In boundary zones (edges) in synthetic apertue radar (SAR) images, there is no logical (scientific) explanation for employing the kernel average for approximating the backscattering factors in central kernel pixels. Therefore, adaptive filters are used to decrease the speckles in these images. These filters prevent the averaging process in the edges when smoothing the images (noise reduction) in homogeneous areas, thus causing reduction of lucidity in the edges. In most existing adaptive filters, the variation coefficient (coefficient of variation) is used to detect the edges in the images. An alternative factor is introduced to detect the edges in intensity images; by employing this factor, an adaptive filter is presented. The results of evaluating this filter and comparing it with other adaptive filters illustrate that it has excellent competency in speckle reduction, and hence it protects the edges of the images during the filtering process. using various assessment measures (such as signal-to-noise ratio and mean absolute error), the obtained results provide evidence that the proposed filtering process outperforms other classical methodologies.

In this paper, an adaptive visual feedback system and controller has been designed and implemented in real-time to control the movements of a line follower robot to be smoother and faster. The robot consists of a couple of motorized wheels, the real-time controller and a CMOS camera as the only sensor for detecting line and feedback. The measurement based on real-time image processing and motor drive feedback used in this robot makes it robust to the obstacles and surface disturbances that may deviate robot. The image processing algorithm is adaptive to the line’s color and width too. Image processing techniques have been implemented in real-time to detect the line in the image frame and extract the necessary information (like line’s edge, coordinates and angle). A NI myRIO module is used as a stand-alone hardware unit and RT (Real-Time) target for implementation of controllers and image processing in LabVIEW environment. Both results of real-time and non-real-time implementation of controllers have been compared. To show the performance of real-time image processing in the control of this robot, three types of controllers (i.e. P, PI and Fuzzy controllers) have been implemented for line following tests and the results have been compared. At the end, it was found that the fuzzy controller controls the robot movements smoother, faster, with less errors and quicker response time compare to the other controllers

The measurement of surface pressure in wind tunnel or in flight tests gives very important data for aerodynamics analysis. In fact, the pressure measurement gives valuable information about air flow phenomena such as shock, flow separation and boundary layer transition. In this paper a real-time image processing system for pressure sensitive paint (PSP) measurement and calibration has been designed using a calibrated pressure sensor. In the implementation of the system, TCL paper is used to luminaphore inside a windowed enclosure and a factory-calibrated pressure sensor for validation of measurement. The pressure measurement of PSP is based on the images acquired from a CCD camera sent to the MyRIO processor module in which the real-time image processing has been applied. The results are compared with the calibrated sensor from which the A and B coefficients of the Estern Volmer Equation have been calculated. During the several tests done for the pressure measurement, the accuracy of the PSP measurement has been calculated in this paper. Both normal and real-time image processing analysis have been compared as well.