In bionic methodology rather than in design methodology more familiar with, summarizing the psychological
researches of emotion, we propose the biologic mechanism of emotion, emotion selection role in creature evolution and
a anima framework including emotion similar to the classical control structure; and consulting Prospect Theory, build an
Emotion Characteristic Functions(ECF) that computer emotion; two more emotion theories are added to them that higher
emotion is preferred and middle emotion makes brain run more efficiently, emotional behavior mechanism comes into
being. A simulation of proposed mechanism are designed and carried out on Alife Swarm software platform. In this
simulation, a virtual grassland ecosystem is achieved where there are two kinds of artificial animals: herbivore and
preyer. These artificial animals execute four types of behavior: wandering, escaping, finding food, finding sex partner in
their lives. According the theories of animal ethnology, escaping from preyer is prior to other behaviors for its existence,
finding food is secondly important behavior, rating is third one and wandering is last behavior. In keeping this behavior
order, based on our behavior characteristic function theory, the specific functions of emotion computing are built of
artificial autonomous animals. The result of simulation confirms the behavior selection mechanism.
A novel approach combining 2DCCA, edge detector, and corner detector for object detection is proposed in this paper.
The detection system consists of two stages. In the first stage, edge and corner information is obtained by edge detector
and corner detector. By setting range for the number of edge pixel and corner in the scanning window, a large number of
non-object windows are rejected. In the second stage, the classifier trained by 2DCCA is combined with slide window
method so that further non-object windows are rejected. For the case that one object is simultaneously contained in
several windows, the algorithm of determining the best position of object is designed. Compared with related approaches,
our method has advantage of obtaining higher precision under the similar recall. The performance of the proposed
approach is illustrated by experimental results.