Image Processing and Computer Vision Research Group
Introduction
The Image Processing and Computer Vision Research Group is a dynamic and multidisciplinary team that advances the field of visual computing through cutting-edge image processing, machine learning, and deep learning techniques. The group’s research spans several thematic areas, including image and video analysis, where they develop algorithms for tasks such as object detection, segmentation, and motion tracking. Their work in machine learning and deep learning for vision utilizes advanced neural network architectures like CNNs, GANs, and transformers to enable systems that perform tasks such as facial recognition, scene understanding, and activity recognition. In addition, the group explores augmented and virtual reality (AR/VR), enhancing immersive user experiences by enabling real-time interactions between the digital and physical worlds. Another significant focus is on computer vision for robotics, developing vision-based solutions that allow robots to navigate autonomously, interact with objects, and understand human actions.
The group’s research has substantial real-world applications across diverse industries including healthcare, video analytics, and agriculture. The Image Processing and Computer Vision Research Group is composed of a collaborative mix of faculty members, researchers, and graduate students who work closely together to address challenges in visual computing. Faculty members bring extensive expertise in artificial intelligence, computer vision, and robotics, guiding research projects and securing funding for advanced studies. The research group fosters a dynamic environment where students have opportunities to collaborate with industry partners, engage in high-impact research, and contribute to solving real-world problems.
2. Research Areas
3. Research Group Members
| Sr. # | Name | Designation | Specialistion | Contact Details |
|---|---|---|---|---|
| a. | Dr. Ihtesham ul Islam | Associate Professor | Computer Vision/ Machine Learning | [email protected] |
| b. | Dr. Asad Ullah | Assistant Professor | Artificial Intelligence, Deep Learning, Pattern recognition | [email protected] |
| c. | Dr. Nida Adnan | Assistant Professor | Computer Vision, Machine Learning, Data Science | [email protected] |
| d. | Dr. Maemoona Farooq | Assistant Professor | Image processing, Image/Video encryption, and Machine learning | [email protected] |
| e. | Dr. Nauman Ali Khan | Assistant Professor | Machine Learning, Social Network Analysis, Wireless Big Data Mining | [email protected] |
4. Publications
- “Subclass discriminant analysis of morphological and textural features for hep-2 staining pattern classification”, S Di Cataldo, A Bottino, IU Islam, TF Vieira, E Ficarra, Pattern Recognition 47 (7), 2389-2399
- “Multi-feature-based crowd video modeling for visual event detection”, H Ullah, IU Islam, M Ullah, M Afaq, SD Khan, J Iqbal, Multimedia Systems
- “Fusion of machine learning and privacy preserving for secure facial expression recognition” A Ullah, J Wang, MS Anwar, A Ahmad, S Nazir, HU Khan, Z Fei, Security and Communication Networks 2021 (1), 6673992
- M Shoaib, B Shah, S Ei-Sappagh, A Ali, A Ullah, F Alenezi, T Gechev, “An advanced deep learning models-based plant disease detection: A review of recent research”, in Frontiers in Plant Science 14, 1158933
- SMAH Shah, A Ullah, J Iqbal, S Bourouis, SS Ullah, S Hussain, MQ Khan “Classifying and localizing abnormalities in brain MRI using channel attention based semi-Bayesian ensemble voting mechanism and convolutional auto-encoder”, in IEEE Access 11, 75528-75545
- Rasheed, S. A. Khan, A. Hassan and S. Safdar, “A Decision Support Framework for National Crop Production Planning,” in IEEE Access, vol. 9, pp. 133402-133415, 2021, doi: 10.1109/ACCESS.2021.3115801.
- Nida Rasheed; Waqar S. Qureshi; Shoab A. Khan; Manshoor A. Naqvi; Eisa Alanazi. AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds. IEICE Transactions on Information and Systems 2021, E104.D, 490 -499.
- M Kayani, A Ghafoor, MM Riaz, “Multi-modal text recognition and encryption in scanned document images”, in The Journal of Supercomputing 79 (7), 7916-7936
- F Arif, NA Khan, J Iqbal, FK Karim, N Innab, SM Mostafa, “DQQS: Deep Reinforcement Learning based Technique for Enhancing Security and Performance in SDN-IoT Environments” in IEEE Access
- MH Zafar, NM Khan, AF Mirza, M Mansoor, N Akhtar, MU Qadir, NA Khan, “A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition” in Sustainable Energy Technologies and Assessments 47, 101367
5. PhD Students
- Muhammad Mudassir Iqbal
- Sumaira Shaukat
- Mobina Zafar
- Shazia Yousaf
6. Projects
- WriteRight (PEC Funded FYDP 2024) WriteRight arises from the recognition that technology can be harnessed to make the process of improving handwriting engaging and accessible. We have developed a gamified handwriting improvement app that makes learning and practicing handwriting fun and rewarding. The app leverages the latest technologies, including Flutter, Firebase, and Django, to provide an interactive and user-friendly interface, a secure and scalable database, and a smart and accurate AI engine.
7. Research Group Facilities
8. Contact Person
Dr. Ihtesham Islam ([email protected])