Download or read book 3D Scene and Object Parsing from a Single Image written by Chuhang Zou and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Single View 3D Reconstruction and Parsing Using Geometric Commonsense for Scene Understanding written by Chengcheng Yu and published by . This book was released on 2017 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: My thesis studies this topic in three perspective: (1) 3D scene reconstruction to understand the 3D structure of a scene. (2) Geometry and physics reasoning to understand the relationships of objects in a scene. (3) The interaction between human action and objects in a scene. Specifically, the 3D reconstruction builds a unified grammatical framework capable of reconstructing a variety of scene types (e.g., urban, campus, county etc.) from a single input image. The key idea of our approach is to study a novel commonsense reasoning framework that mainly exploits two types of prior knowledges: (i) prior distributions over a single dimension of objects, e.g., that the length of a sedan is about 4.5 meters; (ii) pair-wise relationships between the dimensions of scene entities, e.g., that the length of a sedan is shorter than a bus. These unary or relative geometric knowledge, once extracted, are fairly stable across different types of natural scenes, and are informative for enhancing the understanding of various scenes in both 2D images and 3D world. Methodologically, we propose to construct a hierarchical graph representation as a unified representation of the input image and related geometric knowledge. We formulate these objectives with a unified probabilistic formula and develop a data-driven Monte Carlo method to infer the optimal solution with both bottom-to-up and top-down computations. Results with comparisons on public datasets showed that our method clearly outperforms the alternative methods. For geometry and physics reasoning, we present an approach for scene understanding by reasoning physical stability of objects from point cloud. We utilize a simple observation that, by human design, objects in static scenes should be stable with respect to gravity. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Our method consists of two major steps: 1) geometric reasoning: recovering solid 3D volumetric primitives from defective point cloud; and 2) physical reasoning: grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior. We propose to use a novel disconnectivity graph (DG) to represent the energy landscape and use a Swendsen-Wang Cut (MCMC) method for optimization. In experiments, we demonstrate that the algorithm achieves substantially better performance for i) object segmentation, ii) 3D volumetric recovery of the scene, and iii) better parsing result for scene understanding in comparison to state-of-the-art methods in both public dataset and our own new dataset. Detecting potential dangers in the environment is a fundamental ability of living beings. In order to endure such ability to a robot, my thesis presents an algorithm for detecting potential falling objects, i.e. physically unsafe objects, given an input of 3D point clouds captured by the range sensors. We formulate the falling risk as a probability or a potential that an object may fall given human action or certain natural disturbances, such as earthquake and wind. Our approach differs from traditional object detection paradigm, it first infers hidden and situated "causes (disturbance) of the scene, and then introduces intuitive physical mechanics to predict possible "effects (falls) as consequences of the causes. In particular, we infer a disturbance field by making use of motion capture data as a rich source of common human pose movement. We show that, by applying various disturbance fields, our model achieves a human level recognition rate of potential falling objects on a dataset of challenging and realistic indoor scenes.

Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-16 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-22 with total page 803 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 881 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 Intelligent Scene Modeling and Human Computer Interaction written by Nadia Magnenat Thalmann and published by Springer Nature. This book was released on 2021-06-08 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book is one of the first to describe how Autonomous Virtual Humans and Social Robots can interact with real people and be aware of the surrounding world using machine learning and AI. It includes: · Many algorithms related to the awareness of the surrounding world such as the recognition of objects, the interpretation of various sources of data provided by cameras, microphones, and wearable sensors · Deep Learning Methods to provide solutions to Visual Attention, Quality Perception, and Visual Material Recognition · How Face Recognition and Speech Synthesis will replace the traditional mouse and keyboard interfaces · Semantic modeling and rendering and shows how these domains play an important role in Virtual and Augmented Reality Applications. Intelligent Scene Modeling and Human-Computer Interaction explains how to understand the composition and build very complex scenes and emphasizes the semantic methods needed to have an intelligent interaction with them. It offers readers a unique opportunity to comprehend the rapid changes and continuous development in the fields of Intelligent Scene Modeling.

Download or read book Reconstruction and Analysis of 3D Scenes written by Martin Weinmann and published by Springer. This book was released on 2016-03-17 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Santhanam and published by Springer Nature. This book was released on 2022-05-31 with total page 147 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 Computer Vision written by Mrinal Kanti Bhowmik and published by CRC Press. This book was released on 2024-03-07 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of objects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges. Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions. Topics and Features: • Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment. • Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems. • Gives detailed visual examples of applications of object detection in the real world. • Presents a detailed description of popular imaging modalities for object detection adopted by researchers. • Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks. • Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures. • Discusses techniques for detection of objects in real-world applications. • Contains various hands-on practical examples and a tutorial for solving object detection problems using Python. • Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges. The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.

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 3D Scene Understanding written by Zhaoyin Jia and published by . This book was released on 2014 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Segmentation is one of the fundamental computer vision problems and has been investigated over years. In this thesis, we present algorithms for RGB-D image segmentation, and more importantly, the additional information that can be inferred from segmentations: depth ordering, 3D surfaces, occlusion boundaries and volumes of objects. All these clues lead to a more comprehensive 3D understanding of the scene as well as a higher level RGB-D interpretation. Also in return some of these clues can provide important feedbacks and improve the final scene segmentation performance. We start by performing 3D depth interpretation from 2D color images only. We discover that the segment shapes enable us to learn the depth orderings of the objects. Specifically, from the initial segmentation we develop features to encode the information captured in boundaries and junctions. After a supervised learning procedure, our algorithm is able to produce a 3D depth ordering map from a single 2D color image. Secondly, we proceed to 3D scene understanding using RGB-D images. The recent development of the depth sensors improves the performance of the traditional computer vision algorithms by a margin. Therefore, besides using one single image, we incorporate depth information along with it, and parse the scene based on 3D interpretation. We aim at the applications such as 3D point interpolation, boundary detection and scene segmentation. In detail, we propose algorithm for 3D surface segmentation, and show that combining this 3D surface information with 2D color image achieves better performance for 3D interpolation. After that, we use both 2D color and 3D depth channels to find the occlusion and connected boundaries given a RGB-D scene. This serves as an extended 3D scene interpretation with a better understanding of occlusions between objects. Finally we perform a 3D volumetric reasoning of the RGB-D image with support and stability. Objects occupy physical space and obey physical laws. To truly understand a scene, we must reason about the space that objects in it occupy, and how each objects is supported stably by each other. In other words, we seek to understand which objects would, if moved, cause other objects to fall. This 3D volumetric reasoning is important for many scene understanding tasks, ranging from segmentation of objects to perception of a rich 3D, physically well-founded, interpretations of the scene. In this thesis, we propose a new algorithm to parse RGB-D images with 3D block units while jointly reasoning about the segments, volumes, supporting relationships and object stability. Our algorithm is based on the intuition that a good 3D representation of the scene is one that fits the depth data well, and is a stable, self-supporting arrangement of objects (i.e., one that does not topple). We design an energy function for representing the quality of the block representation based on these properties. Our algorithm fits 3D blocks to the depth values corresponding to image segments, and iteratively optimizes the energy function. Our proposed algorithm is the first to consider stability of objects in complex arrangements for reasoning about the underlying structure of the scene. Experimental results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.

Download or read book Proceedings of 2017 Chinese Intelligent Automation Conference written by Zhidong Deng and published by Springer. This book was released on 2017-10-25 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings present selected research papers from the CIAC’17, held in Tianjin, China. The topics include adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, reconfigurable control, and etc. Engineers and researchers from academia, industry, and government can gain valuable insights into solutions combining ideas from multiple disciplines in the field of intelligent automation.

Download or read book 3D Imaging Analysis and Applications written by Yonghuai Liu and published by Springer Nature. This book was released on 2020-09-11 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Vision ACCV 2014 Workshops written by C.V. Jawahar and published by Springer. This book was released on 2015-04-11 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set, consisting of LNCS 9008, 9009, and 9010, contains carefully reviewed and selected papers presented at 15 workshops held in conjunction with the 12th Asian Conference on Computer Vision, ACCV 2014, in Singapore, in November 2014. The 153 full papers presented were selected from numerous submissions. LNCS 9008 contains the papers selected for the Workshop on Human Gait and Action Analysis in the Wild, the Second International Workshop on Big Data in 3D Computer Vision, the Workshop on Deep Learning on Visual Data, the Workshop on Scene Understanding for Autonomous Systems and the Workshop on Robust Local Descriptors for Computer Vision. LNCS 9009 contains the papers selected for the Workshop on Emerging Topics on Image Restoration and Enhancement, the First International Workshop on Robust Reading, the Second Workshop on User-Centred Computer Vision, the International Workshop on Video Segmentation in Computer Vision, the Workshop: My Car Has Eyes: Intelligent Vehicle with Vision Technology, the Third Workshop on E-Heritage and the Workshop on Computer Vision for Affective Computing. LNCS 9010 contains the papers selected for the Workshop on Feature and Similarity for Computer Vision, the Third International Workshop on Intelligent Mobile and Egocentric Vision and the Workshop on Human Identification for Surveillance.

Download or read book From Animals to Robots and Back Reflections on Hard Problems in the Study of Cognition written by Jeremy L. Wyatt and published by Springer. This book was released on 2014-07-10 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive Science is a discipline that brings together research in natural and artificial systems and this is clearly reflected in the diverse contributions to From Animals to Robots and Back. In tribute to Aaron Sloman and his pioneering work in Cognitive Science and Artificial Intelligence, the editors have collected a unique collection of cross-disciplinary papers that include work on: · intelligent robotics; · philosophy of cognitive science; · emotional research · computational vision; · comparative psychology; and · human-computer interaction. Key themes such as the importance of taking an architectural view in approaching cognition, run through the text. Drawing on the expertize of leading international researchers, contemporary debates in the study of natural and artificial cognition are addressed from complementary and contrasting perspectives with key issues being outlined at various levels of abstraction. From Animals to Robots and Back, will give readers with backgrounds in the study of both natural and artificial cognition an important window on the state of the art in cognitive systems research.

Download or read book Computer Vision ACCV 2016 written by Shang-Hong Lai and published by Springer. This book was released on 2017-03-09 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.