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.
Correlation filters for pattern recognition are commonly designed under the assumption that the shape and appearance of an object of interest are explicitly known. In this paper, we consider a signal model in which an object of interest is given at unknown coordinates in a cluttered reference image and corrupted by additive noise. The reference image is used to design filters for detecting a target in scenes with a nonoverlapping background and additive noise. An optimum correlation filter with respect to peak-to-output energy for object detection is derived. The shape and appearance of the target are estimated from the reference image. Two methods to estimate the frequency response of the derived filter are used. Computer simulation results obtained with the proposed filters are presented and discussed. The performance of the filters is evaluated in terms of discrimination capability and location accuracy for different statistics of the backgrounds and noise processes present in the signal model.
Correlation filters for target detection are usually designed under the assumption that the appearance of a target
is explicitly known. Because the shape and intensity values of a target are used, correlation filters are highly
sensitive to changes in the target appearance in the input scene, such as those of due to rotation or scaling.
Composite filter design was introduced to address this problem by accounting for different possibilities for the
appearance of the target within the input scene. However, explicit knowledge for each possible appearance is
still required. In this work, we propose composite filter design when an object to be recognized is given in noisy
training images and its exact shape and intensity values are not explicitly known. Optimal filters with respect
to the peak-to-output energy criterion are derived and used to synthesize a single composite filter that can be
used for distortion invariant target detection. Parameters required for filter design are estimated with suggested
techniques. Computer simulation results obtained with the proposed filters are presented and compared with
those of common composite filters.
Classical correlation filters for object detection and location estimation are designed under the assumption that
the shape and intensity values of the object of interest are explicitly known. In this work we assume that
the target is given at unknown coordinates in a reference image with a cluttered background corrupted by
additive noise. We consider the nonoverlapping signal model for both the reference image and the input scene.
Optimal correlation filters, with respect to signal-to-noise ratio and peak-to-output energy, for object detection
and location estimation are derived. Estimation techniques are proposed for the parameters required for filter
design. Computer simulation results obtained with the proposed filters are presented and compared with those
of common correlation filters.
Classical correlation filters for object detection and location estimation are designed assuming that the appearance
and the shape of the target are explicitly known. In this work we assume that the target is given at
unknown coordinates in a reference image corrupted by additive noise. Optimal correlation filters, with respect
to signal-to-noise ratio and peak-to-output energy, for object detection and location estimation are derived. Two
mathematical models of observed images are used; the additive noise model for the reference image and the
non-overlapping background model for the input scene. Computer simulation results obtained with the proposed
filters are presented and compared with those of common correlation filters.
Classic correlation filters for detection and location estimation of a reference require explicit information about the object to be recognized. In this work we assume that a reference signal is not available. However, we suppose that the target is placed at unknown coordinates in a noisy reference image. Optimal correlation filters with respect to signal-to-noise ratio and peak-to-output energy for detection and localization of a target embedded into an input scene are derived. Computer simulation results obtained with proposed filters are presented and compared with those of common correlation filters.
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.