12,509 research outputs found
Adaptive pre-filtering techniques for colour image analysis
One important step in the process of colour image
segmentation is to reduce the errors caused by image
noise and local colour inhomogeneities. This can be
achieved by filtering the data with a smoothing
operator that eliminates the noise and the weak
textures. In this regard, the aim of this paper is to
evaluate the performance of two image smoothing
techniques designed for colour images, namely
bilateral filtering for edge preserving smoothing and
coupled forward and backward anisotropic diffusion
scheme (FAB). Both techniques are non-linear and
have the purpose of eliminating the image noise,
reduce weak textures and artefacts and improve the
coherence of colour information. A quantitative
comparison between them will be evaluated and also
the ability of such techniques to preserve the edge
information will be investigated
A superior edge preserving filter with a systematic analysis
A new, adaptive, edge preserving filter for use in image processing is presented. It had superior performance when compared to other filters. Termed the contiguous K-average, it aggregates pixels by examining all pixels contiguous to an existing cluster and adding the pixel closest to the mean of the existing cluster. The process is iterated until K pixels were accumulated. Rather than simply compare the visual results of processing with this operator to other filters, some approaches were developed which allow quantitative evaluation of how well and filter performs. Particular attention is given to the standard deviation of noise within a feature and the stability of imagery under iterative processing. Demonstrations illustrate the performance of several filters to discriminate against noise and retain edges, the effect of filtering as a preprocessing step, and the utility of the contiguous K-average filter when used with remote sensing data
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