Mohammad Tanvir Parvez

Work place: Qassim University, Computer Engineering Department, Qassim, Saudi Arabia



Research Interests: Pattern Recognition, Image Processing


Dr. Mohammad Tanvir Parvez is an Associate Professor in Computer Engineering Department at Qassim University. He obtained his Ph.D. in CSE from King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia in 2010. His research interests include Pattern Recognition, Image Processing and Machine Learning with the special interest in handwriting recognition using structural approach. He has received several awards including Best Poster Award in ICFHR 2012.

Author Articles
A Comparative Study of Recent Steganography Techniques for Multiple Image Formats

By Arshiya Sajid Ansari Mohammad Sajid Mohammadi Mohammad Tanvir Parvez

DOI:, Pub. Date: 8 Jan. 2019

Steganography is the technique for exchanging concealed secret information in a way to avoid suspicion. The aim of Steganography is to transfer secrete message to another party by hiding the data in a cover object, so that the imposter who monitors the traffic should not distinguish between genuine secret message and the cover object. This paper presents the comparative study and performance analysis of different image Steganography methods using various types of cover media ((like BMP/JPEG/PNG etc.) with the discussion of their file formats. We also discuss the embedding domains along with a discussion on salient technical properties, applications, limitations, and Steganalysis.

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Image Processing Based Computational Tools for Assisting and Assessing Memorization and Learning Tasks

By Mohammad Tanvir Parvez Sameh Otri

DOI:, Pub. Date: 8 Nov. 2017

In this paper, we present a novel computational framework for assisting and assessing memorization tasks. Such a framework can be used in any cases where certain level of memorization is needed, like in memorizing words/sentences, learning (programming) language structures, etc. We aim to identify the common memorization steps followed in various disciplines and then automate some of these steps to enhance memorization process. Particularly, we focus on annotation of texts (used for memorization) based on state of the art image processing techniques. Once texts are annotated and optionally commented, personalized tests can be automatically generated, focusing on the weakness of a particular student. These tests can further enhance the memorization process. As a case study, we have implemented the framework for a classical example of memorization: memorizing the Qur’an, the sacred book in Islam. Qur’an memorization is a well-known process since the early days of Islam and represents an ideal case for implementing the proposed framework.

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JPEG Image Steganography based on Coefficients Selection and Partition

By Arshiya Sajid Ansari Mohammad Sajid Mohammadi Mohammad Tanvir Parvez

DOI:, Pub. Date: 8 Jun. 2017

In this paper, we propose a novel JPEG image Steganography algorithm based on partition schemes on image coefficient values. Our method selects the AC and DC coefficients of a JPEG image according to a channel selection method and then identifies appropriate coefficients to store the secret data-bits. As opposed to other reported works, in our algorithm each selected coefficient can store a variable number of data-bits that are decided using the concept called ‘Partition Scheme’. Experimental results indicate the suitability of the proposed algorithm as compared to other existing methods. 

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