3d Object Understanding From Rgb D Data
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3d Object Understanding From Rgb D Data
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Author : Jie Feng
language : en
Publisher:
Release Date : 2017
3d Object Understanding From Rgb D Data written by Jie Feng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Instead of solely depending on color images, we combine both color and depth images to achieve significantly higher performance for object segmentation. We use large collection of 3D object models to provide high quality training data and retrieve visually similar 3D CAD models from low-quality captured depth images which enables knowledge transfer from database objects to target object in an observed scene. By using content-based 3D shape retrieval, we also significantly improve pose estimation via similar proxy models without the need to create the exact 3D model as a reference.
Rgb D Image Analysis And Processing
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Author : Paul L. Rosin
language : en
Publisher: Springer Nature
Release Date : 2019-10-26
Rgb D Image Analysis And Processing written by Paul L. Rosin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-26 with Computers categories.
This book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The topics covered include: data acquisition, data quality assessment, filling holes, 3D reconstruction, SLAM, multiple depth camera systems, segmentation, object detection, salience detection, pose estimation, geometric modelling, fall detection, autonomous driving, motor rehabilitation therapy, people counting and cognitive service robots. The availability of cheap RGB-D sensors has led to an explosion over the last five years in the capture and application of colour plus depth data. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data. There remain many technical challenges, and RGB-D image processing is an ongoing research area. This book covers the full state of the art, and consists of a series of chapters by internationally renowned experts in the field. Each chapter is written so as to provide a detailed overview of that topic. RGB-D Image Analysis and Processing will enable both students and professional developers alike to quickly get up to speed with contemporary techniques, and apply RGB-D imaging in their own projects.
Computer Vision Eccv 2014
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Author : David Fleet
language : en
Publisher: Springer
Release Date : 2014-09-22
Computer Vision Eccv 2014 written by David Fleet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-22 with Computers categories.
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Computer Vision Eccv 2014
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Author : David Fleet
language : en
Publisher: Springer
Release Date : 2014-08-14
Computer Vision Eccv 2014 written by David Fleet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-14 with Computers categories.
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Computer Vision And Machine Learning With Rgb D Sensors
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Author : Ling Shao
language : en
Publisher: Springer
Release Date : 2014-07-14
Computer Vision And Machine Learning With Rgb D Sensors written by Ling Shao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-14 with Computers categories.
This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.
Representations And Techniques For 3d Object Recognition And Scene Interpretation
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Author : Derek Hoiem
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011
Representations And Techniques For 3d Object Recognition And Scene Interpretation written by Derek Hoiem and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions
Object Recognition And Semantic Scene Labeling For Rgb D Data
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Author : Kevin Kar Wai Lai
language : en
Publisher:
Release Date : 2013
Object Recognition And Semantic Scene Labeling For Rgb D Data written by Kevin Kar Wai Lai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Machine learning categories.
The availability of RGB-D (Kinect-like) cameras has led to an explosive growth of research on robot perception. RGB-D cameras provide high resolution (640 x 480) synchronized videos of both color (RGB) and depth (D) at 30 frames per second. This dissertation demonstrates the thesis that combining of RGB and depth at high frame rates is helpful for various recognition tasks including object recognition, object detection, and semantic scene labeling. We present the RGB-D Object Dataset, a large dataset of 250,000 RGB-D images of 300 objects in 51 categories, and 22 RGB-D videos of objects in indoor home and office environments. We introduce algorithms for object recognition in RGB-D images that perform category, instance, and pose recognition in a scalable manner. We also present HMP3D, an unsupervised feature learning approach for 3D point cloud data, and demonstrate that HMP3D can be used to learn hierarchies of features from different attributes including color, gradient, shape, and surface normal orientation. Finally, we present a scene labeling approach for scenes constructed from RGB-D videos. The approach uses features learned from both individual RGB-D images and 3D point clouds constructed from entire video sequences. Through these applications, this thesis demonstrates the importance of designing new features and algorithms that specifically utilize the advantages of RGB-D cameras over traditional cameras and range sensors.
Constructing High Quality 3d Object Models Using Rgb D Cameras
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Author : Mayoore Selvarasa Jaiswal
language : en
Publisher:
Release Date : 2018
Constructing High Quality 3d Object Models Using Rgb D Cameras written by Mayoore Selvarasa Jaiswal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
With the introduction of economical depth cameras, computer vision research has made a huge leap forward in 3D reconstruction and understanding. However, the quality of the depth images are limited: 1) depth images contain holes and random noise due to the characteristics of the cameras and the physical world, and 2) depth images have a lower spatial resolution. These challenges make 3D object reconstruction with a limited number of RGB-D frames a difficult task. In this dissertation, we propose a method to construct 3D object models with a limited number of RGB and depth frames. We developed a complete 3D object model construction process with automatic object segmentation, pairwise registration, global alignment, model denoising, and texturing, and studied the effects of these functions on the constructed 3D object models. We also developed a process for objective performance evaluation of the constructed 3D object models. High-quality depth images are paramount to create quality 3D object models. Many depth denoising methods blur object boundaries. Partial point clouds created from depth images denoised by these methods have artifacts that make them unsuitable for 3D object modeling. We propose a method to find the clean depth edge image using the noisy depth image and a color image, then use this clean depth edge image with the proposed edge and color aware adaptive trilateral filter to obtain denoised depth images. Finally, we propose to apply depth denoising and depth super-resolution in the 3D object modeling process. We preprocess depth images with the proposed depth denoising method and a state-of-the-art depth super-resolution method, then experiment the use of these preprocessed depth images under various conditions in the 3D object modeling process. We show that depth denoising and super-resolution can improve the quality of the 3D object models.
Experimental Robotics
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Author : Jaydev P. Desai
language : en
Publisher: Springer
Release Date : 2013-07-09
Experimental Robotics written by Jaydev P. Desai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-09 with Technology & Engineering categories.
The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings, which are organized, in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 13the ISER held in Québec City, Canada, at the Fairmont Le Château Frontenac, on June 18-21, 2012. This present thirteenth edition of Experimental Robotics edited by Jaydev P. Desai, Gregory Dudek, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-centered robotics.
3d Imaging Analysis And Applications
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Author : Yonghuai Liu
language : en
Publisher: Springer Nature
Release Date : 2020-09-11
3d Imaging Analysis And Applications written by Yonghuai Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
This textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops. Overall, the book covers three main areas: ● 3D imaging, including passive 3D imaging, active triangulation 3D imaging, active time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data representation and visualisation; ● 3D shape analysis, including local descriptors, registration, matching, 3D morphable models, and deep learning on 3D datasets; and ● 3D applications, including 3D face recognition, cultural heritage and 3D phenotyping of plants. 3D computer vision is a rapidly advancing area in computer science. There are many real-world applications that demand high-performance 3D imaging and analysis and, as a result, many new techniques and commercial products have been developed. However, many challenges remain on how to analyse the captured data in a way that is sufficiently fast, robust and accurate for the application. Such challenges include metrology, semantic segmentation, classification and recognition. Thus, 3D imaging, analysis and their applications remain a highly-active research field that will continue to attract intensive attention from the research community with the ultimate goal of fully automating the 3D data capture, analysis and inference pipeline.