Summary: Detecting Objects of Variable Shape Structure
with Hidden State Shape Models
Jingbin Wang, Student Member, IEEE, Vassilis Athitsos, Member, IEEE,
Stan Sclaroff, Senior Member, IEEE, and Margrit Betke, Member, IEEE
Abstract--This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images.
The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary
number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models
(HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using
a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure
by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with
real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of
hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed
methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains
speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking.
Index Terms--Object detection, shape modeling, probabilistic algorithms, dynamic programming.
AN important problem in computer vision is detecting
objects in the presence of noise, clutter, and occlusions,
and registering their shape with a model. It is desirable to use