We propose a novel B-spline active contour model based
on image fusion. Compared with conventional active contours, this
active contour has two advantages. First, it is represented by a cubic
B-spline curve, which can adaptively determine the curve parameter’s
step length; and it can also effectively detect and express the
object contour’s corner points. Second, it is implemented in connection
with image fusion. Its external image force is modified as the
weighted sum of two modal image forces, with the two weights in
terms of a local region’s image entropy or image contrast’s standard
deviation. The experiments indicate that this active contour can accurately
detect both the object’s contour edge and the corner points.
Our experiments also indicate that the active contour’s convergence
with a weighted image force by the image contrast’s standard deviation
is more accurate than that of image entropy, restraining the
influence of the texture or pattern.
In visual tracking, the object's appearance may change over time due to illumination changes, pose variations, and partial or full occlusions. This variability makes tracking difficult. This paper proposes an adaptive appearance model for visual tracking. The model can adapt to changes in object appearance over time. The value of each pixel is modeled by a Gaussian mixture distribution. A novel update scheme based on the expectation maximization algorithm is developed to update the appearance model parameters. In designing the tracking algorithm, the observation model is based on the adaptive appearance model, and a particle filter is employed. Outlier pixels and occlusions are handled using a robust-statistics technique. Numerous experimental results demonstrate that the proposed algorithm can track objects well under illumination changes, large pose variations, and partial or full occlusions.
In this paper, a novel radar management strategy technique suitable for RADAR/IRST track fusion, which is based on Fisher Information Matrix (FIM) and fuzzy stochastic decision approach, is put forward. Firstly, optimal radar measurements' scheduling is obtained by the method of maximizing determinant of the Fisher information matrix of radar and IRST measurements, which is managed by the expert system. Then, suggested a "pseudo sensor" to predict the possible target position using the polynomial method based on the radar and IRST measurements, using "pseudo sensor" model to estimate the target position even if the radar is turned off. At last, based on the tracking performance and the state of target maneuver, fuzzy stochastic decision is used to adjust the optimal radar scheduling and retrieve the module parameter of "pseudo sensor". The experiment result indicates that the algorithm can not only limit Radar activity effectively but also keep the tracking accuracy of active/passive system well. And this algorithm eliminates the drawback of traditional Radar management methods that the Radar activity is fixed and not easy to control and protect.