Image Segmentation Phd Thesis 2010

ijcaonline.org/archives/volume109/number8/19208-0915 P. International Journal of Computer Applications 109(8):18-25, January 2015. This algorithm segments the 3D image using neighboring pixels based on a Markov Random Field (MRF) model.

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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.

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.

on Geoscience and Remote Sensing, 52(9): 5771-5782, 2014.

Sveinsson, Automatic Spectral-Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction, IEEE Trans.

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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The dataset was captured over Samford Ecological Research Facility (SERF), Queensland, Australia. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images [B1] M.

The dataset is composed of hyperspectral and Li DAR data as well as their corresponding training and test samples.

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