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Toward Category-Level Object Recognition

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Release : 2007-01-25
Genre : Computers
Kind : eBook
Book Rating : 955/5 ( reviews)

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Book Synopsis Toward Category-Level Object Recognition by : Jean Ponce

Download or read book Toward Category-Level Object Recognition written by Jean Ponce. This book was released on 2007-01-25. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.

Object Categorization

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Release : 2009-09-07
Genre : Computers
Kind : eBook
Book Rating : 380/5 ( reviews)

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Book Synopsis Object Categorization by : Sven J. Dickinson

Download or read book Object Categorization written by Sven J. Dickinson. This book was released on 2009-09-07. Available in PDF, EPUB and Kindle. Book excerpt: A unique multidisciplinary perspective on the problem of visual object categorization.

Visual Object Recognition

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Release : 2022-05-31
Genre : Computers
Kind : eBook
Book Rating : 533/5 ( reviews)

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Book Synopsis Visual Object Recognition by : Kristen Thielscher

Download or read book Visual Object Recognition written by Kristen Thielscher. This book was released on 2022-05-31. Available in PDF, EPUB and Kindle. Book excerpt: 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

Visual Object Recognition

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Author :
Release : 2011
Genre : Computers
Kind : eBook
Book Rating : 689/5 ( reviews)

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Book Synopsis Visual Object Recognition by : Kristen Grauman

Download or read book Visual Object Recognition written by Kristen Grauman. This book was released on 2011. Available in PDF, EPUB and Kindle. Book excerpt: 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

Practical Machine Learning for Computer Vision

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Release : 2021-07-21
Genre : Computers
Kind : eBook
Book Rating : 339/5 ( reviews)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan. This book was released on 2021-07-21. Available in PDF, EPUB and Kindle. Book excerpt: 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

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