object detection

Computer Vision Object Detection In Adversarial Vision【電子書籍】 Mrinal Kanti BhowmikObject Detection Advances, Applications, and Algorithms【電子書籍】 Fouad SabryMoving Object Detection Using Background Subtraction【電子書籍】 Soharab Hossain ShaikhAsteroid, Comet, and Near Earth Object (NEO) Encyclopedia: Sweeping Coverage of Impact Threats, Spacecraft Research, Detection, Deflection, Mitigation, Tunguska, Chelyabinsk, Planetary Defense, PHAs【電子書籍】 Progressive ManagementAdvanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition【電子書籍】 Rowel AtienzaObject Detection and Recognition in Digital Images Theory and Practice【電子書籍】 Boguslaw CyganekAdvanced Applied Deep Learning Convolutional Neural Networks and Object Detection【電子書籍】 Umberto MichelucciPerformance Evaluation Software Moving Object Detection and Tracking in Videos【電子書籍】 Bahadir KarasuluElements of Deep Learning for Computer Vision Explore Deep Neural Network Architectures, PyTorch, Object Detection Algorithms, and Computer Vision Applications for Python Coders (English Edition)【電子書籍】 Bharat SikkaElements of Deep Learning for Computer Vision: Explore Deep Neural Network Architectures, PyTorch, Object Detection Algorithms, and Computer Vision Applications for Python Coders (English Edition)【電子書籍】 Bharat SikkaFoundations of Computer Vision Computational Geometry, Visual Image Structures and Object Shape Detection【電子書籍】 James F. PetersDeep Learning for Crack-Like Object Detection【電子書籍】 Kaige Zhang洋書 Paperback, Geometric Constraints for Object Detection and Delineation (The Springer International Series in Engineering and Computer Science)Mastering the Microsoft Kinect Body Tracking, Object Detection, and the Azure Cloud Services【電子書籍】 Vangos Pterneas洋書 Paperback, Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd EditionTensorFlowはじめました3 Object Detection ─ 物体検出【電子書籍】 有山 圭二Deep Learning in Object Detection and Recognition【電子書籍】Learn OpenCV 4.5 with Python 3.7 by Examples Implement Computer Vision Algorithms Provided by OpenCV with Python for Image Processing, Object Detection and Machine Learning【電子書籍】 James Chen洋書 Paperback, Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using PythonLearn OpenCV with Python by Examples Implement Computer Vision Algorithms Provided by OpenCV with Python for Image Processing, Object Detection and Machine Learning【電子書籍】 James Chen
 

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  • <p>This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of</p> <p>objects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility e...
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  • <p><strong>What is Object Detection</strong></p> <p>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.</p> <p><strong>How you will benefit</strong></p> <p>(I) Insights, and validations about the following topics:</p> <p>Chapter 1: Object detection</p> <p>Chapter 2: Computer vision</p> <p>Chapter 3: Image segmentation</p> <p>Chapter 4: Template matching</p> <p>Chapter 5: Optical braille recognition</p> <p>Chapter 6: Deep learning</p> <p>Chapter 7: Convolutional neural n...
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  • <p>This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.</p>画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、...
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  • <p>Discover all aspects of asteroids, comets, near earth objects (NEOs), and their impact threat to Earth in this massive, authoritative compilation of up-to-date official documents from NASA and other federal sources, with details about current tracking, detection, and survey efforts, spacecraft inspections of asteroids and comets, concepts for deflection of hazardous objects, and much more. There is coverage of the events of February 15, 2013, which were a stark reminder of the threat posed by space objects impacting Earth. The predicted close approach of a small asteroid, called 2012 DA14, and the unpredicted entry and explosion of a very small asteroid about 15 miles above Russia, have focused attention on the necessity of tracking asteroids and other NEOs and protecting our planet from them.</p> <p>Contents include: 1. House and Senate Hearings on Asteroid and Space Threats, March 2013 * 2. Basic Overview * 3. Asteroids * 4. Comets * 5. Tunguska, Planetary Defense * 6. NA...
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  • <p><strong>Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras</strong></p> <h4>Key Features</h4> <ul> <li>Explore the most advanced deep learning techniques that drive modern AI results</li> <li>New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation</li> <li>Completely updated for TensorFlow 2.x</li> </ul> <h4>Book Description</h4> <p>Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.</p> <p>Using Keras as...
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  • <p>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.</p> <p>Key features:</p> <ul> <li>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.</li> <li>Places an emphasis on tensor and statistical based approaches within object detection and recognition.</li> <li>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.</li> <li>Contains numerous case study examples of ma...
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  • <p>Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In <em>Advanced Applied Deep Learning</em>, you will study advanced topics on CNN and object detection using Keras and TensorFlow.</p> <p>Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.</p> <p>Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the boo...
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  • <p><em>Performance Evaluation Software: Moving Object Detection and Tracking in Videos</em> introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS), Optical Flow (OF), etc. An important problem for performance evaluation is the absence of any stable and flexible software for comparison of different algorithms. In this frame, we have designed and implemented the software for comparing and evaluating the well-known video object D&T algorithms on the same platform. This software is able to compare them with the same metrics i...
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  • <p>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-eq...
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  • <p>Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries.</p> <p>KEY FEATURES</p> <p>● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN.</p> <p>● Includes graphical representations and illustrations of neural networks and teaches how to program them.</p> <p>● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford.</p> <p>DESCRIPTION</p> <p>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.</p> <p>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 ...
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  • <p>This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classi?cation of image regions. Algorithms provide a practical, step-by-step means of viewing image structures.</p> <p>The implementations of CV methods in Matlab and Mathematica, classi?cation of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, exampl...
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  • <p>Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems.</p> <p>This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which ...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>Know how to program the Microsoft Kinect and use the device for applications that interact directly with humans through gestures and motion. This book covers the mathematics and theoretical background needed for depth sensing, motion tracking, and object recognition while maintaining a practical focus on getting things done. You will learn to track the human body in three-dimensional space, analyze the human motion, and remove the background to isolate the person being tracked. You will see how to recognize objects and voice, and transform between the three-dimensional physical space and a computer’s two-dimensional screen.</p> <p>The book is written with real-world applications in mind. It provides step-by-step tutorials and source code for common use cases. The author has worked with startups and Fortune 500 companies, and all of the examples are taken directly from the industry. The book’s practical focus simplifies the core principles, removes the clutter, and allows de...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>【大好評「TensorFlowはじめました」シリーズ最新刊!】</p> <p>本書は、TensorFlowがオープンソースで公開されるまで「機械学習」に触れたことがなかった筆者が、TensorFlowを通じて機械学習に挑戦して、七転八倒した成果をまとめた「TensorFlowはじめました」シリーズの第三弾です。今回は画像の中から物体(イラストなら「顔」の部分など)を検出する「物体検出」を題材に、畳込みニューラルネットワークモデルの学習と評価・検証を行っています。<br /> 【目次】<br /> 第1章 TensorFlowの基礎<br />  1.1 TensorFlowとは<br />  1.2 データフローグラフ<br />  1.3 テンソル(Tensor)<br />  1.4 変数とプレースホルダー<br />  1.5 演算子のオーバーロード<br />  1.6 ブロードキャスティング<br /> 第2章 グリッドベースの物体検出<br />  2.1 物体検出とは<br />  2.2 モデルの定義<br />  2.3 データセットの作成<br />  2.4 学習(訓練)<br />  2.5 検証<br /> 第3章 物体認識奮闘記<br />  3.1 確信度と座標<br />  3.2 畳み込み層<br />  3.3 モデルの定義<br />  3.4 デ...
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  • <p>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.</p> <p>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.</p>画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします...
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  • <p><strong>What This Book is About</strong></p> <p>When you searched for this book, you have already known the importance of the OpenCV/Python in the fields of computer vision, image processing and machine learning. This book begins with step-by-step instructions of installation as well as a simple Hello World, then gets into the OpenCV Basics, Image Processing, Object Detection and finally Machine Learning.</p> <p><strong>Key Features</strong></p> <p>Example for every topic, all the source codes are available in Github.</p> <p>Line by line explanation of the source codes.</p> <p>Focus mainly on implementation of algorithms, rather than mathematical theories.</p> <p><strong>Whom This Book Is For</strong></p> <p>This book is for people with a variety of computer programming levels, from those with very limited knowledge of computer vision to the experienced ones. The readers do not need to have previous experiences of Python/OpenCV. No matter...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>This book is a comprehensive guide to learning the basics of computer vision and machine learning using the powerful OpenCV library and the Python programming language. The book offers a practical, hands-on approach to learning the concepts and techniques of computer vision through practical examples. All codes in this book are available on GitHub.</p> <p>Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning, and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets demonstrating how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image-processing applications from scratch.</p> <p>Whether you are a beginner or an experienced programmer, this book provides a valuable res...
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