Murtaza Khan

Sohar University, Faculty of Computing and Information Technology, Faculty Member

Umm Al-Qura University, Makkah, Saudi Arabia, Computer Science, Faculty Member

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Dr. Murtaza Ali Khan is Assistant Professor in the Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia. He obtained his PhD from Keio University, Japan in 2008. He is author of one book and several papers in referred journals and conferences. His areas of research include signal/image/video processing, data compression, and computer graphics. Dr. Khan received many awards including Excellence in Research Award, Yoshida Scholarship Award, and research grant from Keio Leading-edge Laboratory of Science and Technology, Japan.

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Asha Vijayan

Amrita Vishwa Vidyapeetham

Gerassimos Barlas

American University of Sharjah

Norhaida Mohd Suaib

Universiti Teknologi Malaysia - UTM

Annelise Barron

Stanford University

Rossana Baptista Queiroz

Unisinos

Fusun Balik Sanli

Yildiz Technical University

Jean Doumit

Lebanese University

Hamid Ur-Rahman

Quaid-i-Azam University, Islamabad, Pakistan

Christopher S Dick

University of Music and Performing Arts Graz (Austria)

Himawan Wijaya

POLITEKNIK MANUFAKTUR ASTRA

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Modeling and compression of motion capture data

2017 Learning and Technology Conference (L&T) - The MakerSpace: from Imagining to Making!, 2017

Motion capture (MoCap) system uses sensors or markers, placed on human body joints, to record the... more Motion capture (MoCap) system uses sensors or markers, placed on human body joints, to record the movements of a human in space over time. Motion capture data is used in many entertainment applications such as in virtual reality environments to drive avatars, in video games to animate characters, in movies to produce CG effects, etc. In this paper, we present an efficient method for modeling and compression of motion capture data. The method uses quadratic Bezier curve fitting to smoothly model and compress the MoCap data. The temporal variation of MoCap data of each joint is approximated and parameterized using Bezier segments. Simulation results shows that our method uses smaller storage and better visual quality compared to other methods. The low degree of quadratic Bezier curve ensures computationally efficiency required for the realtime gaming applications.

Detection and Classification of Plant Diseases Using Image Processing and Multiclass Support Vector Machine

International Journal of Computer Trends and Technology

Identification of plant disease is very important to prevent the loss and keep the harvest health... more Identification of plant disease is very important to prevent the loss and keep the harvest healthy. Determination of plant disease via visual monitoring is difficult and time consuming. In this paper, we described a method of detection and classification of plant disease using image processing and machine learning techniques. We used standard images of leaves of several types of plants to test our method. Initially, our method segments the input image to isolate disease parts of the leaf. Then we obtain various features from the diseased affected segmented image. Finally, we classify leaves into healthy and disease types based on its features using Multiclass Support Vector Machine (SVM) classifier. Experimental results indicate that our method yields very high accuracy rate for detection and classification of plant diseases.

Murtaza Khan

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