EBookClubs

Read Books & Download eBooks Full Online

EBookClubs

Read Books & Download eBooks Full Online

Book Efficient Image Based Localization Using Machine Learning Techniques

Download or read book Efficient Image Based Localization Using Machine Learning Techniques written by Ahmed Elmougi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Localization is critical for self-awareness of any autonomous system and is an important part of the autonomous system stack which consists of many phases including sensing, perceiving, planning and control. In the sensing phase, data from on board sensors are collected, preprocessed and passed to the next phase. The perceiving phase is responsible for self awareness or localization and situational awareness which includes multi-objects detection and scene understanding. After the autonomous system is aware of where it is and what is around it, it can use this knowledge to plan for the path it can take and send control commands to pursue this path. In this proposal, we focus on the localization part of the autonomous stack using camera images. We deal with the localization problem from different perspectives including single images and videos. Starting with the single image pose estimation, our approach is to propose systems that not only have good localization accuracy, but also have low space and time complexity. Firstly, we propose SurfCNN, a low cost indoor localization system that uses SURF descriptors instead of the original images to reduce the complexity of training convolutional neural networks (CNN) for indoor localization application. Given a single input image, the strongest SURF features descriptors are used as input to 5 convolutional layers to find its absolute position and orientation in arbitrary reference frame. The proposed system achieves comparable performance to the state of the art using only 300 features without the need for using the full image or complex neural networks architectures. Following, we propose SURF-LSTM, an extension to the idea of using SURF descriptors instead the original images. However, instead of CNN used in SurfCNN, we use long short term memory (LSTM) network which is one type of recurrent neural networks (RNN) to extract the sequential relation between SURF descriptors. Using SURF-LSTM, We only need 50 features to reach comparable or better results compared with SurfCNN that needs 300 features and other works that use full images with large neural networks. In the following research phase, instead of using SURF descriptors as image features to reduce the training complexity, we study the effect of using features extracted from other CNN models that were pretrained on other image tasks like image classification without further training and fine tuning. To learn the pose from pretrained features, graph neural networks (GNN) are adopted to solve the single image localization problem (Pose-GNN) by using these features representations either as features of nodes in a graph (image as a node) or converted into a graph (image as a graph). The proposed models outperform the state of the art methods on indoor localization dataset and have comparable performance for outdoor scenes. In the final stage of single image pose estimation research, we study if we can achieve good localization results without the need for training complex neural network. We propose (Linear-PoseNet) by which we can achieve similar results to the other methods based on neural networks with training a single linear regression layer on image features from pretrained ResNet50 in less than one second on CPU. Moreover, for outdoor scenes, we propose (Dense-PoseNet) that have only 3 fully connected layers trained on few minutes that reach comparable performance to other complex methods. The second localization perspective is to find the relative poses between images in a video instead of absolute poses. We extend the idea used in SurfCNN and SURF-LSTM systems and use SURF descriptors as feature representation of the images in the video. Two systems are proposed to find the relative poses between images in the video using 3D-CNN and 2DCNN-RNN. We show that using 3D-CNN is better than using the combination of CNN-RNN for relative pose estimation.

Book Efficient Image based Localization Using Context

Download or read book Efficient Image based Localization Using Context written by Charbel Azzi and published by . This book was released on 2015 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image-Based Localization (IBL) is the problem of computing the position and orientation of a camera with respect to a geometric representation of the scene. A fundamental building block of IBL is searching the space of a saved 3D representation of the scene for correspondences to a query image. The robustness and accuracy of the IBL approaches in the literature are not objective and quantifiable. First, this thesis presents a detailed description and study of three different 3D modeling packages based on SFM to reconstruct a 3D map of an environment. The packages tested are VSFM, Bundler and PTAM. The objective is to assess the mapping ability of each of the techniques and choose the best one to use for reconstructing the IBL 3D map. The study results show that image matching which is the bottleneck of SFM, SLAM and IBL plays the major role in favour of VSFM. This will result in using wrong matches in building the 3D map. It is crucial for IBL to choose the software that provides the best quality of points, \textit{i.e.} the largest number of correct 3D points. For this reason, VSFM will be chosen to reconstruct the 3D maps for IBL. Second, this work presents a comparative study of the main approaches, namely Brute Force Matching, Tree-Based Approach, Embedded Ferns Classification, ACG Localizer, Keyframe Approach, Decision Forest, Worldwide Pose Estimation and MPEG Search Space Reduction. The objective of the comparative analysis was to first uncover the specifics of each of these techniques and thereby understand the advantages and disadvantages of each of them. The testing was performed on Dubrovnik Dataset where the localization is determined with respect to a 3D cloud map which was computed using a Structure-from-Motion approach. The study results show that the current state of the art IBL solutions still face challenges in search space reduction, feature matching, clustering, and the quality of the solution is not consistent across all query images. Third, this work addresses the search space problem in order to solve the IBL problem. The Gist-based Search Space Reduction (GSSR), an efficient alternative to the available search space solutions, is proposed. It relies on GIST descriptors to considerably reduce search space and computational time, while at the same exceeding the state of the art in localization accuracy. Experiments on the 7 scenes datasets of Microsoft Research reveal considerable speedups for GSSR versus tree-based approaches, reaching a 4 times faster speed for the Heads dataset, and reducing the search space by an average of 92% while maintaining a better accuracy.

Book Efficient   Effective Image Based Localization

Download or read book Efficient Effective Image Based Localization written by Torsten Sattler and published by . This book was released on 2014-04-15 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book 2020 IEEE International Conference on Image Processing  ICIP

Download or read book 2020 IEEE International Conference on Image Processing ICIP written by IEEE Staff and published by . This book was released on 2020-10-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing ICIP 2020, the 27th in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world

Book Vision based Localization and Attitude Estimation Methods in Natural Environments

Download or read book Vision based Localization and Attitude Estimation Methods in Natural Environments written by Bertil Grelsson and published by Linköping University Electronic Press. This book was released on 2019-04-30 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, the usage of unmanned systems such as Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vessels (USVs) and Unmanned Ground Vehicles (UGVs) has increased drastically, and there is still a rapid growth. Today, unmanned systems are being deployed in many daily operations, e.g. for deliveries in remote areas, to increase efficiency of agriculture, and for environmental monitoring at sea. For safety reasons, unmanned systems are often the preferred choice for surveillance missions in hazardous environments, e.g. for detection of nuclear radiation, and in disaster areas after earthquakes, hurricanes, or during forest fires. For safe navigation of the unmanned systems during their missions, continuous and accurate global localization and attitude estimation is mandatory. Over the years, many vision-based methods for position estimation have been developed, primarily for urban areas. In contrast, this thesis is mainly focused on vision-based methods for accurate position and attitude estimates in natural environments, i.e. beyond the urban areas. Vision-based methods possess several characteristics that make them appealing as global position and attitude sensors. First, vision sensors can be realized and tailored for most unmanned vehicle applications. Second, geo-referenced terrain models can be generated worldwide from satellite imagery and can be stored onboard the vehicles. In natural environments, where the availability of geo-referenced images in general is low, registration of image information with terrain models is the natural choice for position and attitude estimation. This is the problem area that I addressed in the contributions of this thesis. The first contribution is a method for full 6DoF (degrees of freedom) pose estimation from aerial images. A dense local height map is computed using structure from motion. The global pose is inferred from the 3D similarity transform between the local height map and a digital elevation model. Aligning height information is assumed to be more robust to season variations than feature-based matching. The second contribution is a method for accurate attitude (pitch and roll angle) estimation via horizon detection. It is one of only a few methods that use an omnidirectional (fisheye) camera for horizon detection in aerial images. The method is based on edge detection and a probabilistic Hough voting scheme. The method allows prior knowledge of the attitude angles to be exploited to make the initial attitude estimates more robust. The estimates are then refined through registration with the geometrically expected horizon line from a digital elevation model. To the best of our knowledge, it is the first method where the ray refraction in the atmosphere is taken into account, which enables the highly accurate attitude estimates. The third contribution is a method for position estimation based on horizon detection in an omnidirectional panoramic image around a surface vessel. Two convolutional neural networks (CNNs) are designed and trained to estimate the camera orientation and to segment the horizon line in the image. The MOSSE correlation filter, normally used in visual object tracking, is adapted to horizon line registration with geometric data from a digital elevation model. Comprehensive field trials conducted in the archipelago demonstrate the GPS-level accuracy of the method, and that the method can be trained on images from one region and then applied to images from a previously unvisited test area. The CNNs in the third contribution apply the typical scheme of convolutions, activations, and pooling. The fourth contribution focuses on the activations and suggests a new formulation to tune and optimize a piecewise linear activation function during training of CNNs. Improved classification results from experiments when tuning the activation function led to the introduction of a new activation function, the Shifted Exponential Linear Unit (ShELU).

Book Machine Learning based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment

Download or read book Machine Learning based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment written by Xiaochun Wang and published by Springer. This book was released on 2019-08-12 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Book Machine Learning for Indoor Localization and Navigation

Download or read book Machine Learning for Indoor Localization and Navigation written by Saideep Tiku and published by Springer Nature. This book was released on 2023-06-29 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book: Provides comprehensive coverage of the application of machine learning to the domain of indoor localization; Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization; Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.

Book Computer Vision     ECCV 2024

    Book Details:
  • Author : Aleš Leonardis
  • Publisher : Springer Nature
  • Release :
  • ISBN : 3031727843
  • Pages : 590 pages

Download or read book Computer Vision ECCV 2024 written by Aleš Leonardis and published by Springer Nature. This book was released on with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book ISTFA 2019  Proceedings of the 45th International Symposium for Testing and Failure Analysis

Download or read book ISTFA 2019 Proceedings of the 45th International Symposium for Testing and Failure Analysis written by and published by ASM International. This book was released on 2019-12-01 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theme for the 2019 conference is Novel Computing Architectures. Papers will include discussions on the advent of Artificial Intelligence and the promise of quantum computing that are driving disruptive computing architectures; Neuromorphic chip designs on one hand, and Quantum Bits on the other, still in R&D, will introduce new computing circuitry and memory elements, novel materials, and different test methodologies. These novel computing architectures will require further innovation which is best achieved through a collaborative Failure Analysis community composed of chip manufacturers, tool vendors, and universities.

Book Aerial Robotic Manipulation

Download or read book Aerial Robotic Manipulation written by Anibal Ollero and published by Springer. This book was released on 2019-06-27 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aerial robotic manipulation integrates concepts and technologies coming from unmanned aerial systems and robotics manipulation. It includes not only kinematic, dynamics, aerodynamics and control but also perception, planning, design aspects, mechatronics and cooperation between several aerial robotics manipulators. All these topics are considered in this book in which the main research and development approaches in aerial robotic manipulation are presented, including the description of relevant systems. In addition of the research aspects, the book also includes the deployment of real systems both indoors and outdoors, which is a relevant characteristic of the book because most results of aerial robotic manipulation have been validated only indoor using motion tracking systems. Moreover, the book presents two relevant applications: structure assembly and inspection and maintenance, which has started to be applied in the industry. The Chapters of the book will present results of two main European Robotics Projects in aerial robotics manipulation: FP7 ARCAS and H2020 AEROARMS. FP7 ARCAS defined the basic concepts on aerial robotic manipulation, including cooperative manipulation. The H2020 AEROARMS on aerial robot with multiple arms and advanced manipulation capabilities for inspection and maintenance has two general objectives: (1) development of advanced aerial robotic manipulation methods and technologies, including manipulation with dual arms and multi-directional thrusters aerial platforms; and (2) application to the inspection and maintenance.

Book Large Scale Visual Geo Localization

Download or read book Large Scale Visual Geo Localization written by Amir R. Zamir and published by Springer. This book was released on 2016-07-05 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely and authoritative volume explores the bidirectional relationship between images and locations. The text presents a comprehensive review of the state of the art in large-scale visual geo-localization, and discusses the emerging trends in this area. Valuable insights are supplied by a pre-eminent selection of experts in the field, into a varied range of real-world applications of geo-localization. Topics and features: discusses the latest methods to exploit internet-scale image databases for devising geographically rich features and geo-localizing query images at different scales; investigates geo-localization techniques that are built upon high-level and semantic cues; describes methods that perform precise localization by geometrically aligning the query image against a 3D model; reviews techniques that accomplish image understanding assisted by the geo-location, as well as several approaches for geo-localization under practical, real-world settings.

Book Computer Vision    ECCV 2014

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.

Book Advances in Soft Computing and Machine Learning in Image Processing

Download or read book Advances in Soft Computing and Machine Learning in Image Processing written by Aboul Ella Hassanien and published by Springer. This book was released on 2017-10-13 with total page 711 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.

Book An Investigation Into Image based Indoor Localization Using Deep Learning

Download or read book An Investigation Into Image based Indoor Localization Using Deep Learning written by Qing Li and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Computer Analysis of Images and Patterns

Download or read book Computer Analysis of Images and Patterns written by Mario Vento and published by Springer Nature. This book was released on 2019-08-23 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 11678 and 11679 constitutes the refereed proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019. The 106 papers presented were carefully reviewed and selected from 176 submissions The papers are organized in the following topical sections: Intelligent Systems; Real-time and GPU Processing; Image Segmentation; Image and Texture Analysis; Machine Learning for Image and Pattern Analysis; Data Sets and Benchmarks; Structural and Computational Pattern Recognition; Posters.

Book Computer Vision     ECCV 2022

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 813 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.

Book Proceedings of the International Conference on Computational Innovations and Emerging Trends  ICCIET 2024

Download or read book Proceedings of the International Conference on Computational Innovations and Emerging Trends ICCIET 2024 written by K. Reddy Madhavi and published by Springer Nature. This book was released on 2024 with total page 1527 pages. Available in PDF, EPUB and Kindle. Book excerpt: