There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit line segment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.
Visual tracking is a critical task in many computer vision applications such as surveillance, vehicle tracking, and
motion analysis. The challenges in designing a robust visual tracking algorithm are caused by the presence of
background clutter, occlusion, and illumination changes. In this paper, we propose a visual tracking algorithm in a
particle filter framework to overcome these three challenging issues. Particle filter is an inference technique for
estimating the unknown motion state from a noisy collection of observations, so we employ particle filter to learn the
trajectory of a target. The proposed algorithm depends on the learned trajectory to predict the position of a target at a
new frame, and corrects the predication by a process that can be entitled field transition. At the beginning of the tracking
stage, a set of disturbance templates around the target template are accurately selected and defined as particles. During
tracking, a position of the tracked target is firstly predicted based on the learned motion state, and then we take the
normalized cross-correlation coefficient as a level to select the most suitable field transition parameters of the predicted
position from the corresponding parameters of the particles. After judging the target is not occluded, we apply the named
field transition with the selected parameters to compensate the predicted position to the accurate location of the target,
meanwhile, we make use of the calculated cross-correlation coefficient as a posterior knowledge to update the weights of
all the particles for the next prediction. In order to evaluate the performance of the proposed tracking algorithm, we test
the approach on challenging sequences involving heavy background clutter, severe occlusions, and drastic illumination
changes. Comparative experiments have demonstrated that this method makes a more significant improvement in
efficiency and accuracy than two previously proposed algorithms: the mean shift tracking algorithm (MS) and the
covariance tracking algorithm (CT).
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.