Numan Khurshid
Numan is a senior PhD Student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Numan is a senior PhD Student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Waseem Abbas was a Research Assistant in Computer Vision Lab (cvlab) at LUMS Syed Babar Ali School of Science and Engineering. Research Interest Waseem received BSc degree from the Islamia University of Bahawalpur, Pakistan. He received the M.S. degree in Electrical Engineering from the Lahore University of Management Sciences with a focus on machine learning…
Content Based Image Retrieval Using Hand Crafted Features Asim Waheed, Khawaja Umair Ul Hassan The project involved solving the Cross-View image matching problem between Satellite view images and Street view images. Many hand-crafted features were calculated, such as Histogram, HOG, Bag of Visual Words and VLAD using SIFT and SURF descriptors. The compiled features would…
Research Associate | Full Stack Developer | Web Scraping and Automation Expert | Certified DL & ML Specialist Muhammad Muqsit Islam is a full-stack developer proficient in Web Development, Computer Vision, and Deep Learning. They are skilled in Django, Django Rest Framework, React, Flask, TensorFlow, Docker and other technologies, effectively bridging the gap between frontend…
Spatio-Temporal Analysis of Landuse-Landcover Change Using Satellite Imagery Thursday 28 Feb, 2019 at 10:00 am in Smart Room 9-105 SBASSE. Abstract We propose an approach to recognize large scale, rapid spatio-temporal analysis of satellite remote sensing data. This technique can be used to measure longitudinal changes and yearly changes. Most of the existing methods either…
Muhammad Awais Ather is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
M. U. Qadeer, S. Saeed, M. Taj and A. Muhammad Abstract: Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest. In this…