Vision Models for Target Detection and Recognition

1995
Vision Models for Target Detection and Recognition

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  • Author: Eli Peli
  • Publisher: World Scientific
  • ISBN: 9789810221492
  • Category : Technology & Engineering
  • Languages : en
  • Pages : 438

This book is an international collection of contributions from academia, industry and the armed forces. It addresses current and emerging Spatial Vision Models and their application to the understanding, prediction and evaluation of the tasks of target detection and recognition. The discussion in many of the chapters is framed in terms of military targets and military vision aids. However, the techniques analyses and problems are by no means limited to this area of application. The detection and recognition of an armored vehicle from a reconnaissance image are performed by the same visual system used to detect and recognize a tumor in an X-ray. The analysis of the interaction of the human visual system with night vision devices is not different from the analysis needed in the case of an operator examining structures using a remote (endoscopic) camera, etc. The book is organized into three general sections. The first covers basic modeling of central (foveal) vision and its theoretical background. The second is centered on the evaluation of model performance in applications, while the third is dedicated to aspects of peripheral vision modeling and the expansion of peripheral modeling to include visual search.

Visual Object Recognition

2011
Visual Object Recognition

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  • Author: Kristen Grauman
  • Publisher: Morgan & Claypool Publishers
  • ISBN: 1598299689
  • Category : Computers
  • Languages : en
  • Pages : 184

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

Handbook Of Pattern Recognition And Computer Vision (2nd Edition)

1999-03-12
Handbook Of Pattern Recognition And Computer Vision (2nd Edition)

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  • Author: Chi Hau Chen
  • Publisher: World Scientific
  • ISBN: 9814497649
  • Category : Computers
  • Languages : en
  • Pages : 1045

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

Millimeter Wave Radar

1980
Millimeter Wave Radar

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  • Author: Stephen L. Johnston
  • Publisher:
  • ISBN:
  • Category : Technology & Engineering
  • Languages : en
  • Pages : 686

Deep Learning in Object Detection and Recognition

2020-11-27
Deep Learning in Object Detection and Recognition

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  • Author: Xiaoyue Jiang
  • Publisher: Springer
  • ISBN: 9789811506512
  • Category : Computers
  • Languages : en
  • Pages : 0

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.

Deep Learning for Computer Vision

2019-04-04
Deep Learning for Computer Vision

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  • Author: Jason Brownlee
  • Publisher: Machine Learning Mastery
  • ISBN:
  • Category : Computers
  • Languages : en
  • Pages : 564

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

Practical Machine Learning for Computer Vision

2021-07-21
Practical Machine Learning for Computer Vision

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  • Author: Valliappa Lakshmanan
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1098102339
  • Category : Computers
  • Languages : en
  • Pages : 481

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Intelligent Data Mining and Fusion Systems in Agriculture

2019-10-08
Intelligent Data Mining and Fusion Systems in Agriculture

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  • Author: Xanthoula-Eirini Pantazi
  • Publisher: Academic Press
  • ISBN: 0128143924
  • Category : Business & Economics
  • Languages : en
  • Pages : 334

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. - Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture - Addresses AI use in weed management, disease detection, yield prediction and crop production - Utilizes case studies to provide real-world insights and direction

Template Matching Techniques in Computer Vision

2009-04-29
Template Matching Techniques in Computer Vision

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  • Author: Roberto Brunelli
  • Publisher: John Wiley & Sons
  • ISBN: 9780470744048
  • Category : Science
  • Languages : en
  • Pages : 348

The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms. Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.

Machine Learning for Cyber Security

2020-11-10
Machine Learning for Cyber Security

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  • Author: Xiaofeng Chen
  • Publisher: Springer Nature
  • ISBN: 3030624633
  • Category : Computers
  • Languages : en
  • Pages : 554

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.