The tremendous need for the analysis of massive image data sets in many application areas has been mainly promoting pragmatic approaches to imaging analysis during the last years: adopt a computational model with adjustable parameters and predictive power. This development poses a challenge to the mathematical imaging community: (i) shift the focus from low-level problems (like denoising) to mid- and high-level problems of image analysis (a.k.a. image understanding); (ii) devise mathematical approaches and algorithms that advance our understanding of structure detection in image data beyond a set of rules for adjusting the parameters of black-box approaches. The purpose of this talk is to stimulate the corresponding discussion by sketching past and current major trends including own recent work.