Disparity refinement is a post-processing step in stereo vision that retrieves unknown disparity values caused by pixel occlusions or estimation errors. This step is crucial for improving depth estimation accuracy and reducing artifacts. In this work, we propose an iterative method based on genetic optimization to perform disparity refinement for stereo vision. The estimation of unknown disparity values is formulated as an optimization problem, where a fitness function is optimized by minimizing a trade-off between disparity variations and point correspondence errors. The proposed method achieves accurate refined disparity maps for stereo depth estimation. Computer simulation results are presented and discussed in terms of objective performance measures. Additionally, the results are compared with those obtained using a well-known existing method.
Correlation filters have been widely used in several pattern recognition applications. These filters can reliably detect and accurately locate a target with good tolerance to geometrical modifications and the presence of additive and nonoverlapping noise in the scene. This work presents an exhaustive performance evaluation of several advanced correlation filters for the task of printed character recognition. Several printed character strings in the English alphabet containing geometrical modifications and nonuniform illumination conditions are recognized using different advanced correlation filters. The performance of each tested filter is characterized in terms of efficiency of character recognition and accuracy of character location estimation.
Image restoration is a classic problem in image processing. Image degradations can occur due to several reasons, for instance, imperfections of imaging systems, quantization errors, atmospheric turbulence, relative motion between camera or objects, among others. Motion blur is a typical degradation in dynamic imaging systems. In this work, we present a method to estimate the parameters of linear motion blur degradation from a captured blurred image. The proposed method is based on analyzing the frequency spectrum of a captured image in order to firstly estimate the degradation parameters, and then, to restore the image with a linear filter. The performance of the proposed method is evaluated by processing synthetic and real-life images. The obtained results are characterized in terms of accuracy of image restoration given by an objective criterion.
Scale estimation of objects is a challenging problem in image processing. This work presents a novel method to detect and estimate the scaling factor of a target in an observed scene corrupted with additive noise and clutter. Given a set of available views of the target the proposed method is able to detect the target and estimate its scaling factor using a template matched filters and a scale pyramidal representation. The performance of the proposed method is evaluated in synthetic and real-life scenes in different pattern recognition applications. The obtained results are characterized in terms of objective metrics.
The perspective and lens distortions induced by the imaging system of a camera device are corrected by using
an elementary geometrical approach. We propose a simple method based on the use of a crossed grating in the
reference plane and a phase demodulation process. Preliminary results showing the performance of the proposed
method are discussed.
A performance evaluation of several state-of-the-art correlation filters within the context of target tracking is presented. The filters are tested using an introduced algorithm that is adapted online using information of current and past scene frames of the scene. The algorithm achieves a high-rate operation by focusing signal processing on a small fragment of the scene in each frame. The correlation filters are tested using several video test sequences that contain geometric modifications of the target, partial occlusions and clutter. The performance of the tested filters is characterized in terms of detection efficiency, tracking accuracy, and computational complexity using objective metrics.
A reliable method for real-time target tracking is presented. The method is based on an interest point detector and a bank of locally adaptive correlation filters. The point detector is used to identify local regions in the observed scene around potential location of the target. The bank of correlation filters is employed to reliably detect the target and accurately estimate its position within the scene, by processing the local regions identified by the detector. Using information of past state estimates of the target the proposed algorithm predicts the state of the target in the next frame in order to perform a fast and accurate target tracking by focusing signal processing only on small regions of the scene in each frame. In order to achieve a real-time operation performance the proposed algorithm is implemented in a graphics processing unit. Experimental results obtained with the proposed method are presented, discussed, and compared with those obtained with a similar state-of-the-art target tracking algorithm.
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.