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.

2D Object Detection and Recognition: Models, Algorithms and Networks. 2D Object Detection and Recognition: Models, Algorithms and Networks. Yali AmitMIT PressCambridge, Massachusetts2002ISBN 0-262-01194-8xv + 306 pp.hardback£26.95Review DOI 10.1108/03684920310493459. Keywords: Images, Algorithms, Object-oriented computing. The treatment here is somewhat more general than the title …

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

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Sofya Voigtuh

vide an efficient GPU implementation of our algorithm, and we validate its ... multi -view object recognition, where viewpoint estimates ... problem of matching 3D models to 2D images has been ex- ... and detection using convolutional networks .

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Mattio Müllers

1 Nov 2018 ... One of these challenges is in how to train models that generalize ... For example, the unseen data may differ in viewing angles, object ... At the other extreme is how the performance of recognition algorithms can be effectively scaled with ultra -large-scale ... Image 5 Relational network in target detection. TensorFlow can help you build neural network models to automatically recognize images. These are typically Convolutional Neural Networks (CNN). There are ...

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Noels Schulzen

2D Object Detection and Recognition: Models, …

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Jason Leghmann

2D Object Detection and Recognition-其它文档类资 …

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Jessica Kolhmann

At the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, ... The aforementioned network models are briefly summarized in Table 1. ... Object detection and image classification share a similar technical challenge: both of ... In early FPGA implementations, PEs are arranged in a 2D grid as a systolic array  ... Computer Vision System Toolbox provides algorithms, functions, and apps for the ... You can perform object detection and tracking, as well as feature detection, ... Pretrained models let you detect faces, pedestrians, and other common objects. ... Object Recognition: Deep Learning and Machine Learning for Computer ...