High Dimensional Convolutional Neural Networks For 3d Perception

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High Dimensional Convolutional Neural Networks For 3d Perception

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Author : Christopher Bongsoo Choy
language : en
Publisher:
Release Date : 2020

High Dimensional Convolutional Neural Networks For 3d Perception written by Christopher Bongsoo Choy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

The automation of mechanical tasks brought the modern world unprecedented prosperity and comfort. However, the majority of automated tasks have been simple mechanical tasks that only require repetitive motion. Tasks that require visual perception and high-level cognition still have become the last frontiers of automation. Many of these tasks require visual perception such as automated warehouses where robots package items in disarray, autonomous driving where autonomous agents localize themselves, identify and track other dynamic objects in the 3D world. This ability to represent, identify, and interpret visual three-dimensional data to understand the underlying three-dimensional structure in the real world is known as 3D perception. In this dissertation, we propose learning-based approaches to tackle challenges in 3D perception. Specifically, we propose a set of high-dimensional convolutional neural networks for three categories of problems in 3D perception: reconstruction, representation learning, and registration. Reconstruction is the first step that generates 3D point clouds or meshes from a set of sensory inputs. We present supervised reconstruction methods using 3D convolutional neural networks that take a set of images as input and generate 3D occupancy patterns in a grid as output. We train the networks with a large-scale 3D shape dataset to generate a set of images rendered from various viewpoints validate the approach on real image datasets. However, supervised reconstruction requires 3D shapes as labels for all images, which are expensive to generate. Instead, we propose using a set of foreground masks and unlabeled real 3D shapes to train the reconstruction network as weaker supervision. Combined with the learned constraint, we train the reconstruction system with as few as 1 image and show that the proposed model without direct 3D supervision. In the second part of the dissertation, we present sparse tensor networks, neural networks for spatially sparse tensors. As we increase the spatial dimension, the sparsity of input data decreases drastically as the volume of the space increases exponentially. Sparse tensor networks exploit such inherent sparsity in the input data and efficiently process them. With the sparse tensor network, we create a 4-dimensional convolutional network for spatio-temporal perception for 3D scans or a sequence of 3D scans (3D video). We show that 4-dimensional convolutional neural networks can effectively make use of temporal consistency and improve the accuracy of segmentation. Next, we use the sparse tensor networks for geometric representation learning to capture both local and global 3D structures accurately for correspondences and registration. We propose fully convolutional networks and new types of metric learning losses that allow neurons to capture large context while capturing local spatial geometry. We experimentally validate our approach on both indoor and outdoor datasets and show that the network outperforms the state-of-the-art method while being a few orders of magnitude faster. In the third and the last part of the dissertation, we discuss high-dimensional pattern recognition problems in image and 3D registration. We first propose high-dimensional convolutional networks from 4 to 32-dimensional spaces and analyze the geometric pattern recognition capacity of these high-dimensional convolutional networks for linear regression problems. Next, we show that the 3D correspondences form a hyper-surface in 6-dimensional space; and 2D correspondences form a 4-dimensional hyper-conic section, which we detect using high-dimensional convolutional networks. We extend the proposed high-dimensional convolutional networks for differentiable 3D registration and propose three core modules for this: a 6-dimensional convolutional neural network for correspondence confidence prediction; a differentiable Weighted Procrustes method for closed-form pose estimation; and a robust gradient-based 3D rigid transformation optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art learning-based and classical methods on real-world data while maintaining efficiency.

A Guide To Convolutional Neural Networks For Computer Vision

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Author : Salman Khan
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

A Guide To Convolutional Neural Networks For Computer Vision written by Salman Khan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs.The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Deep Learning For 3d Vision Algorithms And Applications

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Author : Xiaoli Li
language : en
Publisher: World Scientific
Release Date : 2024-08-27

Deep Learning For 3d Vision Algorithms And Applications written by Xiaoli Li and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Computers categories.

3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.

Computer Vision Eccv 2018

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Author : Vittorio Ferrari
language : en
Publisher: Springer
Release Date : 2018-10-08

Computer Vision Eccv 2018 written by Vittorio Ferrari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.

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.

Computer Vision Eccv 2020

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Author : Andrea Vedaldi
language : en
Publisher: Springer Nature
Release Date : 2020-11-12

Computer Vision Eccv 2020 written by Andrea Vedaldi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-12 with Computers categories.

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.

Computer Vision Eccv 2022

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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-11-10

Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Computers categories.

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.

The International Conference On Image Vision And Intelligent Systems Icivis 2021

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Author : Jian Yao
language : en
Publisher: Springer Nature
Release Date : 2022-03-03

The International Conference On Image Vision And Intelligent Systems Icivis 2021 written by Jian Yao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-03 with Technology & Engineering categories.

This book is a collection of the papers accepted by the ICIVIS 2021—The International Conference on Image, Vision and Intelligent Systems held on June 15–17, 2021, in Changsha, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings.

Mastering New Age Computer Vision

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Author : Zonunfeli Ralte
language : en
Publisher: BPB Publications
Release Date : 2025-02-19

Mastering New Age Computer Vision written by Zonunfeli Ralte and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-19 with Computers categories.

DESCRIPTION Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications. This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks. By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field. KEY FEATURES ● Master PyTorch for image processing, segmentation, and object detection. ● Explore advanced computer vision techniques like ViT and panoptic models. ● Apply multi-tasking, metric, bilinear pooling, and self-supervised learning in real-world scenarios. WHAT YOU WILL LEARN ● Use PyTorch for both basic and advanced image processing. ● Build object detection models using CNNs and modern frameworks. ● Apply multi-task and multi-instance learning to complex datasets. ● Develop segmentation models, including panoptic segmentation. ● Improve feature representation with metric learning and bilinear pooling. ● Explore transformers and self-supervised learning for computer vision. WHO THIS BOOK IS FOR This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed. TABLE OF CONTENTS 1. Evolution of New Age Computer Vision Models 2. Image Processing with PyTorch 3. Designing of Advanced Computer Vision Techniques 4. Designing Superior Computer Vision Techniques 5. Advanced Object Detection with FPN, RPN, and DetectoRS 6. Multi-instance Learning 7. More Advanced Multi-instance Learning 8. Beyond Classical Segmentation Panoptic Segmentation with SAM 9. Crafting Deep Metric Learning in Embedding Space 10. Navigating the Realm of Metric Learning 11. Multi-tasking with Multi-task Learning 12. Fine-grained Bilinear CNN 13. The Rise of Self-supervised Learning 14. Advancements in Computer Vision Landscape

Computer Vision Eccv 2014 Workshops

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Author : Lourdes Agapito
language : en
Publisher: Springer
Release Date : 2015-03-18

Computer Vision Eccv 2014 Workshops written by Lourdes Agapito and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-18 with Computers categories.

The four-volume set LNCS 8925, 8926, 8927, and 8928 comprises the refereed post-proceedings of the Workshops that took place in conjunction with the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings. They were presented at workshops with the following themes: where computer vision meets art; computer vision in vehicle technology; spontaneous facial behavior analysis; consumer depth cameras for computer vision; "chalearn" looking at people: pose, recovery, action/interaction, gesture recognition; video event categorization, tagging and retrieval towards big data; computer vision with local binary pattern variants; visual object tracking challenge; computer vision + ontology applies cross-disciplinary technologies; visual perception of affordance and functional visual primitives for scene analysis; graphical models in computer vision; light fields for computer vision; computer vision for road scene understanding and autonomous driving; soft biometrics; transferring and adapting source knowledge in computer vision; surveillance and re-identification; color and photometry in computer vision; assistive computer vision and robotics; computer vision problems in plant phenotyping; and non-rigid shape analysis and deformable image alignment. Additionally, a panel discussion on video segmentation is included. .

Human Factor Security And Safety

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Author : Cheng Wang
language : en
Publisher: Springer Nature
Release Date : 2025-06-04

Human Factor Security And Safety written by Cheng Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-04 with Computers categories.

As we navigate an increasingly complex digital landscape, ensuring human factor security and safety has never been more critical. This groundbreaking book offers an innovative perspective by leveraging behavioral computing to address challenges in human factor security across diverse scenarios. Through comprehensive coverage, the book introduces advanced methods like behavioral modeling and simulation to analyze multimodal behavioral data—spanning structure, language, and vision. These approaches not only deepen our understanding of human behavior but also enable robust solutions to modern security concerns, presenting a new paradigm for safeguarding systems in dynamic environments. Ideal for researchers, professionals, and advanced students in security technologies and behavioral sciences, this work bridges theoretical frameworks with practical applications. Key topics include the evolution of behavioral paradigms, the role of behavioral structures in simulation, and real-world applications in cyberfinance and human factor engineering.