KAUST's Visual Computing Center (VCC) hosted an Open House event on March 28, showcasing its interdisciplinary research in visual computing. Demonstrations included a virtual reality driving simulator by FalconViz, intended for driver education in Saudi Arabia. Researchers also presented a drone trained to autonomously navigate race courses and a neural network for autonomous driving using image-based technology without GPS. Why it matters: The VCC's work highlights KAUST's role in advancing visual computing applications relevant to Saudi Arabia, from driver training to autonomous systems.
KAUST's Visual Computing Center (VCC) is researching computer vision, image processing, and machine learning, with applications in self-driving cars, surveillance, and security. Professor Bernard Ghanem is working on teaching machines to understand visual data semantically, similar to how humans perceive the world. Self-driving cars use visual sensors to interpret traffic signals and detect obstacles, while computer vision also assists governments and corporations with security applications like facial recognition and detecting unattended luggage. Why it matters: Advancements in computer vision at KAUST can contribute to innovations in autonomous vehicles and enhance security measures in the region.
The KAUST Visual Computing (KAUST RC-VC) – Modeling and Reconstruction conference featured speakers from Simon Fraser University, Caltech, Cornell University, and Autodesk. Presentations covered topics like networking topology, shape matching and modeling, data-driven interpolation of optical properties, and computer graphics. Why it matters: The conference highlights KAUST's role in fostering international collaboration and advancing research in visual computing and related fields within Saudi Arabia.
KAUST's Visual Computing Center had two papers recognized at IEEE VIS 2023. One paper, from Prof. Markus Hadwiger's group, introduced a new method for detecting and visualizing vortex structures in 2D fluid flows, which was recognized as one of the best papers. The second paper, from Prof. Ivan Viola's team, presented Dr. KID, a visualization framework for physicalizing biological structures into 3D-printed models, receiving an honorable mention. Why it matters: These awards highlight KAUST's contributions to cutting-edge visualization techniques with potential applications in diverse scientific and engineering fields.
A professor from Nanyang Technological University (NTU), Singapore gave a talk at MBZUAI about "Just-Noticeable Difference (JND)" models in visual intelligence. The talk covered visual JND models, research and applications, and future opportunities for JND modeling. JND can help tackle big data challenges with limited resources by focusing on user-centric and green systems. Why it matters: Exploring JND could lead to advancements in AI applications related to visual signal processing, image synthesis, and generative AI in the region.
MBZUAI Professor Fahad Khan is working on a unified theory of machine visual intelligence. His goal is to enable AI systems to better understand and function in complex, chaotic visual environments. The aim is to improve real-world applications like smart cities, personalized healthcare, and autonomous vehicles. Why it matters: This research could significantly advance AI's ability to perceive and interact with the real world, especially in challenging environments common in the developing world.
Laurent Najman presented the Power Watershed (PW) optimization framework for image and data processing. The PW framework enhances graph-based data processing algorithms like random walker and ratio-cut clustering, leading to faster solutions. It can be adapted for graph-based cost minimization methods and integrated with deep learning networks. Why it matters: This framework could enable more efficient and scalable image and data processing algorithms relevant to computer vision and related fields in the Middle East.
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.