Advanced Methods And Deep Learning In Computer Vision

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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 : 55,6 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

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 : 53,5 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.

Modern Deep Learning and Advanced Computer Vision

J. Nedumaan,Prof Thomas Binford,J. Lepika,J. Tisa,J. Ruby,P. S. Jagadeesh Kumar
Modern Deep Learning and Advanced Computer Vision Book PDF

Author : J. Nedumaan,Prof Thomas Binford,J. Lepika,J. Tisa,J. Ruby,P. S. Jagadeesh Kumar
Publisher : Unknown
Page : 531 pages
File Size : 49,5 Mb
Release : 2019-12-08
Category : Electronic
ISBN : 1708798641

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Modern Deep Learning and Advanced Computer Vision by J. Nedumaan,Prof Thomas Binford,J. Lepika,J. Tisa,J. Ruby,P. S. Jagadeesh Kumar Pdf

Computer vision has enormous progress in modern times. Deep learning has driven and inferred a range of computer vision problems, such as object detection and recognition, face detection and recognition, motion tracking and estimation, transfer learning, action recognition, image segmentation, semantic segmentation, robotic vision. The chapters in this book are persuaded towards the applications of advanced computer vision using modern deep learning techniques. The authors trust in making the readers with more interesting illustrations in understanding the concepts of deep learning and computer vision at a simpler perspective approach.

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 : 322 pages
File Size : 40,8 Mb
Release : 2020-03-23
Category : Computers
ISBN : 9781351003810

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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.

Multi-faceted Deep Learning

Jenny Benois-Pineau,Akka Zemmari
Multi faceted Deep Learning Book PDF

Author : Jenny Benois-Pineau,Akka Zemmari
Publisher : Springer Nature
Page : 321 pages
File Size : 44,5 Mb
Release : 2021-10-20
Category : Computers
ISBN : 9783030744786

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Multi-faceted Deep Learning by Jenny Benois-Pineau,Akka Zemmari Pdf

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

Advanced Image and Video Processing Using MATLAB

Shengrong Gong,Chunping Liu,Yi Ji,Baojiang Zhong,Yonggang Li,Husheng Dong
Advanced Image and Video Processing Using MATLAB Book PDF

Author : Shengrong Gong,Chunping Liu,Yi Ji,Baojiang Zhong,Yonggang Li,Husheng Dong
Publisher : Springer
Page : 590 pages
File Size : 51,7 Mb
Release : 2018-08-21
Category : Technology & Engineering
ISBN : 9783319772233

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Advanced Image and Video Processing Using MATLAB by Shengrong Gong,Chunping Liu,Yi Ji,Baojiang Zhong,Yonggang Li,Husheng Dong Pdf

This book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others. The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning,dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts. The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision.

Deep Learning

Rob Botwright
Deep Learning Book PDF

Author : Rob Botwright
Publisher : Rob Botwright
Page : 261 pages
File Size : 53,9 Mb
Release : 101-01-01
Category : Computers
ISBN : 9781839386251

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Deep Learning by Rob Botwright Pdf

Introducing the Ultimate AI Book Bundle: Deep Learning, Computer Vision, Python Machine Learning, and Neural Networks Are you ready to embark on an exhilarating journey into the world of artificial intelligence, deep learning, and computer vision? Look no further! Our carefully curated book bundle, "DEEP LEARNING: COMPUTER VISION, PYTHON MACHINE LEARNING AND NEURAL NETWORKS," offers you a comprehensive roadmap to AI mastery. BOOK 1 - DEEP LEARNING DEMYSTIFIED: A BEGINNER'S GUIDE 🚀 Perfect for beginners, this book dismantles the complexities of deep learning. From neural networks to Python programming, you'll build a strong foundation in AI. BOOK 2 - MASTERING COMPUTER VISION WITH DEEP LEARNING 🌟 Dive into the captivating world of computer vision. Unlock the secrets of image processing, convolutional neural networks (CNNs), and object recognition. Harness the power of visual intelligence! BOOK 3 - PYTHON MACHINE LEARNING AND NEURAL NETWORKS: FROM NOVICE TO PRO 📊 Elevate your skills with this intermediate volume. Delve into data preprocessing, supervised and unsupervised learning, and become proficient in training neural networks. BOOK 4 - ADVANCED DEEP LEARNING: CUTTING-EDGE TECHNIQUES AND APPLICATIONS 🔥 Ready to conquer advanced techniques? Learn optimization strategies, tackle common deep learning challenges, and explore real-world applications shaping the future. 🎉 What You'll Gain: · A strong foundation in deep learning · Proficiency in computer vision · Mastery of Python machine learning · Advanced deep learning skills · Real-world application knowledge · Cutting-edge AI insights 📚 Why Choose Our Book Bundle? · Expertly curated content · Beginner to expert progression · Clear explanations and hands-on examples · Comprehensive coverage of AI topics · Practical real-world applications · Stay ahead with emerging AI trends 🌐 Who Should Grab This Bundle? · Beginners eager to start their AI journey · Intermediate learners looking to expand their skill set · Experts seeking advanced deep learning insights · Anyone curious about AI's limitless possibilities 📦 Limited-Time Offer: Get all four books in one bundle and save! Don't miss this chance to accelerate your AI knowledge and skills. 🔒 Secure Your AI Mastery: Click "Add to Cart" now and embark on an educational adventure that will redefine your understanding of artificial intelligence. Your journey to AI excellence begins here!

Mastering Computer Vision with TensorFlow 2.x

Krishnendu Kar
Mastering Computer Vision with TensorFlow 2 x Book PDF

Author : Krishnendu Kar
Publisher : Packt Publishing Ltd
Page : 419 pages
File Size : 50,8 Mb
Release : 2020-05-15
Category : Computers
ISBN : 9781838826932

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Mastering Computer Vision with TensorFlow 2.x by Krishnendu Kar Pdf

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world scenariosBuild neural networks or adjust parameters to optimize the performance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into your application to operate at scaleGet up to speed with techniques for performing manual and automated image annotationWho this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities

Chakraborty, Shouvik,Mali, Kalyani
Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition Emerging Research and Opportunities Book PDF

Author : Chakraborty, Shouvik,Mali, Kalyani
Publisher : IGI Global
Page : 271 pages
File Size : 42,8 Mb
Release : 2020-03-13
Category : Computers
ISBN : 9781799827382

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Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities by Chakraborty, Shouvik,Mali, Kalyani Pdf

Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Deep Learning for Computer Vision

Jason Brownlee
Deep Learning for Computer Vision Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 564 pages
File Size : 52,5 Mb
Release : 2019-04-04
Category : Computers
ISBN : 8210379456XXX

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Deep Learning for Computer Vision by Jason Brownlee Pdf

Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

A Guide to Convolutional Neural Networks for Computer Vision

Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun
A Guide to Convolutional Neural Networks for Computer Vision Book PDF

Author : Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun
Publisher : Springer Nature
Page : 187 pages
File Size : 50,9 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031018213

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A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun Pdf

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.

Advanced Topics in Computer Vision

Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla
Advanced Topics in Computer Vision Book PDF

Author : Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla
Publisher : Springer Science & Business Media
Page : 433 pages
File Size : 43,8 Mb
Release : 2013-09-24
Category : Computers
ISBN : 9781447155201

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Advanced Topics in Computer Vision by Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla Pdf

This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. Features: investigates visual features, trajectory features, and stereo matching; reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization; presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization; examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification; describes how the four-color theorem can be used for solving MRF problems; introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule; discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video.

Deep Learning in Mining of Visual Content

Akka Zemmari,Jenny Benois-Pineau
Deep Learning in Mining of Visual Content Book PDF

Author : Akka Zemmari,Jenny Benois-Pineau
Publisher : Springer Nature
Page : 117 pages
File Size : 54,9 Mb
Release : 2020-01-22
Category : Computers
ISBN : 9783030343767

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Deep Learning in Mining of Visual Content by Akka Zemmari,Jenny Benois-Pineau Pdf

This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to computer-aided diagnostics of Alzheimer’s disease on multimodal imaging. This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.

Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Yakoub Bazi,Edoardo Pasolli
Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images Book PDF

Author : Yakoub Bazi,Edoardo Pasolli
Publisher : MDPI
Page : 438 pages
File Size : 49,5 Mb
Release : 2021-06-15
Category : Science
ISBN : 9783036509860

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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by Yakoub Bazi,Edoardo Pasolli Pdf

The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.

The The Computer Vision Workshop

Hafsa Asad,Vishwesh Ravi Shrimali,Nikhil Singh
The The Computer Vision Workshop Book PDF

Author : Hafsa Asad,Vishwesh Ravi Shrimali,Nikhil Singh
Publisher : Packt Publishing Ltd
Page : 567 pages
File Size : 40,7 Mb
Release : 2020-07-27
Category : Computers
ISBN : 9781800207141

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The The Computer Vision Workshop by Hafsa Asad,Vishwesh Ravi Shrimali,Nikhil Singh Pdf

Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems for detecting human behavior Key FeaturesUnderstand OpenCV and select the right algorithm to solve real-world problemsDiscover techniques for image and video processingLearn how to apply face recognition in videos to automatically extract key informationBook Description Computer Vision (CV) has become an important aspect of AI technology. From driverless cars to medical diagnostics and monitoring the health of crops to fraud detection in banking, computer vision is used across all domains to automate tasks. The Computer Vision Workshop will help you understand how computers master the art of processing digital images and videos to mimic human activities. Starting with an introduction to the OpenCV library, you'll learn how to write your first script using basic image processing operations. You'll then get to grips with essential image and video processing techniques such as histograms, contours, and face processing. As you progress, you'll become familiar with advanced computer vision and deep learning concepts, such as object detection, tracking, and recognition, and finally shift your focus from 2D to 3D visualization. This CV course will enable you to experiment with camera calibration and explore both passive and active canonical 3D reconstruction methods. By the end of this book, you'll have developed the practical skills necessary for building powerful applications to solve computer vision problems. What you will learnAccess and manipulate pixels in OpenCV using BGR and grayscale imagesCreate histograms to better understand image contentUse contours for shape analysis, object detection, and recognitionTrack objects in videos using a variety of trackers available in OpenCVDiscover how to apply face recognition tasks using computer vision techniquesVisualize 3D objects in point clouds and polygon meshes using Open3DWho this book is for If you are a researcher, developer, or data scientist looking to automate everyday tasks using computer vision, this workshop is for you. A basic understanding of Python and deep learning will help you to get the most out of this workshop.