Bing Wang, Youdi Central Conservatory of Music Department of Music AI and Music Information Technology Beijing China Xi'An Conservatory of Music Department of Chinese Music and Instruments Xi'an China

ISBN: (纸本)9798350313154

This paper announced a novel digitization method for Chinese Guqin scores, to translate all Guqin scores from image version in books to digital version. As one of the global heritages, Guqin kept using its typical score system called Ch'in Tablature (means 'reduced notation', and pronounced as 'Jian Zi Pu' in Chinese pinyin) in past over one thousand years, which was created from using typical Chinese characters. However, translating Ch'in Tablature from images to digital version was still not solved. In this paper, the world's first Ch'in Tablature character dataset was created which includes over 12,000 characters, a novel method based on Mask R-CNN algorithm was announced as well. The tests with the initial dataset showed over 95% of success rate for recognizing Ch'in Tablature characters. The technical challenges and future works are also discussed in this paper. © 2023 IEEE.

关键词： Statistical tests

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Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms 6

Age Estimation Based on Graph Convolutional Networks and Mul...

引用

2023 IEEE 6th International conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Yang, Miaomiao Yao, Changwei Yan, Shijin Xi'an Jiaotong-Liverpool University Suzhou China Southern University of Science and Technology Shenzhen China Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350313444

Age estimation technology is a part of facial recognition and has been applied to identity authentication. This technology achieves the development and application of a juvenile anti-addiction system by authenticating users in the game. Convolutional Neural Network (CNN) and Transformer algorithms are widely used in this application scenario. However, these two models cannot flexibly extract and model features of faces with irregular shapes, and they are ineffective in capturing key information. Furthermore, the above methods will contain a lot of background information while extracting features, which will interfere with the model. In consequence, it is easy to extract redundant information from images. In this paper, a new modeling idea is proposed to solve this problem, which can flexibly model irregular objects. The Graph Convolutional Network (GCN) is used to extract features from irregular face images effectively, and multi-head attention mechanisms are added to avoid redundant features and capture key region information in the image. This model can effectively improve the accuracy of age estimation and reduce the MAE error value to about 3.64, which is better than the effect of today's age estimation model, to improve the accuracy of face recognition and identity authentication. © 2023 IEEE.

关键词： Age Estimation Graph Convolutional Network Juvenile Anti-addiction System Multi-head Attention Mechanisms

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Hierarchical DNN-Based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions 16

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

引用

16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Yu, Cheng-Hsuan Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781665464956

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both largescaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for oversegmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： image segmentation superpixel merging deep learning

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Sports Training 3D Human Motion Simulation and Video Analysis Based on Fish Swarm Algorithm

Sports Training 3D Human Motion Simulation and Video Analysi...

引用

2023 International conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Zhang, Mengjie Xi'an Academy of Fine Arts Sports Ministry Xi'an710065 China

ISBN: (纸本)9798350313314

Computer vision is an important branch of computer science and artificial intelligence. The main content of its research is how to use a variety of imaging systems instead of visual organs as signal input means, by the computer to complete the processing and interpretation of information instead of the brain. That is, from the traditional method mainly based on human eye observation to the human motion measurement method based on high precision video capture and analysis, from the experience-based training analysis method to the human motion analysis method based on human motion simulation change. With the maturity and popularization of motion capture technology, it has become a reality to acquire a large number of 3d motion data sets efficiently and quickly. Digital graphics and image technology are introduced into sports training, and a 3d human motion simulation and video analysis system for sports training is developed. It has become an important development direction of sports teaching to conduct standardized guidance and scientific teaching for sports participants through sports video. The ultimate research goal of computer vision is to enable computers to observe and understand the world through vision as humans do. Among them, the realization of key frame extraction, automatic recognition and classification of 3D human motion data is an important research content of human motion analysis, and an important premise and basis for the effective management and reuse of motion capture data. © 2023 IEEE.

关键词： Sports

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Towards Video Prediction

Towards Video Prediction

引用

IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Bradley Zylstra Weibo Gong University of Massachusetts Amherst MA USA

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

This paper introduces a novel approach to video prediction and object recognition based on treating image signals as dynamic system operators. We develop algorithms that extract invariant features from pixel patches to construct numerical matrices for image frame transitions. Our method diverges from conventional 2D signal processing by viewing images as operators rather than initial conditions. This perspective aligns more closely with biological visual systems and offers potential for efficient electronic implementations. We demonstrate the efficacy of our approach through experiments in mental rotation, affine transformations, and rotated MNIST digit recognition. Our results show that deformation invariance can be obtained without prior knowledge of the transformation. This work contributes to the broader goal of developing biologically plausible computer vision systems, with implications for video prediction, object recognition, and abstract concept formation for AI.

关键词： Visualization Computer vision Deformation Heuristic algorithms Signal processing algorithms Prediction algorithms Feature extraction

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Remote measurement of the clinical prescription of spectacle lenses 25

Remote measurement of the clinical prescription of spectacle...

引用

conference on Novel Optical systems, Methods, and Applications XXV

作者： Guan, Yuanxin Miller, Joseph M. Harvey, Erin M. Ashley, Sean Schwiegerling, Jim Univ Arizona James C Wyant Coll Opt Sci 1630 E Univ Blvd Tucson AZ 85721 USA Univ Arizona Dept Ophthalmol & Vis Sci 655 N Alvernon Way 204 Tucson AZ 85711 USA

ISBN: (纸本)9781510654174;9781510654167

The SpectRx system has been developed to measure sphero-cylindrical spectacle lens power as an alternative to clinical lensmeters. This work was inspired by the ongoing global pandemic, which limited physical access to eye care facilities for regular eye exams The SpectRx system aims to bypass this limitation by providing at-home prescription measurements. The power and orientation of the spectacle lenses are obtained by the use of readily available objects such as a cell phone camera, a displayed or printed target, and a fixed-dimension magnetic stripe card. The magnification of the lenses can be calculated by examining the image captured through the lens of the target at a fixed distance. The magnification may be spatially varying due to the cylinder component of the lens. processing the pictures captured with a cell phone camera is done automatically with standard image processing algorithms. The processed images, in turn, are used to calculate a clinical prescription, i.e., Sph/CylxAxis. The SpectRx may expand access to quality eye care in not only the current pandemic situation but also in locations where eye care may not be easily accessible, such as some rural or remote areas. The image processing and clinical prescription calculation are discussed here.

关键词： Spectacle lenses power measurement telemedicine image processing

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The Estimation of Inertial Measurement Units Accuracy Using Digital image processing algorithms

The Estimation of Inertial Measurement Units Accuracy Using ...

引用

2022 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2022

作者： Shmatko, E.V. Poroykov, A. Yu. National Research University Moscow Power Engineering Institute Department of Physics Named after V.A. Fabrikant Moscow Russia

ISBN: (纸本)9781665462402

The paper describes an approach for estimation of inertial measurement unit using image processing algorithm to determine the position of an object in space. The results of measurements of the angular velocities of a freely rotating platform using the inertial and photogrammetry methods are presented. The inertial method data contains the measurement results of a three-axis solid gyroscope. The photogrammetry method determines the position of the platform using digital video cameras based on images of fiducial markers installed on it. A comparison of the measurement results is presented, conclusions about the accuracy of the tested gyroscope are drawn. © 2022 IEEE.

关键词： Angular velocity

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Wavefront coded image restoration algorithm based on compressed sensing for off-axis mirror system 7

Wavefront coded image restoration algorithm based on compres...

引用

7th Asia Pacific conference on Optics Manufacture (APCOM)

作者： Yi, Huang Jun, Chang Li Dongmei Cao Jiajing Shan, Du Beijing Inst Technol Beijing 100081 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

By combining optical systems and image processing, wavefront coding can greatly expand the depth of focus and depth of field of optical systems. It has been widely used in iris detection, high-power microscopic objective lens, infrared optical system athermalized, and so on. At present, the image restoration algorithms commonly used in wavefront coding are based on deconvolution, Wiener filtering, and so on. Although these algorithms can achieve an excellent image restoration effect, they will also bring boundary ringing effects and artifacts to the image. When the image is disturbed by strong noise, the restoration effect will also be seriously affected. To solve these problems, a wavefront coded image restoration algorithm based on compressed sensing is proposed in this paper. The strong data reconstruction ability of the compressed sensing restoration algorithm is used to restore the encoded image obtained by the wavefront coding system. This method can effectively suppress noise and reconstruct the image without artifact and boundary ringing effect. Through the comparison of simulation results, the effectiveness of the proposed method is verified.

关键词： wavefront coding compressed sensing artifact

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