A real-time system for multiclass object recognition is proposed. The system is able to identify and correctly
classify several moving targets from an input scene by using a bank of adaptive correlation filters with complex
constraints implemented on a graphics processing unit. The bank of filters is synthesized with the help of
an iterative algorithm based on complex synthetic discriminant functions. At each iteration, the algorithm
optimizes the discrimination capability of each filter in the bank by using all available information about the
known patterns to be recognized and unwanted patterns to be rejected such as false objects or a background.
Computer simulation results obtained with the proposed system in real and synthetic scenes are presented and
discussed in terms of pattern recognition performance and real-time operation speed.
A two-step procedure for the reliable recognition and multiclassication of objects in cloudy environments is proposed. The input scene is preprocessed with the help of an iterative algorithm to remove the effects of the cloudy environment, followed by a complex correlation filtering for the multiclassication of target objects. The iterative algorithm is based on a local heuristic search inside a moving window using a nonlinear signal model for the input scene. The preprocessed scene is correlated with a multiclass correlation filter based in complex synthetic discriminant functions. Computer simulation results obtained with the proposed approach in cloudy images are presented and discussed in terms of different performance metrics.
A new iterative algorithm for the improvement of the visual perception in cloudy environments is presented. The proposed
approach is based on an heuristic search algorithm used to estimate the depth map of a scene taken under bad
weather conditions. By the use of the suggested algorithm, an undegraded signal can be locally estimated at in an iteratve
way, increasing the confidence in the decision making in computer vision applications for human assistance. Computer
simulation results obtained with the proposed algorithm are provided and discussed in terms of performance metrics and