Summary form only given. Traditionally, signal processing is considered simply lowlevel processing. In the past decade, however, signal processing has grown to become the area where a variety of tools are created to solve high-level problems that conventionally would be studied by computer vision or machine learning researchers exclusively. For example, multiresolution analysis created popular image features like SIFT (scale-invariant feature transform), and statistical analysis gave birth to graphical models such as HMM (hidden Markov models) and topic models. In this talk, we will use one application to illustrate this growth of signal processing: object discovery, i.e., extracting the “object of interest” from a set of images in a completely unsupervised manner.