The demeaning filter detects a small object by removing a background with a mean filter as well as the covariance of an
object and backgrounds. The factors considered in the design of the demeaning filter are the method of demeaning, which
involves subtracting the local mean value from all pixel values, and the acquisition of templates for both the object and the
background. This study compares the sliding window method and the grid method as a demeaning method, and studies the
method of acquisition of an object template. Moreover, a method involving the use of previous frames, a mean filter, and an
opening operation are studied in an effort to acquire a background template. Based on the results of this study, a practical
design of a demeaning filter that is able to detect a small object in an IR image in real time is proposed. Experiment results
demonstrate the superiority of the proposed design in detecting a small object following a 2-D Gaussian distribution even
under severe zero-mean Gaussian noise.
In this paper, a new condition for the target is proposed to increase the robustness of the facet-based detection method
for zero-mean Gaussian noise. In the proposed algorithm, the pixels detected from the maximum extremum condition are
checked further to discern if they are false maximum points in the proposed scheme. The experimental results show that
the proposed algorithm is much more robust for zero-mean Gaussian noise than the conventional detection method.
Target segmentation plays an important role in the entire target
tracking process. This process decides whether the current pixel
belongs to the target region or not. In the previous works, the
target region was extracted according to whether the intensity of
each pixel is larger than a certain value. But simple binarization
using one feature, i.e. intensity, can easily fail to track as
condition changes. In this paper, we employ more features such as
intensity, deviation over time duration, matching error, etc.
rather than intensity only and each feature is weighted by the
weighting logic, which compares the characteristics in the target
region with that in the background region. The weighting logic
gives a higher weight to the feature which has a large difference
between the target region and the background region. So the
proposed segmentation method can control the priority of features
adaptively and is robust to the condition changes of various
circumstances.
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.