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.
Tracking deformable objects is very important in many applications such as surveillance, security and military. In this
paper, we implement one tracking scheme based on the block matching using PowerPC. We implement tracking
algorithm using information from Infrared (IR) sensor for object tracking. When an occlusion occurs, the proposed
algorithm predicts movements of an object using the historical tracking information and it can keep the object tracking.
Based on experimental results, the proposed system can reduce calculation time and track object under condition of
camera jitter and the occlusions.
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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