Infrared small target detection is difficult due to several aspects, including the low signal-to-clutter ratio of the infrared image, and the small size, lack of shape and texture information of the target. a novel method, which is based on spatial-temporal association, is presented for infrared target detection. The algorithm consists of the three steps: Firstly, 2-dimensional histogram of entropy flow field is computed to estimate the background motion. Secondly, the difference image through background motion compensation is obtained. Finally, the targets are detected by spatial-temporal filter. The experiment results demonstrate that the proposed algorithm is robust to noise, and also fit to detect small targets under moving backgrounds in infrared image sequences.
We propose a moving objects segmentation method for color image sequences based on the piecewise constant Mumford-Shah model (also known as the C-V model) solving by the semi-implicit additive operator splitting (AOS) scheme, which is unconditionally stable, fast, and easy to implement. The method first uses the Gaussian mixture model for background modeling and then subtracts the background to obtain the moving regions that are the handling objects of our method. As a result of the introduction of the AOS scheme, we could use a rather large time step and still maintain the stability of the evolution process. Additionally, the method can easily be parallelized because the AOS scheme decomposes the equations into a sequence of one-dimensional (1-D) systems. The experimental results demonstrate that under real moving objects video tests, the AOS scheme accelerates the evolution of the curve and significantly reduces the number of iterations, and also demonstrates the validity of our method.
The theoretical analysis shows that fractional differential can greatly improve high frequency, reinforce
medium frequency and non-linearly preserve low frequency of signals, hence they could be used for edge and texture
enhancement as well as smooth area preservation.In this paper, a new covering template and algorithm for fractional
differential image enhancement based on space weight (Fdsw) are discussed. Firstly, pixel neighbourhood relations are
acquired from adopting Caputo and Riemann-Liouville fractional differential. Secondly,covering template coefficients of
space weight are constructed in accordance with pixel neighbourhood relations. Constructing fractional differential
template holds isotropic characteristic, and covering template coefficients are indispensable to renormalize. Finally,
covering template moves on the unprocessed image point by point, then corresponding pixel value is summed. Experiments show that the new method has excellent feedback for enhancing the textural details of rich-grained digital images.