In this paper, we propose multiple demeaning filters for small target detection in infrared (IR) images. The use of
a demeaning filter is a promising method which detects a small object by removing the background components
with a mean filter. The main factors in the design of a demeaning filter are two types of demeaning methods
and the size of its window. We compare two demeaning methods, the sliding window method and the grid
method, and we analyze the trade-off between the window size and the performance of the demeaning filters and
present limitations related to their use. To overcome the drawbacks of a conventional demeaning filter, the use
of multiple demeaning filters with filters of various sizes is considered. The proposed method not only has the
advantage of being able to detect a small object in a densely cluttered environment, but it also can be used with
low complexity with an integral image. Experimental results demonstrate the robustness and stability of the
proposed multiple demeaning filters with low computational complexity compared with conventional methods.
In this paper, we propose a denoising method for hyperspectral images using a joint bilateral filter. The joint bilateral
filter with the fused image of hyperspectral image bands is applied on the noisy image bands. This fused image is a
single grayscale image that is obtained by the weighted summation of hyperspectral image bands. It retains the features
and details of each hyperspectral image band. Therefore the joint bilateral filter with the fused image is powerful in
reducing noise while preserving the characteristics of the individual spectral bands. We evaluated the performance of the
proposed noise reduction method on hyperspectral imaging systems, which we developed for visible and near-infrared
spectral regions. Experimental results show that the proposed method outperforms the conventional approaches, such as
the basic bilateral filter.
This paper presents an extended bilateral filter with spectral angles and a visualization scheme for hyperspectral image
data. The conventional bilateral filter used to be implemented using a position vector and a luminance value at each pixel
in the scene. Since hyperspectral image data can provide a spectrum vector that has hundreds of bands at each pixel, we
propose an extended bilateral filter using spectral angles. The proposed bilateral filter can be used for extracting and
preserving the spectrum edges of the hyperspectral image. A visualization scheme for hyperspectral images exploiting
the proposed bilateral filter has been also proposed. When objects that have the similar tristimulus intensity but different
spectrums are mixed, they can be separated through the proposed visualization scheme. The resulting images show that
the proposed scheme facilitates anomaly detection in the hyperspectral scenes.
In this paper, we propose an overall target tracking scheme performing image stabilization, detection, tracking,
and classification in the IR sensored image. Firstly, in the image stabilization stage, a captured image is
stabilized from visible frame-to-frame jitters caused by camera shaking. After that, the background of the
image is modeled as Gaussian. Based on the results of the background modeling, the difference image between a
Gaussian background model and a current image is obtained, and regions with large differences are considered as
targets. The block matching method is adopted as a tracker, which uses the image captured from the detected
region as a template. During the tracking process, positions of the target are compensated by the Kalman filter.
If the block matching tracker fails to track targets as they hide themselves behind obstacles, a coast tracking
method is employed as a replacement. In the classification stage, key points are detected from the tracked image
by using the scale-invariant feature transform (SIFT) and key descriptors are matched to those of pre-registered
template images.
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.
Recently, multi-sensor image fusion systems and related applications have been widely investigated. In an image fusion
system, robust and accurate multi-modal image registration is essential. In the conventional method, the image registration
process starts with manually-pointed corresponding pairs in both sensored images. Using these corresponding pairs, a
transform matrix is initialized and refined through an optimization process. In this paper, we propose a new automatic
extraction method for such corresponding pairs. The Harris corner detector is employed to extract feature points in both
EO/IR images individually. Patches around the detected feature points are matched with a probabilistic criterion, mutual information
(MI), which is a preferred measure for image registration due to its robust and accurate performance. Simulation
results show that the proposed scheme has a low time complexity and extracts corresponding pairs well.
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