Browsing by Author "Ayten, K. K."

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Real-Time Dehazing Via Multiscale Products for Vision Control
(IEEE, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.
In this study, a dehazing algorithm based on multiscale product (MSP) prior is presented. In this method, first, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image is calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real time robotic vision control system.
Real-Time Implementation of Image Based PLC Control for a Robotic Platform
(2019) Ayten, K. K.; Kurnaz, O.
In this study, image based real-time control of a linear robotic platform was performed. This robotic platform is used to determine the location of the mushroom and to direct the linear platform to the detected position in real time with PLC control. Haar-Cascade classifier was used to detect mushroom position and Visual Studio C # .NET platform was used to test the Cascade classifier and write other evaluation codes. One of the most important outputs of this work is to determine the actual position in the global coordinate from the pixel-based location of the object in the image using an ordinary USB camera or built-in camera. Calibration technique was used for this determination.
Single Image Dehazing Based on Multiscale Product Prior and Application to Vision Control
(Springer London Ltd, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.
In this paper, a novel dehazing algorithm based on multiscale product (MSP) prior is presented. First, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image are calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real-time robotic vision control system.