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Mehdi Masoodi
Young Researchers and Elite Club
Electrical & Electronics Engineering

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Flying object tracking and classification of military versus nonmilitary aircraft Ehsan Akbari Sekehravani; Eduard Babulak; Mehdi Masoodi
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (984.791 KB) | DOI: 10.11591/eei.v9i4.1843

Abstract

Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the direction of motion of objects, and thermal imagery identifies the type of tracked flying objects. To demonstrate the efficiency and the strength of the algorithm and the above system, several scenarios in different videos have been investigated that include challenges such as the number of objects (aircraft), different paths, the diverse directions of motion, different speeds and various objects. One of the most important challenges is the speed of processing and the angle of imaging.
Implementing canny edge detection algorithm for noisy image Ehsan Akbari Sekehravani; Eduard Babulak; Mehdi Masoodi
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1415.746 KB) | DOI: 10.11591/eei.v9i4.1837

Abstract

Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.
Co-Authors Eduard Babulak Eduard Babulak Ehsan Akbari Sekehravani