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 871 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 Deep Learning Based Image Super Resolution written by Xiang Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image super resolution is one of the most significant computer vision researches aiming to reconstruct high resolution images with realistic details from low resolution images. In the past years, a number of traditional methods intended to produce high resolution images. Recently, Deep Convolutional Neural Networks (DCNNs) have developed rapidly and achieved impressive progress in the computer vision area. Benefiting from DCNNs, the performance of image super resolution has improved compared with traditional methods. However, there still exists a large gap between the results of current methods and the real-world high resolution quality. In this thesis, we leverage the techniques of DCNNs to develop image super res- olution models for generating satisfactory high resolution images. There are several proposed methods in this thesis to satisfy different super resolution scenarios. Our proposed methods are based on Generative Adversarial Networks (GANs), leading to powerful generative ability and effective discriminative learning. To breakthrough current bottlenecks, we design novel architectures for generator and discriminator, and involve new optimization strategies to improve the learning stability of the mod- els. In order to improve the generalization ability of proposed methods, we conduct two mainstream super resolution tasks, namely face image hallucination and natu- ral image super resolution. All the proposed components of our methods result in promising super resolution performance for these tasks. Not only handling the supervised super resolution task, we also investigate the more challenging problem, namely the unsupervised image super resolution task where the paired high resolution image and low resolution image data are unavailable. To evaluate the performance of our methods in different scenarios, we conduct exten- sive experiments on several benchmark datasets to study each method separately. Compared to state-of-the-art methods, our methods are able to achieve superior per- formance both quantitatively and qualitatively.
Download or read book Deep Learning Based Image Super resolution with Adaption and Extension of Convolutional Neural Network Models written by Ha Viet Khanh and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Example Based Super Resolution written by Jordi Salvador and published by Academic Press. This book was released on 2016-09-22 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Example-Based Super Resolution provides a thorough introduction and overview of example-based super resolution, covering the most successful algorithmic approaches and theories behind them with implementation insights. It also describes current challenges and explores future trends. Readers of this book will be able to understand the latest natural image patch statistical models and the performance limits of example-based super resolution algorithms, select the best state-of-the-art algorithmic alternative and tune it for specific use cases, and quickly put into practice implementations of the latest and most successful example-based super-resolution methods. - Provides detailed coverage of techniques and implementation details that have been successfully introduced in diverse and demanding real-world applications - Covers a wide variety of machine learning approaches, ranging from cross-scale self-similarity concepts and sparse coding, to the latest advances in deep learning - Presents a statistical interpretation of the subspace of natural image patches that transcends super resolution and makes it a valuable source for any researcher on image processing or low-level vision
Download or read book Advances in Brain Inspired Cognitive Systems written by Jinchang Ren 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: This book constitutes the refereed proceedings of the 9th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2018, held in Xi’an, China, in July 2018. The 83 papers presented in this volume were carefully reviewed and selected from 137 submissions. The papers were organized in topical sections named: neural computation; biologically inspired systems; image recognition: detection, tracking and classification; data analysis and natural language processing; and applications.
Download or read book UAV Photogrammetry and Remote Sensing written by Fernando Carvajal-Ramírez and published by MDPI. This book was released on 2021-09-06 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites. The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained. More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products. This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry.
Download or read book Pattern Recognition written by Gerhard Rigoll and published by Springer. This book was released on 2008-06-29 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 30th Symposium of the German Association for Pattern Recognition, DAGM 2008, held in Munich, Germany, in June 2008. The 53 revised full papers were carefully reviewed and selected from 136 submissions. The papers are organized in topical sections on learning and classification, tracking, medical image processing and segmentation, audio, speech and handwriting recognition, multiview geometry and 3D-reconstruction, motion and matching, and image analysis.
Download or read book Computational Intelligence Methods for Super Resolution in Image Processing Applications written by Anand Deshpande and published by Springer Nature. This book was released on 2021-05-28 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.
Download or read book 2020 IEEE 8th Conference on Systems Process and Control ICSPC written by IEEE Staff and published by . This book was released on 2020-12-11 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference will provide an excellent platform for knowledge exchange between researchers working in areas of listed below In addition, it provides an opportunity for the participants from Malaysia and overseas to share research findings and establish network and collaborations
Download or read book Deep Learning Based Image Processing written by Ying Liu and published by Eliva Press. This book was released on 2022-09-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning enables a model constituted by multiple processing layers to learn the data representation with multiple levels of abstraction. In the past decade, deep learning has brought remarkable achievements in many fields of machine learning and pattern recognition, especially in image processing. The state-of-the-art performance in image super-resolution reconstruction, image classification, target detection, image retrieval and other image processing tasks have been greatly improved. This book introduces these image processing technologies based on deep learning, including recent advances, applications in real scenes and future trends. The first chapter introduces image super-resolution reconstruction, which aims to recover high-resolution images from corresponding low-resolution versions. This chapter reviews these image super-resolution methods based on convolutional neural networks and generative adversarial networks on account of internal network structure. The second chapter presents four categories of few-shot image classification algorithms: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. In the third chapter, deep learning based models for small target detection in video are summarized in detail, which are categorized into one-stage models and two-stage models according to the detection stages. The network structures and plug-in modules for video based small target detection are also explained. The fourth chapter discusses deep learning based cross-modal hashing for image retrieval methods, including the extraction of high-level semantic information and the maintenance of similarity between different mo
Download or read book A Guide to Convolutional Neural Networks for Computer Vision written by Salman Khan and published by Morgan & Claypool Publishers. This book was released on 2018-02-13 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-07 with total page 831 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 Image Fusion written by Gang Xiao and published by Springer Nature. This book was released on 2020-08-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
Download or read book Deep Learning for Computer Vision written by Rajalingappaa Shanmugamani and published by Packt Publishing Ltd. This book was released on 2018-01-23 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.
Download or read book Pattern Recognition and Artificial Intelligence written by Yue Lu and published by Springer Nature. This book was released on 2020-10-09 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Second International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, which took place in Zhongshan, China, in October 2020. The 49 full and 14 short papers presented were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: handwriting and text processing; features and classifiers; deep learning; computer vision and image processing; medical imaging and applications; and forensic studies and medical diagnosis.
Download or read book Artificial Intelligence Science And Technology Proceedings Of The 2016 International Conference Aist2016 written by Hui Yang and published by #N/A. This book was released on 2017-06-28 with total page 845 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016) was held in Shanghai, China, from 15th to 17th July, 2016.AIST2016 aims to bring together researchers, engineers, and students to the areas of Artificial Intelligence Science and Technology. AIST2016 features unique mixed topics of artificial intelligence and application, computer and software, communication and network, information and security, data mining, and optimization.This volume consists of 101 peer-reviewed articles by local and foreign eminent scholars which cover the frontiers and state-of-art development in AI Technology.
Download or read book Deep Learning for Image Processing Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2017-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
