Author: Zhuo Deng Publisher: ISBN: Category : Languages : en Pages : 113
Book Description
With the advent of Microsoft Kinect, the landscape of various vision-related tasks has been changed. Firstly, using an active infrared structured light sensor, the Kinect can provide directly the depth information that is hard to infer from traditional RGB images. Secondly, RGB and depth information are generated synchronously and can be easily aligned, which makes their direct integration possible. In this thesis, I propose several algorithms or systems that focus on how to integrate depth information with traditional visual appearances for addressing different computer vision applications. Those applications cover both low level (image segmentation, class agnostic object proposals) and high level (object detection, semantic segmentation) computer vision tasks. To firstly understand whether and how depth information is helpful for improving computer vision performances, I start research on the image segmentation field, which is a fundamental problem and has been studied extensively in natural color images. We propose an unsupervised segmentation algorithm that is carefully crafted to balance the contribution of color and depth features in RGB-D images. The segmentation problem is then formulated as solving the Maximum Weight Independence Set (MWIS) problem. Given superpixels obtained from different layers of a hierarchical segmentation, the saliency of each superpixel is estimated based on balanced combination of features originating from depth, gray level intensity, and texture information. We evaluate the segmentation quality based on five standard measures on the commonly used NYU-v2 RGB-Depth dataset. A surprising message indicated from experiments is that unsupervised image segmentation of RGB-D images yields comparable results to supervised segmentation. In image segmentation, an image is partitioned into several groups of pixels (or super-pixels). We take one step further to investigate on the problem of assigning class labels to every pixel, i.e., semantic scene segmentation. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from Kinect in a principled manner. The final labeling result must satisfy all mutex constraints, which allows us to eliminate configurations that violate common sense physics laws like placing a floor above a night stand. Three classes of mutex constraints are proposed: global object co-occurrence constraint, relative height relationship constraint, and local support relationship constraint. Segments obtained from image segmentation can be either too fine or too coarse. A full object region not only conveys global features but also arguably enriches contextual features as confusing background is separated. We propose a novel unsupervised framework for automatically generating bottom up class independent object candidates for detection and recognition in cluttered indoor environments. Utilizing raw depth map, we propose a novel plane segmentation algorithm for dividing an indoor scene into predominant planar regions and non-planar regions. Based on this partition, we are able to effectively predict object locations and their spatial extensions. Our approach automatically generates object proposals considering five different aspects: Non-planar Regions (NPR), Planar Regions (PR), Detected Planes (DP), Merged Detected Planes (MDP) and Hierarchical Clustering (HC) of 3D point clouds. Object region proposals include both bounding boxes and instance segments. Although 2D computer vision tasks can roughly identify where objects are placed on image planes, their true locations and poses in the physical 3D world are difficult to determine due to multiple factors such as occlusions and the uncertainty arising from perspective projections. However, it is very natural for human beings to understand how far objects are from viewers, object poses and their full extents from still images. These kind of features are extremely desirable for many applications such as robotics navigation, grasp estimation, and Augmented Reality (AR) etc. In order to fill the gap, we addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2D representation framework, and directly relate 2D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects' 3D locations, physical sizes, and orientations in indoor scenes.
Author: David Fleet Publisher: Springer ISBN: 9783319105833 Category : Computers Languages : en Pages : 632
Book Description
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.
Author: Paul L. Rosin Publisher: Springer Nature ISBN: 3030286037 Category : Computers Languages : en Pages : 524
Book Description
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.
Author: David Fleet Publisher: Springer ISBN: 331910599X Category : Computers Languages : en Pages : 855
Book Description
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.
Author: Jaydev P. Desai Publisher: Springer ISBN: 3319000659 Category : Technology & Engineering Languages : en Pages : 966
Book Description
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.
Author: Andrea Fossati Publisher: Springer Science & Business Media ISBN: 1447146395 Category : Computers Languages : en Pages : 220
Book Description
The potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications. This authoritative text reviews the scope and impact of this rapidly growing field, describing the most promising Kinect-based research activities, discussing significant current challenges, and showcasing exciting applications. Features: presents contributions from an international selection of preeminent authorities in their fields, from both academic and corporate research; addresses the classic problem of multi-view geometry of how to correlate images from different viewpoints to simultaneously estimate camera poses and world points; examines human pose estimation using video-rate depth images for gaming, motion capture, 3D human body scans, and hand pose recognition for sign language parsing; provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton.
Author: Derek Hoiem Publisher: Morgan & Claypool Publishers ISBN: 1608457281 Category : Computers Languages : en Pages : 172
Book Description
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
Author: Kevin Kar Wai Lai Publisher: ISBN: Category : Machine learning Languages : en Pages : 154
Book Description
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.
Author: Rajendra Prasad Mahapatra Publisher: Springer Nature ISBN: 9813345012 Category : Technology & Engineering Languages : en Pages : 834
Book Description
This book is a collection of high-quality peer-reviewed research papers presented at Sixth International Conference on Recent Trends in Computing (ICRTC 2020) held at SRM Institute of Science and Technology, Ghaziabad, Delhi, India, during 3 – 4 July 2020. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. The book presents original works from researchers from academic and industry in the field of networking, security, big data and the Internet of things.
Author: Zhuo Deng
Author: David Fleet
Author: Paul L. Rosin
Author: David Fleet
Author: Jaydev P. Desai
Author: Andrea Fossati
Author: Derek Hoiem
Author: Kevin Kar Wai Lai
Author: Rajendra Prasad Mahapatra
Author: Gaetano Kanizsa