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Fahad Layth Malallah
Ninevah University
Computer Science & IT Electrical & Electronics Engineering

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Reversible color video watermarking scheme based on hybrid of integer-to-integer wavelet transform and Arnold transform Fahad Layth Malallah; Awatif Ali Jafaar; Nidaa Hasan Abbas; Mustafa Ghanem Saeed
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.156 KB) | DOI: 10.11591/ijece.v10i4.pp3519-3527

Abstract

Unauthorized redistribution and illegal copying of digital contents are serious issues which have affected numerous types of digital contents such as digital video. One of the methods, which have been suggested to support copyright protection, is to hide digital watermark within the digital video. This paper introduces a new video watermarking system which based on a combination of Arnold transform and integer wavelet transforms (IWT). IWT is employed to decompose the cover video frames whereby Arnold transform is used to scramble the watermark which is a grey scale image. Scrambling the watermark before the concealment makes the transmission more secure by disordering the information. The system performance was benchmarked against related video watermarking schemes, in which the evaluation processes consist of testing against several video operations and attacks. Consequently, the scheme has been demonstrated to be perfectly robust.
Hand detection and segmentation using smart path tracking fingers as features and expert system classifier Khaled N. Yasen; Fahad Layth Malallah; Lway Faisal Abdulrazak; Aso Mohammad Darwesh; Asem Khmag; Baraa T. Shareef
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5.602 KB) | DOI: 10.11591/ijece.v9i6.pp5277-5285

Abstract

Nowadays, hand gesture recognition (HGR) is getting popular due to several applications such as remote based control using a hand, and security for access control. One of the major problems of HGR is the accuracy lacking hand detection and segmentation. In this paper, a new algorithm of hand detection will be presented, which works by tracking fingers smartly based on the planned path. The tracking operation is accomplished by assuming a point at the top middle of the image containing the object then this point slides few pixels down to be a reference point then branching into two slopes: left and right. On these slopes, fingers will be scanned to extract flip-numbers, which are considered as features to be classified accordingly by utilizing the expert system. Experiments were conducted using 100 images for 10-individual containing hand inside a cluttered background by using Dataset of Leap Motion and Microsoft Kinect hand acquisitions. The recorded accuracy is depended on the complexity of the Flip-Number setting, which is achieved 96%, 84% and 81% in case 6, 7 and 8 Flip_Numbers respectively, in which this result reflects a high level of finite accuracy in comparing with existing techniques.
Co-Authors Asem Khmag Aso Mohammad Darwesh Awatif Ali Jafaar Baraa T. Shareef Khaled N. Yasen Lway Faisal Abdulrazak Mustafa Ghanem Saeed Nidaa Hasan Abbas