Advancement Of Deep Learning And Its Applications In Object Detection And Recognition

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Advancement of Deep Learning and its Applications in Object Detection and Recognition

Roohie Naaz Mir,Vipul Kumar Sharma,Ranjeet Kumar Rout,Saiyed Umer
Advancement of Deep Learning and its Applications in Object Detection and Recognition Book PDF

Author : Roohie Naaz Mir,Vipul Kumar Sharma,Ranjeet Kumar Rout,Saiyed Umer
Publisher : CRC Press
Page : 319 pages
File Size : 51,9 Mb
Release : 2023-05-10
Category : Computers
ISBN : 9781000880410

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Advancement of Deep Learning and its Applications in Object Detection and Recognition by Roohie Naaz Mir,Vipul Kumar Sharma,Ranjeet Kumar Rout,Saiyed Umer Pdf

Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.

Object Detection with Deep Learning Models

S Poonkuntran,Rajesh Kumar Dhanraj,Balamurugan Balusamy
Object Detection with Deep Learning Models Book PDF

Author : S Poonkuntran,Rajesh Kumar Dhanraj,Balamurugan Balusamy
Publisher : CRC Press
Page : 345 pages
File Size : 47,9 Mb
Release : 2022-11-01
Category : Computers
ISBN : 9781000686791

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Object Detection with Deep Learning Models by S Poonkuntran,Rajesh Kumar Dhanraj,Balamurugan Balusamy Pdf

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

Deep Learning in Object Detection and Recognition

Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Deep Learning in Object Detection and Recognition Book PDF

Author : Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Publisher : Springer
Page : 0 pages
File Size : 44,9 Mb
Release : 2020-11-27
Category : Computers
ISBN : 9811506515

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Deep Learning in Object Detection and Recognition by Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng Pdf

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.

Advances and Applications in Deep Learning

Anonim
Advances and Applications in Deep Learning Book PDF

Author : Anonim
Publisher : BoD – Books on Demand
Page : 124 pages
File Size : 42,5 Mb
Release : 2020-12-09
Category : Computers
ISBN : 9781839628788

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Advances and Applications in Deep Learning by Anonim Pdf

Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike.

Deep Learning in Object Recognition, Detection, and Segmentation

Xiaogang Wang
Deep Learning in Object Recognition Detection and Segmentation Book PDF

Author : Xiaogang Wang
Publisher : Unknown
Page : 165 pages
File Size : 52,8 Mb
Release : 2016
Category : Machine learning
ISBN : 1680831178

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Deep Learning in Object Recognition, Detection, and Segmentation by Xiaogang Wang Pdf

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.

Elements of Deep Learning for Computer Vision

Bharat Sikka
Elements of Deep Learning for Computer Vision Book PDF

Author : Bharat Sikka
Publisher : BPB Publications
Page : 224 pages
File Size : 44,7 Mb
Release : 2021-06-24
Category : Computers
ISBN : 9789390684687

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Elements of Deep Learning for Computer Vision by Bharat Sikka Pdf

Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World

Deep Learning for Computer Vision

Rajalingappaa Shanmugamani
Deep Learning for Computer Vision Book PDF

Author : Rajalingappaa Shanmugamani
Publisher : Packt Publishing Ltd
Page : 304 pages
File Size : 54,9 Mb
Release : 2018-01-23
Category : Computers
ISBN : 9781788293358

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Deep Learning for Computer Vision by Rajalingappaa Shanmugamani Pdf

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.

Object Detection

Fouad Sabry
Object Detection Book PDF

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
Page : 159 pages
File Size : 45,5 Mb
Release : 2024-05-04
Category : Computers
ISBN : PKEY:6610000560134

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Object Detection by Fouad Sabry Pdf

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.

Deep Learning for Image Processing Applications

D.J. Hemanth,V. Vieira Estrela
Deep Learning for Image Processing Applications Book PDF

Author : D.J. Hemanth,V. Vieira Estrela
Publisher : IOS Press
Page : 284 pages
File Size : 45,9 Mb
Release : 2017-12
Category : Computers
ISBN : 9781614998228

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Deep Learning for Image Processing Applications by D.J. Hemanth,V. Vieira Estrela Pdf

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.

Advanced Methods and Deep Learning in Computer Vision

E. R. Davies,Matthew Turk
Advanced Methods and Deep Learning in Computer Vision Book PDF

Author : E. R. Davies,Matthew Turk
Publisher : Academic Press
Page : 584 pages
File Size : 47,9 Mb
Release : 2021-11-09
Category : Computers
ISBN : 9780128221495

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Advanced Methods and Deep Learning in Computer Vision by E. R. Davies,Matthew Turk Pdf

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field Illustrates principles with modern, real-world applications Suitable for self-learning or as a text for graduate courses

Concepts and Real-Time Applications of Deep Learning

Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora
Concepts and Real Time Applications of Deep Learning Book PDF

Author : Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora
Publisher : Springer Nature
Page : 212 pages
File Size : 51,9 Mb
Release : 2021-09-23
Category : Technology & Engineering
ISBN : 9783030761677

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Concepts and Real-Time Applications of Deep Learning by Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora Pdf

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Deep Learning Applications

M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh
Deep Learning Applications Book PDF

Author : M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh
Publisher : Springer Nature
Page : 184 pages
File Size : 43,7 Mb
Release : 2020-02-28
Category : Technology & Engineering
ISBN : 9789811518164

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Deep Learning Applications by M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh Pdf

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Deep Learning Research Applications for Natural Language Processing

Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S.
Deep Learning Research Applications for Natural Language Processing Book PDF

Author : Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S.
Publisher : IGI Global
Page : 313 pages
File Size : 55,6 Mb
Release : 2022-12-09
Category : Computers
ISBN : 9781668460030

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Deep Learning Research Applications for Natural Language Processing by Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S. Pdf

Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.

Deep Learning in Computer Vision

Mahmoud Hassaballah,Ali Ismail Awad
Deep Learning in Computer Vision Book PDF

Author : Mahmoud Hassaballah,Ali Ismail Awad
Publisher : CRC Press
Page : 261 pages
File Size : 46,8 Mb
Release : 2020-03-23
Category : Computers
ISBN : 9781351003803

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Deep Learning in Computer Vision by Mahmoud Hassaballah,Ali Ismail Awad Pdf

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Deep Learning Applications, Volume 3

M. Arif Wani,Bhiksha Raj,Feng Luo,Dejing Dou
Deep Learning Applications Volume 3 Book PDF

Author : M. Arif Wani,Bhiksha Raj,Feng Luo,Dejing Dou
Publisher : Springer Nature
Page : 328 pages
File Size : 49,8 Mb
Release : 2021-11-12
Category : Technology & Engineering
ISBN : 9789811633577

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Deep Learning Applications, Volume 3 by M. Arif Wani,Bhiksha Raj,Feng Luo,Dejing Dou Pdf

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.