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Improving reflectance estimation by BRDF-consistent region clustering

Progress in Natural Science:Materials International 2006年第3期16卷 313-320页

作者： LIN Steve Beijing 100080 China Visual Computing Group Microsoft Research Asia

Previous studies in reflectance estimation generally require prior segmentation of an image into regions of uniform reflectance. Due to the measurement noise and limited sampling of the BRDF (bi-directional reflectance function) directions, such estimated results of reflectance are not accurate. In this paper, we propose a novel method for reducing uncertainty in reflectance estimates by merging image regions which have consistent reflectance observations. Each image region acts as a reflectance subspace, so merging of the image regions can result in subspace reduction. We propose a Bayesian segmentation framework to decrease the reflectance uncertainty by using novel merging criteria. Finally, a maximum likelihood reflectance estimation is made for each resulting image region. Experimental results verify the feasibility and superiority of this reflectance-oriented region merging method.

关键词： texture shading grouping and segmentation reflectance estimation BRDF uncertainty.

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Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

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China Communications 2017年第1期14卷 34-49页

作者： Lechan Yang Zhihao Qin Kun Wang Song Deng International Institute for Earth System Science Nanjing University Nanjing 210093 China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and TelecommunicationsNanjing 210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

关键词： reflectance estimation dimensionality reduction gene expression programming model mining

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