We present an algorithm to realign images distorted by motion and vibrations captured in cameras that use a scanning vector sensor with an interlaced scheme. In particular, the method is developed for images captured by a staggered time delay and integration camera distorted by motion. The algorithm improves the motion-distorted image by adjusting its fields irrespective of the type of motion that occurs during the exposure. The algorithm performs two tasks: estimation of the field relative motion during the exposure by a normal least-squares estimation technique and improvement of the degraded image from such motion distortion. The algorithm uses matrix computations; therefore it has a computation advantage over algorithms based on the technique of searching for a match. The algorithm is successfully demonstrated on both simulated and real images.

Registration of motion-distorted interlaced images captured by a scanning vector imaging sensor.

The work is addressed to the problem of an object extraction in the images during sensor motion. In the case the object
extraction requires the registration of the observed image and the reference background image. The error of the
registration causes false alarms in the extraction result. In this paper the problem of object extraction during sensor
motion is solved by taking into the consideration the statistical properties of this error. The solution of this problem has
been obtained using the method based on Johnson distribution parameters estimation. This method has very high
computational complexity; therefore, the simplified algorithm for an object extraction was developed. The experimental
research results are also presented.

Extraction of the objects observed on a non-uniform background during sensor motion

In this paper we propose an object tracking approach that includes the selection of the appropriate coordinate estimation
method from the set of base methods: cross-correlation, statistical segmentation, methods based on spatial and spatiotemporal filtering. The tracking performance of the base methods is estimated by means of the several parameters
(performance features) that describe the reliability of tracking. The features are invariant to the changes of the mean
brightness, contrast, scale and rotation. The comparison of the performance of the methods is based on the binary
performance characteristic that describes the tracking in the terms of work/not work. The proposed object tracking algorithm is implemented in five steps which are carried out on each frame: the calculation of the performance feature for each base coordinate estimation method, the calculation of the binary performance characteristic for each method, the selection of the appropriate method, the estimation of the object coordinates using the selected method, the correction of the tracking process performed by other methods. The algorithm shows a good tracking performance during the variations of the observation conditions. In most cases it comprises the main advantages of each of the source methods.

Object tracking in the video sequence based on the automatic selection of the appropriate coordinate estimation method

Influence of sensor motion on infrared target acquisition

One of the effective ways to improve object tracking performance is a fusion of base tracking algorithms to their advantages and eliminate disadvantages. This fusion requires the estimation of the performances of the base object tracking algorithms. So the real-time estimation of the performance of each base tracking algorithm is required for the algorithm result to be used for the fusion. In this paper we propose an algorithm for performance estimation for the object tracking algorithm based on the pyramidal implementation of Lukas-Kanade feature tracker. The performance estimation is based on the analysis of the variations of the intermediate algorithm parameters calculated during object tracking, such as total and mean feature lifetime, eigenvalues, inter-frame mean square coordinate difference, etc. Different combinations of these parameters were tested to obtain the best evaluation quality. The statistic measures were calculated for the image sequence, one or two hundred frames long. These statistic measures are highly correlated with the algorithm performance measures, based on the ground truth data: tracking precision and the ratio of the false detected features. The experimental research was performed using synthetic and real-world image sequences. We investigated performance estimation effectiveness in different observation conditions and during image degradations caused by noise, blur and low contrast. The experimental results show good performance estimation quality. This allows Lukas-Kanade feature tracker to be fused with another tracking algorithms (correlation-based, segmentation, change detection) to obtain reliable tracking. Since the approach is based on the intermediate Lukas-Kanade algorithm parameters, then it does not bring valuable computational complexity to the tracking process. So real-time performance estimation can be implemented.

Real-time pyramidal Lukas-Kanade tracker performance estimation

The resolution capability of imaging systems is affected by blur resulting from vibration and motion during the exposure. This blur is often more severe than electronic and optical resolution limitations inherent in the system. Such image quality degradation must be considered when dealing with the development and analysis of automatic target recognition (ATR) systems. This research analyzes the influence of image vibrations and motion on the probability of acquiring a target with an ATR system. The analysis includes accepted metrics that characterize the relationship existing between the target and its background. A high level of correlation is expected between these factors and the probability of target detection permitting efficient performance in the prediction and evaluation of any ATR system. Such correlations are considered here in the presence of sensor motion and vibration and situations are considered in which the probability of recognition is improved by the motion, despite the blur. The results of this research can be implemented in military applications as well as in developing image restoration procedures for image-blur conditions.

Automatic target recognition during sensor motion and vibration

The resolution capability of imaging systems is effected by a blurring effect due to the vibration and motion recorded in the image. This disturbance is often more severe than electronic and optical limitations inherent in the system. This fact must be considered when dealing with the development and analysis of automatic target recognition (ATR) systems for military applications. The aim of this research is to analyze the influence of image vibrations and motion upon the probability of acquiring the target with an ATR system. The analysis includes factors that characterize the relationship existing between the target and its background. A high level of correlation is expected between these factors and the probability of target detection, enabling efficient performance in the prediction and evaluation of any ATR system. The results of this research can be implemented in military applications as well as in developing image restoration procedures for image-blur conditions.

A. Kuntsevitsky

Papers

Automatic target recognition during sensor motion and vibration

Automatic target recognition during sensor motion and vibration