  Artificial objects usually have very stable shape features, which are stable,
persistent properties in geometry. They can provide evidence for object
recognition. Shape features are more stable and more distinguishing than
appearance features, color features, grayscale features, or gradient features.
The difficulty with object recognition based on shape features is that objects
may differ in color, lighting, size, position, pose, and background
interference, and it is not currently possible to predict all possible
conditions. The variety of objects and conditions renders object recognition
based on geometric features very challenging. This paper provides a method
based on shape templates, which involves the selection, collection, and
combination discrimination of geometric evidence of the edge segments of
images, to find out the target object accurately from background, and it is
able to identify the semantic attributes of each line segment of the target
object. In essence, the method involves solving a global optimal combinatorial
optimization problem. Although the complexity of the global optimal
combinatorial optimization problem seems to be very high, there is no need to
define the complex feature vector and no need for any expensive training
process. It has very good generalization ability and environmental
adaptability, and more solid basis for cognitive psychology than other methods.
The process of collecting geometric evidence, which is simple and universal,
shows considerable prospects for practical use. The experimental results prove
that the method has great advantages in response to changes in the environment,
invariant recognition, pinpointing the geometry of objects, search efficiency,
and efficient calculation. This attempt contributes to understanding of some
types of universal processing during the process of object recognition.
