Welcome to the Technion Computer Graphics & Multimedia (CGM) Lab ! The lab is led by Prof. Ayellet Tal and Prof. Lihi Zelnik-Manor and is in the Endrew and Erna Viterbi Faculty of Electrical & Computer Engineering at the Technion, Meyer Building.
The Computer Graphics & Multimedia Lab (CGM) is a cutting-edge facility dedicated to advancing the field of computer vision, with a focus on both 2D and 3D visual data processing and analysis. Leveraging state-of-the-art machine learning tools, the lab explores innovative solutions and applications in computer graphics, computer vision, and haptic devices.
Key Areas of Research
2D Computer Vision
- Image Processing: Techniques for enhancing, segmenting, and analyzing 2D images.
- Object Recognition: Machine learning algorithms to identify and classify objects within images.
- Pattern Recognition: Identifying patterns and features in image data for various applications.
3D Computer Vision:
- 3D Reconstruction: Creating 3D models from 2D images or video sequences.
- Depth Sensing: Utilizing depth sensors and stereo vision for accurate depth measurement.
- Motion Capture: Tracking and analyzing the movement of objects and humans in 3D space.
- Point Cloud Processing: Analyzing and processing 3D point clouds for applications in mapping, modeling, and recognition.
- Object Detection: Developing and refining algorithms for detecting and identifying objects within 3D environments.
Haptic Devices:
- Haptic Feedback: Developing systems that provide tactile feedback to users, enhancing interaction with virtual environments.
- Virtual Reality (VR) and Augmented Reality (AR): Integrating haptic feedback into VR and AR applications to improve user experience.
- Robotic Touch: Creating interfaces that allow robots to perceive and manipulate objects with precision through haptic feedback.
Machine Learning Tools and Techniques
- Deep Learning: Using convolutional neural networks (CNNs) and other deep learning models for image recognition, segmentation, and classification.
- Computer Vision Libraries: Leveraging tools like OpenCV, TensorFlow, and PyTorch for developing and implementing computer vision algorithms.
- Data Augmentation: Techniques to expand and diversify training datasets, improving the robustness and accuracy of machine learning models.
Applications and Projects
- Medical Imaging: Enhancing diagnostic accuracy through advanced image processing and machine learning techniques.
- Autonomous Vehicles: Developing vision systems for navigation, object detection, and environment mapping.
- Entertainment and Gaming: Creating immersive experiences through realistic 3D graphics and haptic feedback.
- Industrial Automation: Implementing vision-based systems for quality control, robotics, and automation.
Facilities and Equipment
- High-Performance Computing: Access to powerful computational resources for training and deploying complex machine learning models.
- Haptic Devices and Simulators: A range of haptic interfaces for research and development in tactile feedback and virtual interaction.
- 3D Scanners and Cameras: State-of-the-art equipment for capturing and processing high-quality 3D data.
Collaborations and Partnerships
The CGM Lab collaborates with academic institutions, industry partners, and research organizations to push the boundaries of computer graphics and multimedia technology. These collaborations facilitate the exchange of knowledge, resources, and expertise, driving innovation and real-world application of research outcomes.
