2D Object Detection and Recognition. Models, Algorithms, and Networks.pdf

2D Object Detection and Recognition. Models, Algorithms, and Networks PDF

Yali Amit

Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency. The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse-to-fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.

Humans can perform object recognition and semantic segmentation pretty well even in photographs or on a computer screen where we don’t have depth data. Infact we can do this even when those photographs grossly disobey scale relationships. Anyways,

6.34 MB Taille du fichier
9780262011945 ISBN
2D Object Detection and Recognition. Models, Algorithms, and Networks.pdf

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Sofya Voigtuh

2d objects detention and recognition - Y.Amit - Librairie ...

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Mattio Müllers

2D Object Detection and Recognition: Models, Algorithms, and Networks [Amit, Yali] on Amazon.com. *FREE* shipping on qualifying offers. 2D Object Detection and Recognition: Models, Algorithms, … 2D Object Detection and Recognition | The MIT Press

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Noels Schulzen

Tutorial: Algorithms For 2-Dimensional Object Recognition.

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Jason Leghmann

Deep Exemplar 2D-3D Detection by Adapting from Real to ... Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views rendered from CAD models and real images; and (ii) 2D-based object detection has benefited from recent successes of convolutional neural networks (CNNs) [31, 32]. This work Universit´e Paris-Est, LIGM (UMR CNRS 8049), ENPC, F-77455 Marne-la-Valle. This work was carried out in IMAGINE, a joint research project between

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Jessica Kolhmann

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