This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically
recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is
to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image
so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via
the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly.
(b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and
their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in
a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of
the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and
male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition
accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that
quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest
recognition. (3) Scale yields useful information about the relationship between the model assembly and any given
image such that the recognition results in a placement of the model close to the actual pose without doing any
elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.