Image Segmentation Phd Thesis 2010

Image Segmentation Phd Thesis 2010-3
Ulfarsson, Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields, IEEE Trans. Benediktsson, Integration of Segmentation Techniques for Classification of Hyperspectral Images, IEEE Geosci. You may download the data from the following address: https://figshare.com/articles/Main_zip/2007723 P.

Ulfarsson, Spectral-Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields, IEEE Trans.

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Benediktsson, “A Novel Feature Selection Approach Based on FODPSO and SVM,” IEEE Transactions on Geoscience and Remote Sensing, vol.

Benediktsson, “A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles,”IEEE Transactions on Geoscience and Remote Sensing, vol.

Three new techniques were the key to achieve this speed: Pipelined computational cores, sixteen parallel data paths and a novel memory interface for maximizing the external memory bandwidth.

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Geoscience and Remote Sensing, 52(5): 2382-2394, 2014.

Benediktsson, Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization, IEEE Trans.

This paper presents a Field Programmable Gate Array (FPGA) based embedded system which is used to achieve high speed segmentation of 3D images.

Segmentation is performed using Expectation-Maximization (EM) with Maximization of Posterior Marginals (MPM) Bayesian algorithm.

Zhu, “Hyperspectral Data Classification Using Extended Extinction Profiles,” in IEEE Geoscience and Remote Sensing Letters. PP, no.99, pp.1-5 doi: 10.1109/LGRS.2016.2600244 P.

Zhu, “A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data,” in IEEE Geoscience and Remote Sensing Letters. PP, no.99, pp.1-5 doi: 10.1109/LGRS.2016.2595108 [J18] Y.

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