In an optical flow field, the background and moving objects present different vector groups with different directions,
velocities and region areas. The idea optical flow field is not easy to obtain for some kinds of reasons; in practical field,
the motion vectors present confusion and uncertainty to some extent. The fuzzy clustering provides an effective way to
process unclear classification. It maps every vector into every group, and the ascription presents a degree a vector
belongs to a group. However, conventional fuzzy clustering method needs to determine the group number, namely the
moving objects number in the view field. Before all samples are processed and the group number is fixed during
iteration. The unsuitable number easily results in inaccurate segmentation. In view of this problem, an enhanced
detection algorithm using fuzzy clustering with elastic grouping logic is proposed. To be called elastic grouping logic, it
means that in the process of optical flow field detection, according to the ascription the vector to each group, together
with the vector's location, direction and magnitude, the group number, namely the moving object number, is selfadaptively
generated, and further to achieve the moving objects segmentation with precision. A stability model of motion
vectors for an object group and the group's partition is also established. The experimental results illustrate the proposed
algorithm is able to satisfy the need of multi-objects detection and locate the moving objects successfully.
We propose a super-resolution resolution algorithm on the basis of maximum likelihood (ML) method and edge-orient diffusion. By using Hammerseley-Clifford theorem, an image field assumed to be a Markov random field is Gibbs distributed. An edge-orient diffusion function is introduced and employed in the Gibbs prior. According to Bayesian theorem, the solution to the maximum likelihood function is equal to that to maximum a posterior function. Therefore we incorporate ML with a prior distributed function. Experimental results illustrate that our method has a powerful super-resolution restoration performance. Compared with traditional ML method, our approach can not only obtain super-resolution images, but also eliminate noise artifacts effectively without smoothing edges.