检索结果-内蒙古大学图书馆

24th ACM multimedia Conference, MM 2016

作者： Wang, Meng Fang, Yi NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates Dept. of ECE NYU Tandon School of Engineering United States

ISBN: (纸本)9781450336031

Finding the accurate corresponded landmarks from a collection of shape instances plays critical role in constructing active shape models (ASMs). We have developed a global consistent shape correspondence paradigm for efficient and effective active shape models to address challenging issues in statistical shape modelling. Specifically, in this paper, we developed techniques to perform a fast multiple shape matching to identify global consistent shape correspondence from a set of training shape instances via efficient low-rank recovery optimization. High quality ASMs can then be constructed based on the identified corresponded points. The entire process is unsupervised without manual annotation as well as free of selection of anatomically significant point. Experimental results on mobile hand image data demonstrate the superior performance of our proposed method over other state-of-the-art techniques like MDL in constructing active shape models. © 2016 ACM.

关键词： 3d shape correspondence non-rigid shape matching 3d shape signature

来源：评论

学校读者我要写书评

暂无评论

Image Dehazing With Contextualized Attentive U-NET

Image Dehazing With Contextualized Attentive U-NET

引用

IEEE International Conference on Image Processing

作者： Yean-Wei Lee Lai-Kuan Wong John See Visual Processing Lab Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Haze, which occurs due to the accumulation of fine dust or smoke particles in the atmosphere, degrades outdoor imaging, resulting in reduced attractiveness of outdoor photography and the effectiveness of vision-based systems. In this paper, we present an end-to-end convolutional neural network for image dehazing. Our proposed U-Net based architecture employs Squeeze-and-Excitation (SE) blocks at the skip connections to enforce channel-wise attention and parallelized dilated convolution blocks at the bottleneck to capture both local and global context, resulting in a richer representation of the image features. Experimental results demonstrate the effectiveness of the proposed method in achieving state-of-the-art performance on the benchmark SOTS dataset.

关键词： Convolution Training Atmospheric modeling visualization Feature extraction Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Smartphone Application Designed to Detect Obstacles for Pedestrians’ Safety 6th

A Smartphone Application Designed to Detect Obstacles for Pe...

引用

6th EAI International Conference on Science and Technologies for Smart Cities, SmartCity 2020

作者： Thoma, Marios Theodosiou, Zenonas Partaourides, Harris Tylliros, Charalambos Antoniades, Demetris Lanitis, Andreas Research Centre on Interactive Media Smart Systems and Emerging Technologies - RISE Nicosia Cyprus Visual Media Computing Research Lab Department of Multimedia and Graphic Arts Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783030760625

Encouraging people to walk rather than using other means of transportation is an important factor towards personal health and environmental sustainability. However, given the large number of pedestrian accidents recorded every year, the need for safe urban environments is increasing. Taking advantage of the potential of citizen-science for crowdsourcing data and creating awareness, we developed a smartphone application for enhancing the safety of pedestrians while walking in cities. Using the application, citizens will monitor the urban sidewalks and update a crowdsourcing platform with the detected barriers and damages that hinder safe walking, along with their location on a city map. To help users assign the correct type of obstacle, and authorities to assess the urgency, a Convolutional Neural Network (CNN) model for barrier and damage recognition is embedded in the application. The results of a user evaluation, based on a group of volunteers who used the application in real conditions, demonstrate the potential of using the application in conjunction with a smart city framework. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Robust Reconstruction of Interior Building Structures with Multiple Rooms under Clutter and Occlusions

Robust Reconstruction of Interior Building Structures with M...

引用

IEEE International Conference on Computer-Aided Design and Computer Graphics

作者： Claudio Mura Oliver Mattausch Alberto Jaspe Villanueva Enrico Gobbetti Renato Pajarola Visualization and MultiMedia Lab University of Zürich Switzerland Visual Computing Group Italy

We present a robust approach for reconstructing the architectural structure of complex indoor environments given a set of cluttered input scans. Our method first uses an efficient occlusion-aware process to extract planar patches as candidate walls, separating them from clutter and coping with missing data. Using a diffusion process to further increase its robustness, our algorithm is able to reconstruct a clean architectural model from the candidate walls. To our knowledge, this is the first indoor reconstruction method which goes beyond a binary classification and automatically recognizes different rooms as separate components. We demonstrate the validity of our approach by testing it on both synthetic models and real-world 3D scans of indoor environments.

关键词： Three-dimensional displays Robustness Buildings Solid modeling Data models Clutter Shape

来源：评论

学校读者我要写书评

暂无评论

Robust enhancement of depth images from Kinect sensor

Robust enhancement of depth images from Kinect sensor

引用

IEEE Annual International Symposium Virtual Reality

作者： A B M Tariqul Islam Christian Scheel Renato Pajarola Oliver Staadt Visual Computing Lab University of Rostock Germany Visualization and MultiMedia Lab University of Zürich Switzerland

We propose a new method to fill missing or invalid values in depth images generated from the Kinect depth sensor. To fill the missing depth values, we use a robust least median of squares (LMedS) approach. We apply our method for telepresence environments, where Kinects are used very often for reconstructing the captured scene in 3D. We introduce a modified 1D LMedS approach for efficient traversal of consecutive image frames. Our approach solves the unstable nature of depth values in static scenes that is perceived as flickering. We obtain very good result both for static and moving objects inside a scene.

关键词： Three-dimensional displays Cameras Electronic mail Color Noise Robustness Indexes

来源：评论

学校读者我要写书评

暂无评论

Virtual-Reality Based Crisis Management Training for Teachers: An Overview of the VRTEACHER Project 23

Virtual-Reality Based Crisis Management Training for Teacher...

引用

23rd International Symposium on Computers in Education, SIIE 2021

作者： Stavroulia, Kalliopi Evangelia Aedo, Ignacio Diaz, Paloma Lanitis, Andreas Cyprus University of Technology Department of Multimedia and Graphic Arts Limassol Cyprus Universidad Carlos III de Madrid Computer Science Department Madrid Spain Cyprus University of Technology and CYENS Centre of Excellence Visual Media Computing Lab Department of Multimedia and Graphic Arts Limassol Cyprus

ISBN: (纸本)9781665440240

The coronavirus pandemic has created an educational crisis without precedent, posed new challenges, and highlighted the need for digital skills empowerment among teachers along with the use of technology-based approaches to support the delivery of education and ensure the future of learning. As a return to normality is far from over, the implementation of novel training approaches for teacher training is more imperative than ever. In this context, this paper presents the VRTEACHER project that aims to promote the use of digital technologies and innovative and open pedagogies as part of teachers' training methodology. The vision of the project is to empower teacher's skills (such as empathy, perspective-taking, self-efficacy, adaptability, etc.) and readiness related to class management in crisis situations, through Virtual Reality (VR) immersive and experiential training approaches that reflect real-life scenarios and situations. The aim and challenge of the project are not just to adapt the training provided to the digital era and connect it with new technologies but to build a holistic new approach to teacher training through the use of VR. © 2021 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Model-based generation of realistic 3D full body avatars from uncalibrated multi-view photographs

IFIP Advances in Information and Communication Technology

引用

IFIP Advances in Information and Communication Technology 2014年 436卷 354-363页

作者： Michael, Nicholas Lanitis, Andreas Visual Media Computing Lab Dept. of Multimedia and Graphic Arts Cyprus University of Technology Lemesos3036 Cyprus

In today’s world of rapid technological advancement, we find an increasing demand for low-cost systems that are capable of fast and easy generation of realistic avatars for use in Virtual Reality (VR) applications. For example, avatars can enhance the immersion experience of users in video games and facilitate education in virtual classrooms. Therefore, we present here a novel model based technique that is capable of real-time generation of personalized full-body 3D avatars from orthogonal photographs. The proposed method utilizes a statistical model of human 3D shape and a multi-view statistical 2D shape model of its corresponding silhouettes. Our technique is automatic, requiring minimal user intervention, and does not need a calibrated camera. Each component of our proposed technique is extensively evaluated and validated. © IFIP International Federation for Information Processing 2014.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation

3D-MetaConNet: Meta-learning for 3D Shape Classification and...

引用

International Conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Hao Huang Xiang Li Lingjing Wang Yi Fang NYU Multimedia and Visual Computing Lab USA New York University Abu Dhabi UAE

ISBN: (纸本)9781665426893

Supervised learning on 3D shapes are extensively studied by prior literature, among which PointNet [29] and its variants PointNet++ [31] are representatives. However, these methods tackle 3D shape learning problems by training from scratch using a fixed learning algorithm over large amounts of labeled data, potentially challenged by data and computation bottlenecks. In the paper, we design a novel model, under the framework of meta-learning, to learn 3D shape representation. By training over multiple 3D tasks, each of which is defined as a supervised learning problem, our method can fast adapt to unseen tasks containing limited labeled data. Specifically, our model consists of a 3Dmeta-learner and a task-oriented 3D-learner, where the 3D-meta-learner produces parameter initialization for the 3D-learner after being trained over different tasks. With adaptively initialized parameters, the 3D-learner can be tuned rapidly in a few steps to achieve good performance on novel tasks with a small amount of training data. To further facilitate discriminative shape feature learning, we introduce a novel task-aware feature adaptation module under a contrastive learning scheme, in which all shapes in each task are considered as a whole and task-oriented compact features are learned. Therefore, we dub our model as 3DMetaConNet. Experiments on three public 3D datasets for few-shot shape classification and segmentation demonstrate that our method can learn compact and discriminative 3D shape features efficiently and robustly in a fast adaptation manner. Our method particularly outperforms the methods without a meta-learning framework and is also superior to existing meta-learning approaches.

关键词： Training Representation learning Adaptation models Solid modeling Three-dimensional displays Shape Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Geometry-Aware Segmentation of Remote Sensing Images via implicit height estimation

arXiv

引用

arXiv 2020年

作者： Li, Xiang Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon Abu DhabiUAE United States

Deep learning-based methods, especially deep convolutional neural networks (CNNs), have made significant breakthroughs in the field of remote sensing and greatly advanced the performance of the semantic segmentation of remote sensing images. Recent studies have shown the benefits of using additional elevation data (e.g., DSM or nDSM) for enhancing the performance of the semantic segmentation of aerial images. However, previous methods mostly adopt 3D elevation information as additional inputs. While in many real-world applications, one does not have the corresponding DSM information at hand and the spatial resolution of acquired DSM images usually do not match the aerial images. To alleviate this data constraint and also take the advantage of 3D elevation information, in this paper, we propose a geometry-aware segmentation model that achieves accurate semantic segmentation of aerial images via implicit height estimation. Instead of using a single-stream encoder-decoder network for semantic labeling, we design a separate decoder branch to predict the height map and use the DSM images as side supervision to train this newly designed decoder branch. In this way, our model does not require DSM as model input and still benefits from the helpful 3D geometric information during training. With the newly designed decoder branch, our model can distill the 3D geometric features from 2D appearance features under the supervision of ground truth DSM images. Moreover, we develop a new geometry-aware convolution module that fuses the 3D geometric features from the height decoder branch and the 2D contextual features from the semantic segmentation branch. The fused feature embeddings can produce geometry-aware segmentation maps with enhanced performance. Our model is trained with DSM images as side supervision, while in the inference stage, it does not require DSM data and can directly predict the semantic labels in an end-to-end fashion. Experiments on ISPRS Vaihingen and Potsdam d

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Unsupervised 3D reconstruction from a single image via adversarial learning

arXiv

引用

arXiv 2017年

作者： Wang, Lingjing Fang, Yi NYU Multimedia and Visual Computing Lab Courant Institute of Mathematical Science NYU Tandon School of Engineering United States NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates

Recent advancements in deep learning opened new opportunities for learning a high-quality 3D model from a single 2D image given sufficient training on large-scale data sets. However, the significant imbalance between available amount of images and 3D models, and the limited availability of labeled 2D image data (i.e. manually annotated pairs between images and their corresponding 3D models), severely impacts the training of most supervised deep learning methods in practice. In this paper, driven by a novel design of adversarial networks, we have developed an unsupervised learning paradigm to reconstruct 3D models from a single 2D image, which is free of manually annotated pairwise input image and its associated 3D model. Particularly, the paradigm begins with training an adaption network via autoencoder with adversarial loss, which embeds unpaired 2D synthesized image domain with real world image domain to a shared latent vector space. Then, we jointly train a 3D deconvolutional network to transform the latent vector space to the 3D object space together with the embedding process. Our experiments verify our network's robust and superior performance in handling 3D volumetric object generation from a single 2D image. Copyright © 2017, The Authors. All rights reserved.

关键词： Vector spaces

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：