Usman Nazir
Usman Nazir is a PhD Student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Usman Nazir is a PhD Student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Sana Jabbar is a PhD Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Omair Hassaan is a Research Assistant in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Research Associate | AI & Computer Vision Expert | Deep Learning & Machine Learning Specialist | Full Stack Developer | Web Scraping and Automation Enthusiast Nauman Fazal is a skilled full-stack developer with expertise in machine learning, computer vision, and web development. With proficiency in languages such as Python, TypeScript, and frameworks like React, Angular,…
Ali Hassan is a Research Assistant in Computer Vision Lab (cvlab ) at LUMS Syed Babar Ali School of Science and Engineering. Research Interest My main areas of interest are Computer Vision, Machine Learning and Robotics. I am primarily interested in Human Motion Analysis. I am currently working on “Human Body Pose Estimation from Monocular…
Muhammad Ahmed Bhimra, Usman Nazir, Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network…
Deep Learning is a hierarchical learning methodology based on artificial neural networks which are algorithms inspired by the structure and function of the brain. It has applications in wide-range of industries these days such as face-recognisers working at massive scales, robotics, speech translation, text analysis, improving customer experience, autonomous vehicles etc. In this course we…