Digital Image Processing Using MATLAB

TL;DR: This paper proposes an algorithmic solution for the bunch-drop fault detection for both glass and ceramic insulators based on spatial morphological features, which can be integrated into an unmanned aerial vehicle-based inspection system.

TL;DR: An innovative and intelligent method based on deep learning and image processing technologies is proposed for the online condition monitoring of the pantograph slide plate and demonstrates that the proposed PDDNet can detect the surface defects and also recognize the four kinds of defects with a sound accuracy.

TL;DR: The proposed image segmentation algorithm was able to correctly detect 97% of the defective orange and also presents the detailed image processing procedure including removal of background pixels, image binarization using local segmentation, image subtraction, image morphological modification, removal of stem end pixels for detecting surface defect in an orange gray-level image.

TL;DR: The automated separation of coal-gangue and increased efficiency of raw coal sorting and quality of coal can be achieved if the method proposed in this paper can be applied in coal industry.

TL;DR: In this work wet granulation experiments were carried out in a planetary mixer and the method proved to be robust enough to easily monitor the process and its use for more refined analyses on the different rate processes occurring during granulation is suggested.

TL;DR: This project is using image detection system for indicating the vacant places in parking lots and use of hydraulic lift for lifting and lowering of cars from floor to floor in Kolhapur city and increases the efficiency of parking the cars.

TL;DR: The performance of the filter was improved by including the mathematical morphology operations along with adaptive bilateral filter process and was judged by three metrics; mean square error (MSE), structure similarity index (SSIM) and peak signal to noise ratio (PSNR).