Sampling the parameter domain of image series

While analyzing a scene of interest in real environments, the acquisition and evaluation of image series has proven to yield promising results in order to provide useful information. However, acquiring and evaluating image series imposes several difficulties on the imaging and analysis process: The amount of data to be processed increases significantly, especially when more than one parameter is varied. Recording image series thus leads to a dilemma: Whereas a dense scanning of a varied parameter is desirable in order not to lose any information of interest, the number of recorded images should be as small as possible to ensure both adequate acquisition time and manageable amount of data. This dilemma can be considered as a sampling issue of the parameter space of a variable image acquisition. However, when two or more parameters are varied simultaneously, the resulting sampling condition is not necessarily a simple superposition of the one-dimensional cases. In this contribution, the topic of optimally sampling the parameter spaces for image series is addressed. Sampling conditions for different parameters to be varied are discussed. For image series with multiple varied parameters, interdependences that can be used to reduce the acquisition expenses without loosing relevant information are pointed out.