Image processing has a wide range of applications especially in our daily lives. Image processing is not common in sensitive industrial applications. Because of these applications, very high percentage of success is requested. Also these applications work in real-time. However, it can be widely used in many daily routines (driving, entrance to the workplace/ exit, control of multimedia devices, security applications, identification applications, etc.). Especially Advanced Driver Assistance Systems (ADAS) is a popular working area for image processing. Strip tracking systems, pedestrian detection systems, reading of traffic signs and signals are based on image processing.In this study, a new method has been developed to increase the visibility levels of road images at night driving. In these images, the brightness level is low because of insufficient of light sources (headlights and road lighting) which are often used to increase the driver's view. On the other hand, adversely affects the view of driver which the headlight of coming vehicles from opposite directions, poorly structured road lighting and etc. Especially the vehicle headlights coming from the opposite direction take the eye of the drivers and cause the level of view to decrease.Intense dark areas and light sources are in the image together. By so, special to these images requires the use of an adaptive improvement method. This is because, when classical image enhancement methods are used, the visibility levels of the dark areas are increased, and the shining regions are more likely to shine and the visibility level decreases in these regions.The developed method aims at enhancement these images that drivers be exposed to. For this purpose, the light sources in the image and the magnitudes of these light sources, the distance of the pixels to be calculated from the light sources, the value of the pixel itself and the neighboring pixels are used as separate parameters. Images are enhancement with the equations developed using these parameters. When the output images obtained with the use of the developed equations and the obtained Structural Similarity İndex Maps (SSIM) are examined, it is seen that the developed method gives good results.
Abstract:In this study, the aim is to extract the attributes of the eye regions of laptop users. To achieve this, the iris and eye corners are detected by processing the images captured by the standard internal webcam of a laptop. In addition, an artificial neural network (ANN) is used for determining the eye region. Hereby, the iris and eye corners can be detected in the determined eye region. In the study, 107 user images are captured by using a laptop's internal camera under different light intensities, environments, viewpoints, and positions. These images are used for the training of the ANN. Two different methods are used for the iris detection. In the first, circular Hough transform (CHT) is employed for iris detection in the determined eye region. In the second, the right and the left iris regions are determined by using two different ANNs respectively and then CHT is employed for the iris. Higher success rates are achieved by the second method. In the next stage of the study, two different methods, weighted variance projection (WVPF) function and lowest valued pixels (LVP), are used for the detection of the eye corners. It is demonstrated that the second method has a higher performance than the first.
In this study, a new adaptive filter is proposed to eliminate salt and pepper noise (SPN). The basis of the proposed method consists of two-stages. (1) Changing the noisy pixel value with the closest pixel value or assigning their average to the noisy pixel in case there is more than one pixel with the same distance; (2) the updating of the calculated noisy pixel values with the average filter by correlating them with the noise ratio. The method developed was named as Nearest Value Based Mean Filter (NVBMF), because of using the pixel value which the closest distance in the first stage. Results obtained with the proposed method: it has been compared with the results obtained with the Adaptive Frequency Median Filter, Adaptive Riesz Mean Filter, Improved Adaptive Weighted Mean Filter, Adaptive Switching Weight Mean Filter, Adaptive Weighted Mean Filter, Different Applied Median Filter, Iterative Mean Filter, Two-Stage Filter, Multistage Selective Convolution Filter, Different Adaptive Modified Riesz Mean Filter, Stationary Framelet Transform Based Filter and A New Type Adaptive Median Filter methods. In the comparison phase, nine different noise levels were applied to the original images. Denoised images were compared using Peak Signal-to-Noise Ratio, Image Enhancement Factor, and Structural Similarity Index Map image quality metrics. Comparisons were made using three separate image datasets and Cameraman, Airplane images. NVBMF achieved the best result in 52 out of 84 comparisons for PSNR, best in 47 out of 84 comparisons for SSIM, and best in 36 out of 84 comparisons for IEF. In addition, values nearly to the best result were obtained in comparisons where the best result could not be reached. The results obtained show that the NVBMF can be used as an effective method in denoising SPN.
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