Transparent Object Recognition And Detection Using Deep Neural Networks With Rgb D Images

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Transparent Object Recognition And Detection Using Deep Neural Networks With Rgb D Images

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Author : Andreas Barthen
language : en
Publisher:
Release Date : 2015

Transparent Object Recognition And Detection Using Deep Neural Networks With Rgb D Images written by Andreas Barthen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.

Rgb D Object Recognition Using Deep Convolutional Neural Networks

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Author : Saman Zia
language : en
Publisher:
Release Date : 2016

Rgb D Object Recognition Using Deep Convolutional Neural Networks written by Saman Zia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computer vision categories.

Object Detection By Stereo Vision Images

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Author : R. Arokia Priya
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-14

Object Detection By Stereo Vision Images written by R. Arokia Priya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-14 with Computers categories.

OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

Object Detection With Deep Learning Models

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Author : S Poonkuntran
language : en
Publisher: CRC Press
Release Date : 2022-11-01

Object Detection With Deep Learning Models written by S Poonkuntran and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-01 with Computers categories.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Visual Object Tracking With Deep Neural Networks

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Author : Pier Luigi Mazzeo
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-12-18

Visual Object Tracking With Deep Neural Networks written by Pier Luigi Mazzeo and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-18 with Computers categories.

Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Deep Learning In Object Recognition Detection And Segmentation

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Author : Xiaogang Wang
language : en
Publisher:
Release Date : 2016

Deep Learning In Object Recognition Detection And Segmentation written by Xiaogang Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Machine learning categories.

As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.

Deep Learning In Object Detection And Recognition

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Author : Xiaoyue Jiang
language : en
Publisher: Springer
Release Date : 2018-09-11

Deep Learning In Object Detection And Recognition written by Xiaoyue Jiang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-11 with Computers categories.

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Object Detection And Recognition In Digital Images

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Author : Boguslaw Cyganek
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-20

Object Detection And Recognition In Digital Images written by Boguslaw Cyganek and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-20 with Science categories.

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

Object Detection

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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2024-05-04

Object Detection written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-04 with Computers categories.

What is Object Detection The field of computer technology known as object detection is closely associated with computer vision and image processing. Its primary objective is to identify instances of semantic objects belonging to a specific class inside digital images and videos. In the field of object detection, face detection and pedestrian detection are two areas that have received extensive attention. Object detection is useful in a wide variety of computer vision applications, including image retrieval and video surveillance, among others. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Object detection Chapter 2: Computer vision Chapter 3: Image segmentation Chapter 4: Template matching Chapter 5: Optical braille recognition Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: DeepDream Chapter 9: Saliency map Chapter 10: Small object detection (II) Answering the public top questions about object detection. (III) Real world examples for the usage of object detection in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Object Detection.

Frontiers Of Computer Vision

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Author : Kazuhiko Sumi
language : en
Publisher: Springer Nature
Release Date : 2022-05-16

Frontiers Of Computer Vision written by Kazuhiko Sumi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-16 with Computers categories.

This book constitutes refereed proceedings of the 28th International Workshop on Frontiers of Computer Vision, IW-FCV 2022, held in Hiroshima, Japan, in February 2022. Due to the COVID-19 pandemic the conference was held online. The 24 full papers presented in this volume were thoroughly reviewed and selected from 63 submissions. The papers are organized according to the following topics: ​camera, 3D, and imaging; learning algorithm; object detection/segmentation; recognition/generation.