An improved algorithm integrating wavelet decomposition, multilevel filtering, and an additive operator splitting (AOS)-based level-set framework for infrared small target detection is proposed. This model has two components: a filtering operation, and level-set evolution. In the filtering step, the original image is first decomposed using a wavelet transform. After determining the location of sea-sky line, we construct a subimage based on the sea-sky-line position, and then execute multilevel filtering on this subimage. This filtering framework provides the input image for the level-set evolution. Using the level-set formulation, complex curves can be detected while naturally handling topological changes of the evolving curves. To reduce the computational cost required by an explicit implementation of the level-set formulation, a new solver named AOS is proposed. Additionally, the quantitative analyses for our algorithm are also given. Experiments on real infrared image sequences indicate that our method is efficient and robust.
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.
Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of
infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method
based on the integration of visual attention computational model and level set methodology. The two distinct techniques
for image processing are combined in a manner to utilize the strengths of both. The visual attention searches the salient
regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions
in terms of circles centered at these winner points. This provides a priori knowledge for level set method, the initial level
set function could be constructed based on the winner points, in this way, an automatic initialization of level set evolution
can be obtained, and the boundaries of the targets can be obtained. The cost time does not depend on the size of the
image but the salient regions, therefore the consumed time is greatly reduced. At the same time, this algorithm discards
the re-initialization procedure and force the level set function to be close to a distance function, therefore reduces the side
effects of re-initialization, The method is used in the recognition of several kinds of real infrared images, and the
experimental results reveal the effectiveness of the algorithm presented in this paper.
Infrared images at sea background are notorious for the low
signal-to-noise ratio, therefore, the target recognition of infrared image through
traditional methods is very difficult. In this paper, we present a novel target
recognition method based on the integration of visual attention computational model
and conventional approach (selective filtering and segmentation). The two distinct
techniques for image processing are combined in a manner to utilize the strengths of
both. The visual attention algorithm searches the salient regions automatically, and
represented them by a set of winner points, at the same time, demonstrated the salient
regions in terms of circles centered at these winner points. This provides a priori
knowledge for the filtering and segmentation process. Based on the winner point, we
construct a rectangular region to facilitate the filtering and segmentation, then the
labeling operation will be added selectively by requirement. Making use of the
labeled information, from the final segmentation result we obtain the positional
information of the interested region, label the centroid on the corresponding original
image, and finish the localization for the target. The cost time does not depend on the
size of the image but the salient regions, therefore the consumed time is greatly
reduced. The method is used in the recognition of several kinds of real infrared
images, and the experimental results reveal the effectiveness of the algorithm
presented in this paper.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.