SMARTER SENSORS, SAFER VEHICLES
Since 2007, advances in Artificial Intelligence are rapidly changing the world. Namely the concept of Deep Learning, where a machine learns from a large amount of examples, has been applied to various fields and have produced results comparable to, and in some cases superior, to human experts. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision. Recent advances in hardware and the introduction of specialized hardware for neural networks fuelled its expansion. The vehicle industry is part of that revolution; fully autonomous vehicle already exist. In a few years, fleet of taxis, buses, trucks, will be fully autonomous, and consumers will require their car to have more autonomous features and intelligence.
We at Sensor Cortek, want to participate in the AI revolution by creating strong relationships with partners in the autonomous vehicle industry, offer our expertise and solutions in deep learning and commercialize solutions and other products developed by the company (ex: tools, data, systems, frameworks) that are useful to our partners.
Sensor Cortek focuses on improving performance, reliability and capability of sensors used in the autonomous vehicle industry by developing embedded artificial intelligence solutions. We work with camera, radar and lidar sensors. We research, test and design deep neural networks that provide high level information from these sensors. We embed our solutions in leading-edge specialized intelligent processing units. To build robust solutions, we research and perform data capture, data simulation, testing, diagnosis, and analysis, and sensor fusion.
WHAT WE DO
Research, test and design deep neural networks for scene understanding (detection, segmentation, classification, tracking) from sensors (camera, radar, lidar).
Multimodal multi-sensor data fusion.
Sensor signal processing and calibration.
Embedding artificial intelligent solutions into edge components.
Work with leading-edge sensors and processing units to leverage best performance in terms of processing, power consumption, accuracy, etc.
Optimization of embedded solutions.
DNN training based on domain adaptation, transfer learning, and one-shot learning methods.
2D & 3D data annotation, calibration, and registration.
Rich data creation from both real and synthetic sources.
Diagnosis and white-box testing of neural networks.
Data and feature space visualization.
Dr. Robert Laganiere has 25 years experience in computer vision and image/video analysis. He is co-founder of the VIVA research lab and is the authors of several books, articles and patents in object detection and tracking, driver assistance, visual surveillance, embedded vision and deep learning. He is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa.
Robert has been involved in the creation and the growth of a number of startup companies such as Visual Cortek, iWatchLife, Cognivue, Tempo Analytics where served as Chief Scientist. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and M.Sc. and Ph.D. degrees from INRS-Telecommunications, Montreal (1996).
Dr. Pascal Blais has 13 years of experience with companies in the domain of computer vision. As director of R&D, he helped building startups, led research and development activities and used his experience in artificial intelligence, machine learning and computer vision to develop new technologies. He is the co-author of 9 patents. He holds a bachelor in science, a master of computer science and a doctorate in computer science from the University of Ottawa.
AI Software Developer
Dhanvin is a Masters candidate in the Electrical and Computer Engineering faculty at the University of Ottawa. His work is in the field of semantic and instance segmentation using deep learning. He has experience with convolutional neural network design for computer vision tasks such as scene segmentation and object detection.
Robert is an entrepreneur and business owner based in NYC. He founded Tempo SOS in 2015 introducing retail analytics to the fast food industry. Robert obtained his MBA from Columbia Business School (1992).
FARZAN ERLIK NOWURUZI
Erlik is a PhD candidate in EECS at the University of Ottawa. His research focus is on deep learning methods for autonomous vehicles. Erlik has an extensive background in theory and practice of traditional and modern computer vision, machine learning and optimization methods. In 2016, he did an internship at the autonomous driving department for Mercedes-Benz, where he was part of the environment perception team that developed localization and mapping solutions using multiple sensory data.
FRANCOIS DE BELLEFEUILLE
VP Project Management
At the age of 18, François built a 30-person company from scratch. Within a few years, he sold the company, joining the Spiria team in 2014. From day one, François has thrown himself into his projects: he isn’t afraid of change, or of starting fresh. François’ leadership philosophy is about putting people in positions of strength, and by doing so motivate and inspire them to be at their best. Francois has 13 years of experience in software development and project management.
AI Software Engineer
Elnaz Jahani has completed her Ph.D. in the field of object recognition using deep neural networks under domain shift. During her Ph.D., she also participated in a medical image analysis project using deep learning in Spain. Elnaz had actively contributed in a project related to semantic segmentation of aerial image during her internship in Austria. Her research interest includes object recognition, semantic and instance segmentation, domain adapation and knowledge distillation in deep neural networks.
Fahed Hassanat is a Software Engineer by academics and an entrepreneur by passion. With 15 years of experience in the domain, Fahed has brought startups to light as founder and co-founder. Having experienced full stack development, Fahed participated in the creation of very uniques software products used by companies such as Samsung. Fahed holds a Bachelor of Applied Science in Software Engineering from the University of Ottawa and Masters of Applied Science in Electrical and Computer Engineering from the University of Ottawa.
Embedded System Engineer
Prince has a Masters in Deep Learning and Computer Vision Applications from the University of Ottawa's Electrical and Computer Engineering faculty. He has expertise in Machine Learning, Deep learning methodologies for autonomous vehicles as well as system security and surveillance. He gained a lot of experience in working with different embedded systems architecture like APEX-CORE and ARM.
News & Events
April 8 -10, 2019
Mar 22, 2019
SENSOR CORTEK 2019