A Gaussian model-based statistical matching procedure is proposed for image enhancement and segmentation. Generally
speaking, enhanced images are desired for visual analysis whereas segmented images are required for target recognition.
A histogram matching procedure is used to enhance a given image. To perform histogram matching, two histograms are
needed, an original histogram computed from the given image and a specified histogram to be matched to. For image
enhancement, the specified histogram is a Gaussian model (mean & standard deviation) that can be estimated from a
number of well-exposed images or properly processed images. Certainly the Gaussian model varies with the category of
imagery. For image segmentation, N Gaussian models (means & standard deviations) are estimated from the original
histogram of a given image. The number of Gaussian models (N) is decided by analyzing the original histogram. A
statistical matching procedure is used to map the original histogram onto one of the Gaussian models defined by their
means and standard deviations. Specifically, the mapped image can be computed by subtracting the mean of original
image from the original image, scaling with the ratio of the standard deviation of Gaussian model to the standard
deviation of original image and plus the mean of Gaussian model. The statistically mapped image is thresheld by using
the mean of Gaussian model, which results one set of expected segments. The statistical matching plus thresholding
procedure is repeated N times for N Gaussian models. Finally, all N sets of segments are fully obtained. The proposed
image enhancement and segmentation procedure are validated with multi-sensor imagery.