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Study on Multi-Scene VR Service Mode of Library in 5G Environment 3

Study on Multi-Scene VR Service Mode of Library in 5G Enviro...

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3rd IEEE International conference on Information Systems and Computer Aided Education, ICISCAE 2020

作者： Xu, Jinping Song, Wenqing Library Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781728183039

In order to realize the visual reconstruction and feature extraction of library multi-scene VR service mode, the library multi-scene 3D reconstruction and service mode recognition under 5G environment are required. A library multi-scene VR service reconstruction technology based on virtual reality VR visual feature analysis and service mode simulation is proposed. Firstly, RFID radio frequency tag identification technology is used to collect images of service reliability areas of libraries in 5G environment, and VR visual information simulation technology is used to enhance the collected images. Then, VR technology is used to track and render the contour and texture of the image edge to realize the visual feature reconstruction, service pattern recognition and three-dimensional scene reconstruction of VR service patterns in multiple scenes of the library. Finally, an actual library multi-scene VR service reconstruction simulation experiment is carried out. The results show that the method can realize the integrity detection of library multi-scene features in 5G environment, and the output image quality is high. The library multi-scene VR service mode in 5G environment is improved. © 2020 IEEE.

关键词： image reconstruction

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Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth image analysis 3rd

Axial Data Modeling with Collapsed Nonparametric Watson Mixt...

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3rd Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Yang, Lin Li, Yuhang Fan, Wentao Huaqiao Univ Dept Comp Sci & Technol Xiamen Fujian Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen Peoples R China Huaqiao Univ Key Lab Comp Vis & Machine Learning Fujian Prov Univ Xiamen Peoples R China

ISBN: (纸本)9783030606381;9783030606398

Recently, axial data (i.e. the observations are axes of direction) have been involved with various fields ranging from blind speech separation to gene expression data clustering. In this paper, axial data modeling is performed by proposing a nonparametric infinite Watson mixture model which is constructed in a collapsed space (denoted by Co-InWMM) where the mixing coefficients are integrated out. Then, an effective collapsed variational Bayes (CVB) inference method is theoretically developed to learn the Co-InWMM with closed-from solutions. The proposed Co-InWMM with CVB inference for modeling axial data is validated through both synthetical data sets and a challenging application regarding depth image analysis.

关键词： Axial data modeling Mixture model Watson distributions Collapsed variational Bayes Depth image analysis

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3D Reconstruction of Archaeological Pottery from Its Point Cloud 9th

3D Reconstruction of Archaeological Pottery from Its Point C...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Sakpere, Wilson Univ Bologna Bologna Italy

ISBN: (纸本)9783030313326;9783030313319

This paper presents a 3D reconstruction process for an archaeological pottery. Because of the shape of the pottery and its fracture, applying existing methods of registration did not yield the desired result. Thus, to improve the registration output and accuracy, a custom acquisition setup was used to acquire the point cloud data after it was calibrated. The setup was also cost effective, a consideration that is useful for archaeologists. Furthermore, the acquired point clouds were pre-processed to remove noise and outliers. Key-points were extracted from the point clouds using Principal Component analysis (PCA) and descriptors were computed adapting the ColorSHOT descriptor. With correspondences between the key-points of point clouds established, a pairwise alignment of the point clouds was done. Finally, the global registration was done on all the point clouds, with ICP used to refine the alignment for all the point clouds. The peculiarity of the approach of this paper is in adapting and adjusting parameters as required due to the peculiar nature of the data acquired. This improved the robustness of this approach, evident in the final registration output.

关键词： 3D reconstruction Archaeological pottery Potsherd Point cloud registration

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An Efficient Binary Descriptor to Describe Retinal Bifurcation Point for image Registration 9th

An Efficient Binary Descriptor to Describe Retinal Bifurcati...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Islam, Sarder Tazul Saha, Sajib Rahaman, G. M. Atiqur Dutta, Deep Kanagasingam, Yogesan Khulna Univ Computat Color & Spectral Image Anal Lab Comp Sci & Engn Discipline Khulna Bangladesh CSIRO Australian E Hlth Res Ctr Perth WA Australia

ISBN: (纸本)9783030313326;9783030313319

Bifurcation points are typically considered as landmark points for retinal image registration. Robust detection, description and accurate matching of landmark points between images are crucial for successful registration of image pairs. This paper introduces a novel descriptor named Binary Descriptor for Retinal Bifurcation Point (BDRBP), so that bifurcation point can be described and matched more accurately. BDRBP uses four patterns that are reminiscent of Haar basis function. It relies on pixel intensity difference among groups of pixels within a patch centering on the bifurcation point to form a binary string. This binary string is the descriptor. Experiments are conducted on publicly available retinal image registration dataset named FIRE. The proposed descriptor has been compared with the state-of-the art Li Chen et al.'s method for bifurcation point description. Experiments show that bifurcation points can be described and matched with an accuracy of 86-90% with BDRBP, whereas, for Li Chen et al.'s method the accuracy is 43-78%.

关键词： Bifurcation point Binary descriptor Haar feature Hamming distance image registration

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606350

The proceedings contain 158 papers. The special focus in this conference is on pattern recognition and Computer Vision. The topics include: Underwater image Processing by an Adversarial Network with Feedback Control;inception Parallel Attention Network for Small Object Detection in Remote Sensing images;hyperspectral image Denoising Based on Graph-Structured Low Rank and Non-local Constraint;multi-human Parsing with Pose and Boundary Guidance;m2E-Net: Multiscale Morphological Enhancement Network for Retinal Vessel Segmentation;DUDA: Deep Unsupervised Domain Adaptation Learning for Multi-sequence Cardiac MR image Segmentation;learning from Rankings with Multi-level Features for No-Reference image Quality Assessment;reversible Data Hiding Based on Prediction-Error-Ordering;aggregating Spatio-temporal Context for Video Object Segmentation;Position and Orientation Detection of Insulators in Arbitrary Direction Based on YOLOv3;R-PFN: Towards Precise Object Detection by Recurrent Pyramidal Feature Fusion;image Super-Resolution Based on Non-local Convolutional Neural Network;VH3D-LSFM: Video-Based Human 3D Pose Estimation with Long-Term and Short-Term Pose Fusion Mechanism;automatic Tooth Segmentation and 3D Reconstruction from Panoramic and Lateral Radiographs;unregistered Hyperspectral and Multispectral image Fusion with Synchronous Nonnegative Matrix Factorization;Cloud Detection Algorithm Using Advanced Fully Convolutional Neural Networks in FY3D-MERSI imagery;multi-layer Pointpillars: Multi-layer Feature Abstraction for Object Detection from Point Cloud;building Detection via Complementary Convolutional Features of Remote Sensing images;hyperspectral image Super-Resolution via Self-projected Smooth Prior;3D Point Cloud Segmentation for Complex Structure Based on PointSIFT;completely Blind image Quality Assessment with Visual Saliency Modulated Multi-feature Collaboration;blood Flow Velocity Detection of Nailfold Microcirculation Based on Spatiotemporal analysis;blind

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606329

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

3rd Chinese Conference on Pattern Recognition and Computer V...

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3rd Chinese conference on pattern recognition and Computer Vision, PRCV 2020

ISBN: (纸本)9783030606381

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A multi-scale context encoder for high quality restoration of facial images 3

A multi-scale context encoder for high quality restoration o...

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3rd International conference on Multimedia analysis and pattern recognition, MAPR 2020

作者： Do, Trung Dung Nguyen, Quoc Khanh Nguyen, Viet Hung Le Quy Don Technical University Faculty of Information Technology Hanoi Viet Nam

ISBN: (纸本)9781728165554

In recent years, with the growth of data, storage and computing power many challenging problems can be solved. One of the most challenging and interesting tasks is to recover data in pixel level of image without using any additional information. Context encoder, a combination of auto-encoder and GAN, uses an unsupervised visual feature learning algorithm to predict missing pixel data in images. This paper presents a novel method to extract features in context encoder to restore high quality facial image. Instead of computing features from a single scale input image, the proposed method utilizes features from multiscale input images to create features in the encoder step. The features are then fed into the decoder to create image with the needed information after feature aggregation in different ways. The restored images after using decoder and the original ones are then provided to a discriminator to make the network creating a looked-real image. Experimental results on some public data sets such as CELEBA-HQ and LFW show that the proposed method with features in multi-scale achieves a promising result compared with the original one. © 2020 IEEE.

关键词： image reconstruction

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Class Reconstruction Driven Adversarial Domain Adaptation for Hyperspectral image Classification 9th

Class Reconstruction Driven Adversarial Domain Adaptation fo...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Pande, Shivam Banerjee, Biplab Pizurica, Aleksandra Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India Univ Ghent Dept Telecommun & Informat Proc Ghent Belgium

ISBN: (纸本)9783030313326;9783030313319

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach.

关键词： Domain adaptation Adversarial training Hyperspectral images

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Study and Automatic Translation of Toki Pona 11th

Study and Automatic Translation of Toki Pona

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Baggetto, Pablo López, Damián Larriba, Antonio M. valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence Camí de Vera S/N Edificio 3Q Valencia46022 Spain vrAIn: Valencian Research Institute for Artificial Intelligence Universitat Politècnica de València Valencia46022 Spain

ISBN: (纸本)9783031366154

In this work, we explore the use of Neural Machine Translation models for Toki Pona, a small, low resourced, and minimalistic language. We tackle the problem by employing the transformer model and transfer learning. Despite the challenges posed by the language’s limited resources and the scarcity of available data, we demonstrate that a high-accuracy machine translator can be developed for Toki Pona using these advanced techniques. Through transfer learning from existing models, we were able to adapt to the unique characteristics of Toki Pona and overcome the challenges of limited data. Our results show the potential for applying these methods to other under-resourced languages, providing a valuable tool for increasing accessibility and communication in these communities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural machine translation

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