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Aircraft Detection by Deep Belief Nets

https://doi.org/10.1109/acpr.2013.5

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft

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Using cross-ratios to model curve data for aircraft recognition

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Aircraft type recognition in satellite images

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Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

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Fusion of intelligent agents for the detection of aircraft in SAR images

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Histograms of Oriented Gradients for Human Detection

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Deep belief nets for natural language call-routing

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Robust airplane detection in satellite images

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Co-segmentation of aircrafts from high-resolution satellite images

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Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model

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Aircraft Recognition in High-Resolution Satellite Images Using Coarse-to-Fine Shape Prior

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Detection of stationary aircrafts from satelitte images

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Entropy-Balanced Bitmap Tree for Shape-Based Object Retrieval From Large-Scale Satellite Imagery Databases

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Vehicle Detection Using Partial Least Squares

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Reducing the Dimensionality of Data with Neural Networks

inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot

A Fast Learning Algorithm for Deep Belief Nets

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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