No abstract

We present a study concerning the practical possibilities of using the homomorphic filtering for color image enhancement. Two of the most popular color models, RGB and C-Y (color difference), are employed and the results are comparatively discussed. The homomorphic filtering has proven to be a viable tool for both color models considered.

Many approaches to target recognition on SAR images employ model-based techniques. These systems incorporate computationally intensive operations such as large database probing or complex 3D renderings that are used to produce simulations that are compared against unknown targets. These operations would achieve a significant improvement in speed performance if the target poses were known in advance. A study that addresses the problem of estimating the poses of vehicles in SAR images is reported in this paper. A pose estimation algorithm suite is proposed that is based on a set of partially independent criteria. A statistical analysis of the performance obtained by employing the established criteria, both individually and in combination, is also conducted and the results are comparatively discussed.

Background clutter characterization in infrared imagery has become an actively researched field, and several clutter models have been reported. These models attempt to evaluate the target detection and recognition probabilities that are characteristic of a certain scene when specific target and human visual perception features are known. The prior knowledge assumed and required by these models is a severe limitation. Furthermore, the attempt to model subjective and intricate mechanisms such as human perception with general mathematical formulas is controversial. In this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised learning mechanism based on genetic programming foundations. A set of characteristic scene and target features with a demonstrated influence on the human visual perception mechanism is first extracted from the original images. Then, the correlations between these features and detection performance results obtained by visual observer tests on the same set of images are captured into models by a learning algorithm. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper.

Due to the special features of the homomorphic filter, relying on its capabilities of selectively enhancing blurred images with poor contrast and nonuniform illumination, a study concerning the possibility of applying it to RGB (24-bit true color) images has been made. Moreover, the effects of different shapes for the linear filter employed by the process are discussed and illustrated using a classical high pass buUerworth filter modified for more flexibility in the final enhancement. An image of poor quality in terms of blurring and nonuniform illumination was used to demonstrate the results of different stages of the filtering process. It is shown that homomorphic filtering is a viable tool for enhancing poor quality RGB images.