Download or read book Combining Recognition and Geometry for Data driven 3D Reconstruction written by Andrew Hale Owens and published by . This book was released on 2013 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's multi-view 3D reconstruction techniques rely almost exclusively on depth cues that come from multiple view geometry. While these cues can be used to produce highly accurate reconstructions, the resulting point clouds are often noisy and incomplete. Due to these issues, it may also be difficult to answer higher-level questions about the geometry, such as whether two surfaces meet at a right angle or whether a surface is planar. Furthermore, state-of-the-art reconstruction techniques generally cannot learn from training data, so having the ground-truth geometry for one scene does not aid in reconstructing similar scenes. In this work, we make two contributions toward data-driven 3D reconstruction. First, we present a dataset containing hundreds of RGBD videos that can be used as a source of training data for reconstruction algorithms. Second, we introduce the concept of the Shape Anchor, a region for which the combination of recognition and multiple view geometry allows us to accurately predict the latent, dense point cloud. We propose a technique to detect these regions and to predict their shapes, and we demonstrate it on our dataset.

Download or read book Combining Geometry and Learning for Scene Understanding written by Arun Kumar Chockalingam Santha Kumar and published by . This book was released on 2018 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: When an image is captured, the 3D Euclidean space describing its world is projected onto a 2D plane, effectively losing most pertinent underlying 3D Euclidean geometry information. Consequently, the ultimate goal of any 3D scene understanding system is to recover the lost 3D geometry while deconstructing the semantics of the scene, to perform perceptual decision making tasks such as object detection, pose estimation, shape recovery, etc. Furthermore, the ill-posed nature of the 3D scene recovery problem, where multiple shapes can generate the same image, adds further complexity to an already challenging problem. Significant research has been devoted toward solving the 3D scene recovery problem over the past few decades, with approaches ranging from triangulation and space carving using multiple views of the scene, to using learning-based models to learn semantic priors, to reason and reconstruct the scene. An alternative view of 3D scene understanding is that, given large amounts of data, it is possible to design machines that can automatically learn relevant relationships to perform various vision tasks such as reconstruction, pose prediction, etc. with minimal human supervision and without resorting to complex, manually-designed objective functions. The recent upsurge in deep learning techniques and abundance of data accompanied by the availability of annotations, has resulted in several state-of-the-art learning-based 3D reconstruction models that regress the underlying information in a purely data-driven manner. However, the success of deep learning has come at a hefty price, from the cost of gathering training data to the cost of painstaking labor involved in manual annotation of these data. In light of the above, the goal of this dissertation is to explore both learning-based and geometry-based approaches to 3D scene reconstruction, more specifically, equip learning-based models with geometric reasoning to enable joint scene understanding. This dissertation aims to move away from annotation-intensive learning-based techniques to develop 3D scene reconstruction models that harness the power of geometry and learn from arbitrary data instead of from manually curated 3D datasets, exploit class priors, and most importantly, address the learning and geometric reasoning tasks holistically, to more effectively combat ambiguities in reconstruction and recognition.

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 Geometry Inspired Deep Neural Networks for 3D Reconstruction written by Fengting Yang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reconstructing 3D models from given 2D images is one of the most fundamental problems in computer vision. Most traditional 3D reconstruction algorithms focus on geometric knowledge and attempt to tackle this problem with hand-craft features. However, these algorithms are usually fragile if the images contain noisy, textureless, or repetitive patterns. On the contrary, recent deep neural network-based methods rely on the image patterns and learn 3D information (e.g., depth and normal) in a data-driven manner. Without explicit geometric knowledge, these networks often suffer from performance drop once being applied to environments that are significantly different from the training ones. In this dissertation, we ask the question of whether we can address these shortcomings by combining the merits of traditional geometry-based methods and recent data-driven-based methods. To answer this question, we explore four popular 3D reconstruction tasks: (1) single-view 3D reconstruction, (2) stereo matching, (3) multi-view stereo (MVS), and (4) depth-from-focus (DFF). In each task, we take the deep neural network as the basic framework and integrate task-specific geometric knowledge into the network design. More specifically, in single-view reconstruction, we introduce plane regularity into the network and propose a structure-induced loss to train the network to recover 3D planes without supervision from ground truth plane annotation. In stereo matching, we apply the piecewise plane model to the network to better preserve object boundaries and fine details. A fully convolutional network-based superpixel segmentation approach is developed, and we incorporate it with an existing stereo matching network by considering each superpixel represents a projection of a slanted plane in the scene. In MVS, we integrate two common indoor priors into a truncated sign distance function (TSDF) regression network for indoor multi-view reconstruction. Finally, in DFF, we consider the special projective geometry of the defocus system and propose a deep differential focus volume for the DFF network. By developing these geometry-inspired networks for various tasks, we validate the effectiveness of integrating geometry with deep networks and provide an important stepping stone toward high-performance 3D reconstruction methods in multiple application settings.

Download or read book Robust Methods for Dense Monocular Non Rigid 3D Reconstruction and Alignment of Point Clouds written by Vladislav Golyanik and published by Springer Nature. This book was released on 2020-06-04 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book. About the Author: Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras).

Download or read book Advances in Visual Computing written by George Bebis and published by Springer Science & Business Media. This book was released on 2011-09-13 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 6938 and LNCS 6939 constitutes the refereed proceedings of the 7th International Symposium on Visual Computing, ISVC 2011, held in Las Vegas, NV, USA, in September 2011. The 68 revised full papers and 46 poster papers presented together with 30 papers in the special tracks were carefully reviewed and selected from more than 240 submissions. The papers of part I (LNCS 6938) are organized in computational bioimaging, computer graphics, motion and tracking, segmentation, visualization; mapping modeling and surface reconstruction, biomedical imaging, computer graphics, interactive visualization in novel and heterogeneous display environments, object detection and recognition. Part II (LNCS 6939) comprises topics such as immersive visualization, applications, object detection and recognition, virtual reality, and best practices in teaching visual computing.

Download or read book 3D Computer Vision written by Christian Wöhler and published by Springer Science & Business Media. This book was released on 2012-07-23 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This indispensable text introduces the foundations of three-dimensional computer vision and describes recent contributions to the field. Fully revised and updated, this much-anticipated new edition reviews a range of triangulation-based methods, including linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Also covered are intensity-based techniques that evaluate the pixel grey values in the image to infer three-dimensional scene structure, and point spread function based approaches that exploit the effect of the optical system. The text shows how methods which integrate these concepts are able to increase reconstruction accuracy and robustness, describing applications in industrial quality inspection and metrology, human-robot interaction, and remote sensing.

Download or read book Guide to 3D Vision Computation written by Kenichi Kanatani and published by Springer. This book was released on 2018-07-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.

Download or read book Pattern Recognition written by Volker Roth and published by Springer. This book was released on 2017-09-06 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 39th German Conference on Pattern Recognition, GCPR 2017, held in Basel, Switzerland, in September 2017.The 33 revised full papers presented were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on biomedical image processing and analysis; classification and detection; computational photography; image and video processing; machine learning and pattern recognition; mathematical foundations, statistical data analysis and models; motion and segmentation; pose, face and gesture; reconstruction and depth; and tracking.

Download or read book Pattern Recognition written by Gernot A. Fink and published by Springer Nature. This book was released on 2019-10-24 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 41st DAGM German Conference on Pattern Recognition, DAGM GCPR 2019, held in Dortmund, Germany, in September 2019. The 43 revised full papers presented were carefully reviewed and selected from 91 submissions. The German Conference on Pattern Recognition is the annual symposium of the German Association for Pattern Recognition (DAGM). It is the national venue for recent advances in image processing, pattern recognition, and computer vision and it follows the long tradition of the DAGM conference series.

Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-08 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Download or read book Pattern Recognition written by Katrin Franke and published by Springer Science & Business Media. This book was released on 2006-09-11 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 28th Symposium of the German Association for Pattern Recognition, DAGM 2006. The book presents 32 revised full papers and 44 revised poster papers together with 5 invited papers. Topical sections include image filtering, restoration and segmentation, shape analysis and representation, recognition, categorization and detection, computer vision and image retrieval, machine learning and statistical data analysis, biomedical data analysis, and more.

Download or read book Cyber Security Intelligence and Analytics written by Zheng Xu and published by Springer Nature. This book was released on 2022-02-26 with total page 1080 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the outcomes of the 2022 4th International Conference on Cyber Security Intelligence and Analytics (CSIA 2022), an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field of cyber-security, particularly focusing on threat intelligence, analytics, and countering cyber-crime. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings and novel techniques, methods and applications on all aspects of cyber-security intelligence and analytics. Due to COVID-19, authors, keynote speakers and PC committees will attend the conference online.

Download or read book Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate written by K. W. Chau and published by Springer. This book was released on 2017-12-18 with total page 1500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of CRIOCM_2016, 21st International Conference on Advancement of Construction Management and Real Estate, sharing the latest developments in real estate and construction management around the globe. The conference was organized by the Chinese Research Institute of Construction Management (CRIOCM) working in close collaboration with the University of Hong Kong. Written by international academics and professionals, the proceedings discuss the latest achievements, research findings and advances in frontier disciplines in the field of construction management and real estate. Covering a wide range of topics, including building information modelling, big data, geographic information systems, housing policies, management of infrastructure projects, occupational health and safety, real estate finance and economics, urban planning, and sustainability, the discussions provide valuable insights into the implementation of advanced construction project management and the real estate market in China and abroad. The book is an outstanding reference resource for academics and professionals alike.

Download or read book Current Trends on Knowledge Based Systems written by Giner Alor-Hernández and published by Springer. This book was released on 2017-03-13 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents innovative and high-quality research on the implementation of conceptual frameworks, strategies, techniques, methodologies, informatics platforms and models for developing advanced knowledge-based systems and their application in different fields, including Agriculture, Education, Automotive, Electrical Industry, Business Services, Food Manufacturing, Energy Services, Medicine and others. Knowledge-based technologies employ artificial intelligence methods to heuristically address problems that cannot be solved by means of formal techniques. These technologies draw on standard and novel approaches from various disciplines within Computer Science, including Knowledge Engineering, Natural Language Processing, Decision Support Systems, Artificial Intelligence, Databases, Software Engineering, etc. As a combination of different fields of Artificial Intelligence, the area of Knowledge-Based Systems applies knowledge representation, case-based reasoning, neural networks, Semantic Web and TICs used in different domains. The book offers a valuable resource for PhD students, Master’s and undergraduate students of Information Technology (IT)-related degrees such as Computer Science, Information Systems and Electronic Engineering.

Download or read book Smartphone Based Indoor Map Construction written by Ruipeng Gao and published by Springer. This book was released on 2018-03-27 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on ubiquitous indoor localization services, specifically addressing the issue of floor plans. It combines computer vision algorithms and mobile techniques to reconstruct complete and accurate floor plans to provide better location-based services for both humans and vehicles via commodity smartphones in indoor environments (e.g., a multi-layer shopping mall with underground parking structures). After a comprehensive review of scene reconstruction methods, it offers accurate geometric information for each landmark from images and acoustics, and derives the spatial relationships of the landmarks and rough sketches of accessible areas with inertial and WiFi data to reduce computing overheads. It then presents the authors’ recent findings in detail, including the optimization and probabilistic formulations for more solid foundations and better robustness to combat errors, several new approaches to promote the current sporadic availability of indoor location-based services, and a holistic solution for floor plan reconstruction, indoor localization, tracking, and navigation. The novel approaches presented are designed for different types of indoor environments (e.g., shopping malls, office buildings and labs) and different users. A valuable resource for researchers and those in start-ups working in the field, it also provides supplementary material for students with mobile computing and networking backgrounds.

Download or read book Multibiometrics for Human Identification written by Bir Bhanu and published by Cambridge University Press. This book was released on 2011-04-29 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's security-conscious society, real-world applications for authentication or identification require a highly accurate system for recognizing individual humans. The required level of performance cannot be achieved through the use of a single biometric such as face, fingerprint, ear, iris, palm, gait or speech. Fusing multiple biometrics enables the indexing of large databases, more robust performance and enhanced coverage of populations. Multiple biometrics are also naturally more robust against attacks than single biometrics. This book addresses a broad spectrum of research issues on multibiometrics for human identification, ranging from sensing modes and modalities to fusion of biometric samples and combination of algorithms. It covers publicly available multibiometrics databases, theoretical and empirical studies on sensor fusion techniques in the context of biometrics authentication, identification and performance evaluation and prediction.