A wide variety of techniques has been examined in the literature for the detection and segmentation of target objects in images. This paper is concerned with the comparison of a set of alternatives drawn from two generic approaches to the problem. Histogram-based techniques focus on the distribution of some descriptive attribute or set of attributes within the image. The use of several such algorithms is considered including a sampled peak-finding method, a sampled percentile-finding method, multivariate histogramming based on greylevel and edge information and the well-known superspike algorithm. Hierarchical target detection techniques, on the other hand, attempt to exploit links between multiple reduced resolution views of the image. A range of such methods is also described based on the use of both iterative and top-down traversal procedures. Each of the algorithms is discussed, and their performance on a database of synthetic and real infra-red images is compared in terms of segmentation quality and computational cost.