Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval using color histograms generated by Gauss mixture vector quantization
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Color Histogram Based Image Retrieval
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Texture Classification Based on Completed Modeling of Local Binary Pattern
ICCIS '11 Proceedings of the 2011 International Conference on Computational and Information Sciences
Building Change Detection by Histogram Classification
SITIS '11 Proceedings of the 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems
Image analysis with local binary patterns
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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Census data provides an important source of information with respect to decision makers operating in many different fields. However, census collection is a time consuming and resource intensive task. This is especially the case in rural areas where the communication and transportation infrastructure is not as robust as in urban areas. In this paper the authors propose the use of satellite imagery for census collection. The proposed method is not as accurate as "on ground" census collection, but requires very little resource. The proposed method is founded on the idea of collecting census data using classification techniques applied to relevant satellite imagery. The objective is to build a classifier that can label households according to "family" size. More specifically the idea is to segment satellite images so as to obtain pixel collections describing individual households and represent these collections using some appropriate representation to which a classifier generator can be applied. Two representations are considered, histograms and Local Binary Patterns (LBPs). The paper describes the overall method and compares the operation of the two representation techniques using labelled data obtained from two villages lying some 300km to the northwest of Addis Ababa in Ethiopia.