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Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling. Pyramid representation is a predecessor to scale-space representation and multiresolution analysis.
Pyramid is an open source web framework written in Python and is based on WSGI.It is a minimalistic web framework inspired by Zope, Pylons and Django. [4]Originally called "repoze.bfg", Pyramid gathered attention mostly in the Zope [5] and Plone community as the Open Society Institute's KARL project migrated from Plone to BFG. [6]
A tree-pyramid (T-pyramid) is a "complete" tree; every node of the T-pyramid has four child nodes except leaf nodes; all leaves are on the same level, the level that corresponds to individual pixels in the image.
The origin of the term mipmap is an initialism of the Latin phrase multum in parvo ("much in little"), and map, modeled on bitmap. [4] The term pyramids is still commonly used in a GIS context. In GIS software, pyramids are primarily used for speeding up rendering times.
The layer pyramid exhibits at one site both complex developments concerning its substructures and simplifications concerning the building methods employed for the superstructure. According to these egyptologists, the layer pyramid is a clearly advanced version of the buried pyramid of Sekhemkhet. [4] [5] [10]
Twisted is designed for complete separation between logical protocols (usually relying on stream-based connection semantics, such as HTTP or POP3) and transport layers supporting such stream-based semantics (such as files, sockets or SSL libraries). Connection between a logical protocol and a transport layer happens at the last possible moment ...
LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.
Instead of using a 4×4 grid of histogram bins, all bins extend to the center of the feature. This improves the descriptor's robustness to scale changes. The SIFT-Rank [24] descriptor was shown to improve the performance of the standard SIFT descriptor for affine feature matching. A SIFT-Rank descriptor is generated from a standard SIFT ...