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.
In this paper, we propose an image fusion for open and unknown environments using normalized mutual information
(NMI) in an infrared (IR) and visual vision system. Image fusion is a field of study of image processing, and it creates a
new image to extract information from various different sensors. And also it gets effective information for a special
object. This can get object types, sensitive characteristic, and information which it not to get characteristic of object from
a single sensor. Image fusion in multi-sensors is two advantages. First, multi-sensor image has inherent redundancy for
each sensor because it can be fused each image from a various multi-band sensor. Second, multi-sensor differs from a
single sensor because it is included information of each sensor and is separated information of object easily in real
environments. Proposed method consists of extraction and comparison of feature point, image registration, and pseudo
color for display. Extraction of feature point is stage which it looks for a similar feature points between each sensor.
Then, the extraction of a similar feature point uses a corner detector. A detected correspondence point from multi-sensor
is compared feature point by using NMI. An acquired image in multi-sensor needs an image registration between two
images. Because it needs transformation from reference image to a coordinated system of sensed image. And this
represents each coordinated system independently between two images. Image registration use transformation of H
matrix. Method for overlay between two images uses blending based on HSV. Based on experimental results, the
proposed method shows high precision for fused pseudo image in multi-sensor, and can be represented image
registration by using probability-based method.
In this paper, we propose an automated target recognition by using scale-invariant feature transform (SIFT) in PowerPC-based
infrared (IR) imaging system. An IR image can be acquired more feature values at night than in the daytime, but
visual image can be acquired more feature values in the daytime. IR-based object recognition puts application into digital
surveillance system because it exist some more feature values at night than in the daytime. Feature of IR image in its
system appears a little feature value in the daytime. It is not comprised within an effective feature values at a visual
image from an IR of the daytime. Proposed method consists of two stages. First, we must localize the interest point in
position and scale of moving objects. Second, we must build a description of the interest point and recognize moving
objects. Proposed method uses SIFT for an effective feature extraction in PowerPC-based IR imaging system. Proposed
SIFT method consists of scale space, extrema detection, orientation assignment, key point description, and feature
matching. SIFT descriptor sets up extensive range about 1.5 times than visual image when feature value of SIFT in IR
image is less than visual image. Because an object in IR image is analogized by field test that it exist more expanse form
than visual image. Therefore, proposed SIFT descriptor is constituted at more expanse term for a precise matching of
object. Based on experimental results, the proposed method is extracted object's feature values in PowerPC-based IR
imaging system, and the result is presented by experiment.
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.
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.
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