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Boosting Unsupervised Domain Adaptation For 3d Object Detection In Point Clouds Via 2d Image Semantic Information
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Book Synopsis Boosting Unsupervised Domain Adaptation for 3D Object Detection in Point Clouds Via 2D Image Semantic Information by : 谷俊杰
Download or read book Boosting Unsupervised Domain Adaptation for 3D Object Detection in Point Clouds Via 2D Image Semantic Information written by 谷俊杰 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Boosting for Generic 2D/3D Object Recognition by : Doaa Abd al-Kareem Mohammed Hegazy
Download or read book Boosting for Generic 2D/3D Object Recognition written by Doaa Abd al-Kareem Mohammed Hegazy and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generic object recognition is an important function of the human visual system. For an artificial vision system to be able to emulate the human perception abilities, it should also be able to perform generic object recognition. In this thesis, we address the generic object recognition problem and present different approaches and models which tackle different aspects of this difficult problem. First, we present a model for generic 2D object recognition from complex 2D images. The model exploits only appearance-based information, in the form of a combination of texture and color cues, for binary classification of 2D object classes. Learning is accomplished in a weakly supervised manner using Boosting. However, we live in a 3D world and the ability to recognize 3D objects is very important for any vision system. Therefore, we present a model for generic recognition of 3D objects from range images. Our model makes use of a combination of simple local shape descriptors extracted from range images for recognizing 3D object categories, as shape is an important information provided by range images. Moreover, we present a novel dataset for generic object recognition that provides 2D and range images about different object classes using a Time-of-Flight (ToF) camera.
Book Synopsis Pattern Recognition and Computer Vision by : Qingshan Liu
Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2023-12-23 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.
Book Synopsis Person Re-Identification by : Shaogang Gong
Download or read book Person Re-Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.
Book Synopsis Reconstruction and Analysis of 3D Scenes by : Martin Weinmann
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.
Book Synopsis Enhancing Point Cloud Generation From Various Information Sources by Applying Geometry-aware Folding Operation by : Yu Lin
Download or read book Enhancing Point Cloud Generation From Various Information Sources by Applying Geometry-aware Folding Operation written by Yu Lin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A plethora of cutting-edge computer vision and graphic applications, such as Augmented Reality (AR), Virtual Reality (VR), automatic vehicles, and robotics, require rapid creation and access to abundant 3D data. Among various 3D data representations, e.g., RGB images, depth images, or voxel grids, point cloud attracts considerable attention from the research community because it offers additional geometric, shape, and scale information in comparison with 2D images and demands less computational resource to process in contrast to other 3D representations, e.g., voxel grids, octree, or triangle meshes. Unfortunately, even with the increasing availability of 3D sensors, the size and variety of 3D point clouds datasets pale when compared to the vast size datasets of other representations. Therefore, it will benefit many applications if we can generate point clouds from other information sources. Point cloud generation is a sub-field of 3D reconstruction, which aims to generate a complete 3D object from other information sources. Conventional methods generally focus on 2D images and heavily rely on the knowledge of multi-view geometry, while multiple 2D views of a target 3D object usually are inaccessible in many real-world scenarios. On the contrary, recent deep learning approaches either dedicate to 3D representations with regular structures, such as voxel grids and octrees, and thus suffer from resolution and scalability issues, or unconsciously ignore the crucial 3D prior knowledge and lead to sub-optimal solutions. To address the aforementioned drawbacks, we explore the possibilities to improve the point cloud generation by developing advanced folding operations and geometry-aware (3D-prioraware) reconstruction networks in this dissertation. Specifically, we start with a novel point cloud generation framework TDPNet that reconstructs complete point clouds by employing a hierarchical manifold decoder and a collection of latent 3D prototypes. Later, we find that applying vanilla folding operation is insufficient for a realistic reconstruction, and using KMeans centroids as the prototype features is unstable and lacks interpretability. Inspired by these observations, we further introduce a novel framework equipped with a collection of Learnable Shape Primitives (L-SHAP), which encode the crucial 3D prior knowledge from training data through an additional folding operation. On the other hand, it’s beneficial to many applications if point clouds can be generated in a few-shot scenario. We tackle this problem by a novel few-shot generation framework FSPG, which simultaneously considers class-agnostic and class-specific 3D priors during the generation process. Finally, we observe that conventional folding operations are implemented by a simple shared-MLP, which increases training difficulty and limits the network’s modeling capability. In order to solve this problem, we incorporate the popular Transformer architecture into a novel attentional folding decoder AttnFold and introduce a Local Semantic Consistency (LSC) regularizer to further boost the model’s capability. Based on our research, we demonstrate that learning flexible data-driven 3D priors and adopting advanced folding operations are effective for point cloud generation under different problem settings.
Book Synopsis Domain Adaptation in Computer Vision with Deep Learning by : Hemanth Venkateswara
Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Book Synopsis 2D Object Detection and Recognition by : Yali Amit
Download or read book 2D Object Detection and Recognition written by Yali Amit and published by MIT Press. This book was released on 2002 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the computer detection and recognition of 2D objects in gray-level images.
Book Synopsis Distributionally Robust Unsupervised Domain Adaptation and Its Applications in 2D and 3D Image Analysis by : Yibin Wang
Download or read book Distributionally Robust Unsupervised Domain Adaptation and Its Applications in 2D and 3D Image Analysis written by Yibin Wang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obtaining ground-truth label information from real-world data along with uncertainty quantification can be challenging or even infeasible. In the absence of labeled data for a certain task, unsupervised domain adaptation (UDA) techniques have shown great accomplishment by learning transferable knowledge from labeled source domain data and adapting it to unlabeled target domain data, yet uncertainties are still a big concern under domain shifts. Distributionally robust learning (DRL) is emerging as a high-potential technique for building reliable learning systems that are robust to distribution shifts. In this research, a distributionally robust unsupervised domain adaptation (DRUDA) method is proposed to enhance the machine learning model generalization ability under input space perturbations. The DRL-based UDA learning scheme is formulated as a min-max optimization problem by optimizing worst-case perturbations of the training source data. Our Wasserstein distributionally robust framework can reduce the shifts in the joint distributions across domains. The proposed DRUDA method has been tested on various benchmark datasets. In addition, a gradient mapping-guided explainable network (GMGENet) is proposed to analyze 3D medical images for extracapsular extension (ECE) identification. DRUDA-enhanced GMGENet is evaluated, and experimental results demonstrate that the proposed DRUDA improves transfer performance on target domains for the 3D image analysis task successfully. This research enhances the understanding of distributionally robust optimization in domain adaptation and is expected to advance the current unsupervised machine learning techniques.
Book Synopsis Visual Domain Adaptation in the Deep Learning Era by : Gabriela Csurka
Download or read book Visual Domain Adaptation in the Deep Learning Era written by Gabriela Csurka and published by Morgan & Claypool Publishers. This book was released on 2022-04-05 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.
Book Synopsis Unsupervised Domain Adaptation by : Jingjing Li
Download or read book Unsupervised Domain Adaptation written by Jingjing Li and published by Springer Nature. This book was released on with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Boosted Object Detection Based on Local Features by : Haoyu Ren
Download or read book Boosted Object Detection Based on Local Features written by Haoyu Ren and published by . This book was released on 2016 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is to find and localize objects of a specific class in images or videos. This task is the foundation of image and video understanding, thus it becomes one of the most popular topics in the area of computer vision and pattern recognition. Object detection is not only essential for the study of computer vision, pattern recognition and image processing, but also valuable in the applications of public safety, entertainment and business. In this research, we aim to solve this problem in two focused areas: the local feature design, and the boosting learning. Our research on local features could be summarized into a hierarchical structure with 3 levels. The features in different levels capture different object characteristic information. In the lower level, we investigate how to design effective binary features, which perform quite well for the object categories with small intra-class variations. In the middle level, we consider integrating the gradient information and structural information together. This results in more discriminative gradient features. In the higher level, we discuss how to construct the co-occurrence features. Using such features, we may get a classifier with high accuracy for general object detection.After the feature extraction, boosted classifiers are learned for the final decision. We work on two aspects to improve the effectiveness of boosting learning. Firstly, we improve the discriminative ability of the weak classifiers by the proposed basis mapping. We show that learning in the mapped space is more effective compared to learning in the original space. In addition, we explore the efficiency-accuracy trade-off problem in boosting learning. The Generalization and Efficiency Balance (GEB) framework, and the hierarchical weak classifier are designed for this target. As a result, the resulting boosted classifiers not only achieve high accuracy, but also have good generalization and efficiency. The performance of the proposed local features and boosting algorithms are evaluated using the benchmark datasets of faces, pedestrians, and general objects. The experimental results show that our work achieves better accuracy compared to the methods using traditional features and machine learning algorithms.
Book Synopsis 3D Object Detection for Advanced Driver Assistance Systems by : Selameab Demilew
Download or read book 3D Object Detection for Advanced Driver Assistance Systems written by Selameab Demilew and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and timely perception of the environment is an essential requirement of all autonomous and semi-autonomous systems. This necessity has been the main factor behind the rapid growth and adoption of LiDAR sensors within the ADAS sensor suite. In this thesis, we develop a fast and accurate 3D object detector that converts raw point clouds collected by LiDARs into sparse occupancy cuboids to detect cars and other road users using deep convolutional neural networks. The proposed pipeline reduces the runtime of PointPillars by 43% and performs on par with other state-of-the-art models. We do not gain improvements in speed by compromising the network's complexity and learning capacity but rather through the use of an efficient input encoding procedure. In addition to rigorous profiling on three different platforms, we conduct a comprehensive error analysis and recognize principal sources of error among the predicted attributes. Even though point clouds adequately capture the 3D structure of the physical world, they lack the rich texture information present in color images. In light of this, we explore the possibility of fusing the two modalities with the intent of improving detection accuracy. We present a late fusion strategy that merges the classification head of our LiDAR-based object detector with semantic segmentation maps inferred from images. Extensive experiments on the KITTI 3D object detection benchmark demonstrate the validity of the proposed fusion scheme.
Book Synopsis Unsupervised Learning for 3D Point Cloud Object Detection Using Roadside Dataset by : 吳泯駿
Download or read book Unsupervised Learning for 3D Point Cloud Object Detection Using Roadside Dataset written by 吳泯駿 and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Object Detection and Tracking in Images and Point Clouds by : Daniel Finnegan
Download or read book Object Detection and Tracking in Images and Point Clouds written by Daniel Finnegan and published by . This book was released on 2013-07 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelor Thesis from the year 2012 in the subject Computer Science - Software, printed single-sided, grade: A+, University College Dublin, language: English, abstract: Tracking objects in 3-dimensions is an important problem in computer vision. This paper aims to present the problem in the context of modern technology combined with established algorithms to create a hybrid system for tracking moving objects. The main issues in terms of state of the art implementation and theoretical viewpoint are discussed and conclusions are drawn on the direction taken.
Book Synopsis 3D Object Detection and Depth Completion for Scene Perception by : Jason Ku
Download or read book 3D Object Detection and Depth Completion for Scene Perception written by Jason Ku and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D object detection is a fundamental component in the autonomous driving perception pipeline. While images provide rich semantic information, depth information is lost during perspective projection. LiDAR scans provide accurate depth measurements, but the data is sparse at longer distances. In this thesis, we first propose a method to fuse LiDAR and image features in an end-to-end trainable network to achieve robust, real-time 3D detection performance. Next, to alleviate the sparsity of LiDAR data, we propose a fast, CPU based, depth completion algorithm using only classical image processing techniques, which outperforms several learning based methods. The densified depth map is then used to synthesize virtual views of pedestrians, which are incorporated into a proposed orientation estimation pipeline to improve pedestrian heading estimation. Finally, a monocular 3D detection neural network is proposed, which leverages proposal regression and the task of instance reconstruction using densified object point clouds.
Book Synopsis 3D Point Cloud Processing Using Spin Images for Object Detection by : Jason Marco K. Ligon
Download or read book 3D Point Cloud Processing Using Spin Images for Object Detection written by Jason Marco K. Ligon and published by . This book was released on 2017 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:
