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 Performance Model Analysis of Human Performance in IR Target Recognition

1998
Visual Performance Model Analysis of Human Performance in IR Target Recognition

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  • Author: Frederick Smith
  • Publisher:
  • ISBN:
  • Category :
  • Languages : en
  • Pages : 26

This technical report describes the application of a human-visual system simulation model, a computational vision model, as a method for prediction of operator target detection or recognition performance with infrared imaging sensors. The present report correlates the simulation model's predictions with laboratory data from human observers. The eventual goal of this work is to develop a methodology that will allow reliable predictions of imaging sensor system performance without the need for repeated laboratory testing with human observers. In this report, the visual performance model (VPM) has been applied to a set of airborne, 1st generation FLIR imagery of mobile ground targets (including Scud-B mobile transporter-erector-launchers TELs). The detectability/recognizability metrics (d' values) obtained from VPM have been compared with similar laboratory data obtained using human operators. Good correlations between the VPM detectability/recognizability predictions and the human operator results were obtained during the present study (i.e., r values greater than .70). Future efforts are planned to examine VPM's utility for camouflaged targets and for 3rd generation FLIR imagery.

Target Detection Through Visual Recognition; a Quantitative Model [by] H. H. Bailey

Target Detection Through Visual Recognition; a Quantitative Model [by] H. H. Bailey

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  • Author: H H. Bailey
  • Publisher:
  • ISBN:
  • Category : Target practice
  • Languages : en
  • Pages : 27

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

Automatic Target Recognition

2007
Automatic Target Recognition

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  • Author:
  • Publisher:
  • ISBN:
  • Category : Image processing
  • Languages : en
  • Pages : 438

Target Detection Through Visual Recognition

1970
Target Detection Through Visual Recognition

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  • Author: H. H. Bailey
  • Publisher:
  • ISBN:
  • Category : Target practice
  • Languages : en
  • Pages : 27

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

Advanced Methods and Deep Learning in Computer Vision

2021-11-09
Advanced Methods and Deep Learning in Computer Vision

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  • Author: E. R. Davies
  • Publisher: Academic Press
  • ISBN: 0128221496
  • Category : Computers
  • Languages : en
  • Pages : 584

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

Night Vision Processing and Understanding

2019-01-11
Night Vision Processing and Understanding

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  • Author: Lianfa Bai
  • Publisher: Springer
  • ISBN: 9811316694
  • Category : Computers
  • Languages : en
  • Pages : 266

This book systematically analyses the latest insights into night vision imaging processing and perceptual understanding as well as related theories and methods. The algorithm model and hardware system provided can be used as the reference basis for the general design, algorithm design and hardware design of photoelectric systems. Focusing on the differences in the imaging environment, target characteristics, and imaging methods, this book discusses multi-spectral and video data, and investigates a variety of information mining and perceptual understanding algorithms. It also assesses different processing methods for multiple types of scenes and targets.Taking into account the needs of scientists and technicians engaged in night vision optoelectronic imaging detection research, the book incorporates the latest international technical methods. The content fully reflects the technical significance and dynamics of the new field of night vision. The eight chapters cover topics including multispectral imaging, Hadamard transform spectrometry; dimensionality reduction, data mining, data analysis, feature classification, feature learning; computer vision, image understanding, target recognition, object detection and colorization algorithms, which reflect the main areas of research in artificial intelligence in night vision. The book enables readers to grasp the novelty and practicality of the field and to develop their ability to connect theory with real-world applications. It also provides the necessary foundation to allow them to conduct research in the field and adapt to new technological developments in the future.

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.