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Name Afshan Jamil
Designation Assistant Professor
Department Computer Engineering (Taxila)
Highest Qualification PhD Computer Engg.. UET Taxila
Specialization Multimedia and image processing, Image and video retrieval, Digital image processing, Video content analysis
Phone No 051 9047588
Cell No
Fax No
Email [email protected]

PhD Computer Engineering 

University of Engineering & Technology Taxila

Msc. Computer Engineering

University of Engineering & Technology Taxila

Bsc. (Hons) Computer Engineering (Gold Medalist)

University of Engineering & Technology Taxila

Visual saliency aware content based image and video retrieval in compressed domain (PhD thesis)

Content based retrieval is becoming an active research area from so many years. Content based retrieval systems are time efficient and use contents of visual data to search for relevant material. Automatic indexing relieves human beings from doing indexing task thus saving both time and effort. Content based retrieval systems have applications in military, art collection, image/video categorization, object detection, medical diagnosis, crime prevention, textile industry, digital libraries, finger print identification etc. Currently in the field of image and video processing research is focusing more towards content based retrieval which is time and space efficient. Limited space and bandwidth demands images and videos to be stored and transferred in compressed form. Majority of images and videos on web are stored in compressed form. Compression has increased the overhead of pixel based retrieval systems because images and videos needs to be decompressed for feature extraction. Current research trends in content based retrieval system is in compressed domain, which is computationally more efficient because features are extracted directly from compressed images and videos.

The main aim of this research is to propose content based image and video retrieval system using visual saliency models that results into computationally efficient system.

Environment Augmentation for Driver Assistance (MS Thesis)

This research work focuses on designing an augmented reality based system for novice drivers by overlaying future position of vehicle on windscreen, assuming the movement of vehicle in straight line. Future longitudinal information of vehicle is projected over windscreen that assists driver in estimating future positions of vehicle over road with ease. Windscreen of vehicle serves as augmented display. Each line extends over wind screen to give visualization as if lines are physically drawn over road according to the width of either side of vehicle. The novice driver gets trained by this projected information about the width of vehicle. The position of these lines varies according to head pose of driver and is adjusted accordingly.

Multimedia and Image processing, Image and video retrieval, Artificial Intelligence, Visual Saliency.

1. Afshan Jamil, Muhammad Majid, Syed Muhammad Anwar, " An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain", Arabian Journal for Science and Engineering, 25 April 2019, https://doi.org/10.1007/s13369-019-03880-0 (IF= 1.518)

Final Year Projects Supervised:

  • Gestures to speech conversion system for assisting blind and deaf
  • Text to speech conversion system
  • Intelligent image retrieval system for university database
  • e-learning app for kids
  • Automatic garbage collection robot for classroom environment
  • e-cure: health monitoring system
  • Student course managaement system
  • A quadruped stair climbing robot for search and rescue applications (In progress)
  • Context aware activity recognition (In progress)
  • Warden Girls Hostel
  • Final year project Adviser
  • Class Adviser of 2nd year

Assistant Professor (Dec 2012 onwards )

UET Taxila

Lecturer (Dec 2008 to Dec 2012)

UET Taxila

  • Computer Architecture & Organization
  • Artificial Intelligence
  • Digital Logic Design
  • Computer Programming
  • Engineering Drawing & Workshop
  • Computer Communication & Networks

Award of Gold medal in BS (Computer Engg.)

MS Thesis Supervised:

  • Fire detection in crowded videos
  • Artifact Removal from EEG Signals Recorded in Non-Restricted Environment.
  • Two-Wheeled Vehicle Detection for Intelligent Transportation System
  • Feed Forward Instructor's Emotion Recognition Approach using Deep Neural Network and Regularized Extreme Learning Machine

Professional Competency Enhancement Teacher's Training Program (PCEPT). 

Currently three MS full time students are attached with me.