Download or read book Semantic and Generic Object Segmentation for Scene Analysis Using RGB D Data written by Xiao Lin and published by . This book was released on 2018 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we study RGB-D based segmentation problems from different perspectives in terms of the input data. Apart from the basic photometric and geometric information contained in the RGB-D data, also semantic and temporal information are usually considered in an RGB-D based segmentation system. The first part of this thesis focuses on an RGB-D based semantic segmentation problem, where the predefined semantics and annotated training data are available. First, we review how RGB-D data has been exploited in the state of the art to help training classifiers in a semantic segmentation tasks. Inspired by these works, we follow a multi-task learning schema, where semantic segmentation and depth estimation are jointly tackled in a Convolutional Neural Network (CNN). Since semantic segmentation and depth estimation are two highly correlated tasks, approaching them jointly can be mutually beneficial. In this case, depth information along with the segmentation annotation in the training data helps better defining the target of the training process of the classifier, instead of feeding the system blindly with an extra input channel. We design a novel hybrid CNN architecture by investigating the common attributes as well as the distinction for depth estimation and semantic segmentation. The proposed architecture is tested and compared with state of the art approaches in different datasets. Although outstanding results are achieved in semantic segmentation, the limitations in these approaches are also obvious. Semantic segmentation strongly relies on predefined semantics and a large amount of annotated data, which may not be available in more general applications. On the other hand, classical image segmentation tackles the segmentation task in a more general way. But classical approaches hardly obtain object level segmentation due to the lack of higher level knowledge. Thus, in the second part of this thesis, we focus on an RGB-D based generic instance segmentation problem where temporal information is available from the RGB-D video while no semantic information is provided. We present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry and temporal correspondences in RGB-D. The proposed approach is validated and compared with state of the art generic segmentation approaches in different datasets. Finally, in the third part of this thesis, we present a method which combines the advantages in both semantic segmentation and generic segmentation, where we discover object instances using the generic approach and model them by learning from the few discovered examples by applying the approach of semantic segmentation. To do so, we employ the one shot learning technique, which performs knowledge transfer from a generally trained model to a specific instance model. The learned instance models generate robust features in distinguishing different instances, which is fed to the generic segmentation approach to perform improved segmentation. The approach is validated with experiments conducted on a carefully selected dataset.

Download or read book Object Recognition and Semantic Scene Labeling for RGB D Data written by Kevin Kar Wai Lai and published by . This book was released on 2013 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book RGB D Image Analysis and Processing written by Paul L. Rosin and published by Springer Nature. This book was released on 2019-10-26 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book RGB DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES written by Zhuo Deng and published by . This book was released on 2016 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-09-22 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Multimodal Scene Understanding written by Michael Ying Yang and published by Academic Press. This book was released on 2019-07-16 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Download or read book Pattern Recognition and Image Analysis written by Roberto Paredes and published by Springer. This book was released on 2015-06-09 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015. The 83 papers presented in this volume were carefully reviewed and selected from 141 submissions. They were organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Applications; Medical Image; Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; and Applications

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 855 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book ECPPM 2022 eWork and eBusiness in Architecture Engineering and Construction 2022 written by Eilif Hjelseth and published by CRC Press. This book was released on 2023-03-29 with total page 1412 pages. Available in PDF, EPUB and Kindle. Book excerpt: ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction contains the papers presented at the 14th European Conference on Product & Process Modelling (ECPPM 2022, Trondheim, Norway, 14-16 September 2022), and builds on a long-standing history of excellence in product and process modelling in the construction industry, which is currently known as Building Information Modelling (BIM). The following topics and applications are given special attention: Sustainable and Circular Driven Digitalisation: Data Driven Design and/or Decision Support Assessment and Documentation of Sustainability Information lifecycle Data Management: Collection, Processing and Presentation of Environmental Product Documentation (EPD) and Product Data Templates (PDT) Digital Enabled Collaboration: Integrated and Multi-Disciplinary Processes Virtual Design and Construction (VDC): Production Metrics, Integrated Concurrent Engineering, Lean Construction and Information Integration Automation of Processes: Automation of Design and Engineering Processes, Parametric Modelling and Robotic Process Automation Expert Systems: BIM based model and compliance checking Enabling Technologies: Machine Learning, Big Data, Artificial and Augmented Intelligence, Digital Twins, Semantic Technology Sensors and IoT Production with Autonomous Machinery, Robotics and Combinations of Existing and New Technical Solutions Frameworks for Implementation: International Information Management Series (ISO 19650), and Other International Standards (ISO), European (CEN) and National Standards, Digital Platforms and Ecosystems Human Factors in Digital Application: Digital Innovation, Economy of Digitalisation, Client, Organisational, Team and/or Individual Perspectives Over the past 25 years, the biennial ECPPM conference proceedings series has provided researchers and practitioners with a unique platform to present and discuss the latest developments regarding emerging BIM technologies and complementary issues for their adoption in the AEC/FM industry.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Roadside Video Data Analysis written by Brijesh Verma and published by Springer. This book was released on 2017-04-28 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment.

Download or read book Consumer Depth Cameras for Computer Vision written by Andrea Fossati and published by Springer Science & Business Media. This book was released on 2012-10-04 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Semantic Segmentation of Indoor Scene RGB D Images Based on Iterative Contraction and Merging written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Depth Aware Deep Learning Networks for Object Detection and Image Segmentation written by James Dickens and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectures for object detection and image segmentation are modified to incorporate dual backbone approaches inputting RGB and depth images, combining features from both modalities through the use of novel fusion modules. For each task, the models developed are competitive with state-of-the-art RGBD architectures. In particular, the proposed RGBD object detection approach achieves 53.5% mAP on the SUN RGBD 19-class object detection benchmark, while the proposed RGBD semantic segmentation architecture yields 69.4% accuracy with respect to the SUN RGBD 37-class semantic segmentation benchmark. An original 13-class RGBD instance segmentation benchmark is introduced for the SUN RGBD dataset, for which the proposed model achieves 38.4% mAP. Additionally, an original depth-aware panoptic segmentation model is developed, trained, and tested for new benchmarks conceived for the NYUDv2 and SUN RGBD datasets. These benchmarks offer researchers a baseline for the task of RGBD panoptic segmentation on these datasets, where the novel depth-aware model outperforms a comparable RGB counterpart.

Download or read book Applications of Computer Vision in Automation and Robotics written by Krzysztof Okarma and published by MDPI. This book was released on 2021-01-28 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research results related to various applications of computer vision methods in the widely understood contexts of automation and robotics. As the current progress of image analysis applications may be easily observed in various areas of everyday life, it becomes one of the most essential elements of development of Industry 4.0 solutions. Some of the examples, partially discussed in individual chapters, may be related to the visual navigation of mobile robots and drones, monitoring of industrial production lines, non-destructive evaluation and testing, monitoring of the IoT devices or the 3D printing process and the quality assessment of manufactured objects, video surveillance systems, and decision support in autonomous vehicles.

Download or read book Laws of Seeing written by Wolfgang Metzger and published by MIT Press. This book was released on 2009-08-21 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first English translation of a classic work in vision science from 1936 by a leading figure in the Gestalt movement, covering topics that continue to be major issues in vision research today. This classic work in vision science, written by a leading figure in Germany's Gestalt movement in psychology and first published in 1936, addresses topics that remain of major interest to vision researchers today. Wolfgang Metzger's main argument, drawn from Gestalt theory, is that the objects we perceive in visual experience are not the objects themselves but perceptual effigies of those objects constructed by our brain according to natural rules. Gestalt concepts are currently being increasingly integrated into mainstream neuroscience by researchers proposing network processing beyond the classical receptive field. Metzger's discussion of such topics as ambiguous figures, hidden forms, camouflage, shadows and depth, and three-dimensional representations in paintings will interest anyone working in the field of vision and perception, including psychologists, biologists, neurophysiologists, and researchers in computational vision—and artists, designers, and philosophers. Each chapter is accompanied by compelling visual demonstrations of the phenomena described; the book includes 194 illustrations, drawn from visual science, art, and everyday experience, that invite readers to verify Metzger's observations for themselves. Today's researchers may find themselves pondering the intriguing question of what effect Metzger's theories might have had on vision research if Laws of Seeing and its treasure trove of perceptual observations had been available to the English-speaking world at the time of its writing.

Download or read book Computer Vision Metrics written by Scott Krig and published by Apress. This book was released on 2014-06-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.