In order to make devices partially or completely autonomous, it is imperative nowadays to extract relevant information from the myriad of data available. In the last years, it has become very common to use images as signals of interest to propose feasible solution to this problem. Image recognition can be used with high accuracy rates when the object of interest or the environment are controlled or well known. However, in open urban spaces, for instance, where there are all sorts of visual artifacts and stimuli (information), the segmentation of the object of interest (foreground) from the rest of the image (background) is a challenging issue. One possible way to tackle this problem is to use low-depth of field images, which analogously to our visual perception highlight the object of interest from the rest of the image. In this work, some methods and algorithms for segmenting low-depth of field images are analyzed and compared, providing an updated and contextualized version of the state-of-the-art of this topic.