Face Recognition on Linear Motion-blurred Image

Most face recognition algorithms are generally capable to achieve a high level of accuracy when the image is acquired under wellcontrolled conditions. The face should be still during the acquisition process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand still during the process is impractical; extremely likely that recognition should be performed on a blurred image. It is important to understand the relation between the image blur and the recognition accuracy. The ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty different persons, ten images per person. Those images were randomly divided into training and testing datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in the testing datasets were artificially blurred to represent a linear motion, and recognition was performed. The blurred images were also filtered using various methods. The accuracy levels of the recognition on the basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that at its best, the image improvement processes are capable to improve the recognition accuracy level by less than five percent

Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum

Motion blur is one of the most common causes of image degradation. Restoration of such images is highly dependent on accurate estimation of motion blur parameters. To estimate these parameters, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts. The most important advantage of this algorithm is its robustness and precision in noisy images. This method was tested on a wide range of different types of standard images that were degraded with different directions (between 0° and 180°) and motion lengths (between 10 and 50 pixels). The results showed that the method works highly satisfactory for SNR >22 dB and supports lower SNR compared with other algorithms

Trajectory Optimization of a Mobile Camera System for Maximizing Optical Character Recognition

Camera systems in motion are subject to significant blurring effects that lead to a loss of information during the image capture. This is especially damaging for optical character recognition for which edge preservation is critical to achieving a high recognition rate. Using non-blind motion deblurring, a trajectory and point spread function can be designed to maximize the recognition rate while meeting endpoint constraints. Optimization through the use of radial basis function networks can therefore be used as a way to find ideal trajectories to reduce blurring effects and preserve text sharpness. This work investigates this problem using simulation of a blurred image capture process. The simulation is automated using radial basis function network optimization and a genetic algorithm to determine trajectories with the best recognition rate. Optimized trajectories yielded recognition scores with up to 57.3% improvement in simulation compared to an analogous linear profile. These results were then verified through physical experimentation with a real-world, controlled-blur image capture process that yielded up to 29.4% improvement across the same comparison. Results were then analyzed using spectral analysis to understand why the chosen trajectories preserve text edges. These findings can be applied to a wide variety of controlled mobile camera platforms, such as autonomous automobiles or unmanned aerial vehicles, to improve their ability to gather information from their environment.M.S