In recent years, many proposals that consider an adaptive perspective had been developed to solve some drawbacks, such as geometric distortions, background noise and target discrimination. The metrics are based only in the correlation peak output for the filter synthesis. In this paper, the correlation shape is studied to implement adaptive correlation filters guided by the peak and shape of the correlation output. Furthermore, the shape of correlation output is studied to improve the search in the filters bank. In addition, parallel algorithms are developed for accelerated the search in the filters bank. Some results are shown, such as time of synthesis, filter performance and comparisons with other adaptive correlation filter proposals.
An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.
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 complex-composite correlation filter for the simultaneous recognition and classification of several targets is presented. By using both the intensity and phase distributions of the output complex-correlation plane, a reliable recognition and classification of several objects can be easily carried out with only one correlation operation. The input objects are recognized using the intensity distribution of the output correlation plane, whereas recognized targets are classified using the phase values at the location of maximum intensities. Computer simulation results obtained with the proposed approach in geometrically distorted input test scenes are provided and discussed in terms of recognition performance, classification ability, and computational complexity.