Tag: Murtaza Taj
3xPapers accepted at ICONIP 2019
Three papers have been accepted at “International Conference on Neural Information Processing – 19 (ICONIP-19)”. This conference is ranked A by the CORE rating measure and is going to be held from 12th to 15th December 2019 in Sydney, Australia. “Cross-view Image Retrieval – Ground to Aerial Image Retrieval through Deep Learning” “Patch-based Generative Adversarial Network […]
ICIAP 2019 – Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval
Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019 Abstract Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory […]
Statistically Correlated Multi-task Learning for Autonomous Driving
Waseem Abbas, M. Fakhir Khan, Murtaza Taj, and Arif Mahmood Abstract Autonomous driving research is an emerging domain in computer vision and machine learning areas. Most existing methods perform Single Task Learning (STL) from one or more images while Multi-Task Learning (MTL) is more efficient due to the leverage of shared information between different tasks. […]
Digital Image Processing
This is a graduate-level introductory course on the fundamentals of digital image processing. The course will emphasize the general principles of image processing. It will extend the signals and systems knowledge of the students to two-dimensional signals. This is a very important course for any student who wants to do a senior project related to […]
Computer Graphics
Computer Graphics is one of the most exciting ‘application’ fields of Computer Science. This course is intended to introduce the basics of Computer Graphics, laying the foundation for more advanced graduate classes or industry work. The basic graphics pipeline is covered in this course, along with an introduction to OpenGL. This course will be conducted […]
Computer Vision
This course gives a broad overview of the field of computer vision, laying the foundations for advanced graduate level classes and research work in vision. This course will be conducted with an application perspective. Therefore students will be expected to implement several techniques learnt in the lectures. A good calculus, linear algebra and programming background […]
Deep Learning
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 […]
PacificGraphics 2017 – Outdoor scene segmentation and reconstruction using LiDAR data
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj The 25th Pacific Conference on Computer Graphics and Applications (Pacific Graphics 2017), Taipei, Taiwan, Oct 16 – 19, 2017 Abstract Recent advancements in 3D scanning technologies have paved way for generation of highly accurate 3D scenes in the form of point cloud data. For the segmentation and […]
CGI 2016 – Coarse-to-fine model fitting on point cloud
Reema Bajwa, Syed Rizwan Gilani, Murtaza Taj Short Paper Proceedings of the 33rd Computer Graphics International, Heraklion, Greece, June 28 – July 1, 2016 Abstract We present a coarse-to-fine model fitting approach that automatically generates a detailed CAD like model from a point cloud. We first developed a library of detailed parametric models for each […]
Eurographics 2015 – Efficient RANSAC for n-gonal Primitive Fitting
Ahsan Abdullah, Reema Bajwa, Syed Rizwan Gilani, Zuha Agha, Saeed Boor Boor, Murtaza Taj, Sohaib Ahmed Khan The 36th Annual Conference of the European Association for Computer Graphics, 2015 Kongresshaus in Zürich, Switzerland, 4th – 8th May, 2015 Abstract We present a modeling approach to automatically fit 3D primitives to point clouds in order to […]
