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Classification Experiments on Real-World TextureMany papers have been published concerning the analysis of visual texture and yet, very few application domains use texture for image classification. A possible reason for this low transfer of the technology is the lack of experience and testing in real-world imagery. In this paper, we assess the performance of texture-based classification methods on a number of real-world images relevant to autonomous navigation on cross-country terrain and to autonomous geology. Texture analysis will form part of the closed loop that allows a robotic system to navigate autonomously. We have implemented two different classifiers on features extracted by Gabor filter banks. The first classifier models feature distributions for each texture class using a mixture of Gaussians. Classification is performed using Maximum Likelihood. The second classifier represents local statistics using marginal histograms of the features over a region centered on the pixel to be classified. We measure system performance by comparison to ground truth image labels.
Document ID
20100009825
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Castano, Rebecca
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Manduchi, Roberto
(California Univ. Santa Cruz, CA, United States)
Fox, Justin
(California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 25, 2013
Publication Date
December 10, 2001
Subject Category
Instrumentation And Photography
Distribution Limits
Public
Copyright
Other
Keywords
image segmentation
image processing
texture analysis

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