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A methodology for processing images of diesel sprays under different experimental situations is presented. The new approach has been developed for cases where the background does not follow a Gaussian distribution but a positive bias appears. In such cases, the lognormal and the gamma probability density functions have been considered for the background digital level distributions. Two different algorithms have been compared with the standard log-likelihood ratio test (LRT): a threshold defined from the cumulative probability density function of the background shows a sensitive improvement, but the best results are obtained with modified versions of the LRT algorithm adapted to non-Gaussian cases.

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To characterize the macroscopic behavior of Diesel sprays and to validate and extend for current high-pressure injection systems the correlations existent in the literature, it is necessary to determine the spray geometry accurately, at least in terms of spray tip penetration and cone angle. These parameters are measured by analyzing Diesel spray images and are highly sensitive to the correct edge determination. An algorithm for segmentation of color images based on a likelihood ratio test is presented. This algorithm is compared with others available in the literature and has been validated, even for adverse experimental conditions. The experimental facilities, optical layouts, and image-processing algorithms are described.

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