Projects Proposed Projects
Please see propose projects for Spring and Winter upcoming semesters. This is a 4 academic point course. For further details please contact the laboratory engineer Yaron Honen (04-8295535, room 441).
Deep Semantic Shape Reconstruction
Deep Learning architectures have revolutionized the field of Computer Vision, completely altering the way we view 2D and 3D signals. In this project, we tackle the problem of precise and realistic reconstruction of 3D signals by semantic segmentation. The student will take part in creating novel architectures to push current state-of-the-art benchmarks one step further. More details: @https://arxiv.org/abs/2001.09650
Deep Precise Shape Reconstruction
Deep Learning architectures have revolutionized the field of Computer Vision, completely altering the way we view 2D and 3D signals. In this project, we tackle the problem of precise and realistic reconstruction of 3D signals via supervised and unsupervised learning methods. The student will take part in creating novel architectures to push current state-of-the-art benchmarks one step further. More details: @https://arxiv.org/abs/2001.09650
Virtual Try On
Deep Learning architectures have revolutionized the field of Computer Vision, completely altering the way we view 2D and 3D signals. In this project, we tackle the problem of precise and realistic reconstruction of 3D signals by semantic segmentation. The student will take part in creating novel architectures to push current state-of-the-art benchmarks one step further. More details: @https://arxiv.org/abs/2001.09650
ZoomEmotion
In this project, we would like to develop the ZoomEmotion - an algorithm that detects the emotional state of the child in real time and signals the teacher when the child is experiencing a negative emotion.
A previous very successful students’ project performed recently in the GIP lab in cooperation with the Educational neuroimaging center, trained a deep learning network to recognize facial expressions in children. The results of the network gave very high sensitivity and specificity rates. In this project we would like to expand the previous project to recognize the faces of the children and their emotional state in real time. We would also like to quantify the time that negative emotions are present and to provide a feedback
- Excellence students
- Programming experience in C++ and/or Python
- Advantage courses in Machine/Deep Learning and/or Image Processing
Neuro feedback based Virtual reality for practicing mindfulness principles
In this project we would like to expend the previous project by adding more channels of physiological input from the user together with more sensory feedback to the user. In addition new VR scenes will be designed.
The project’s main objectives are -
1. Getting to know the current system.
2. Integrate new physiological inputs of Heart rate and breathing (which are additional outputs of the muse 2) to the system software.
3. integrate a tactile sensory feedback to the user.
4. Create new VR scenes.
- Excellence students
- Programming experience in C++ and/or Python
- Advantage courses in Machine/Deep Learning and/or Image Processing
A video-based detector for attention-slips in children
This project is a collaboration between Geometric Image Processing Lab (GIP) and Educational Neuroimaging Center (ENC).
An innovative intervention program of joint storytelling was used to promote cognitive abilities, sustained attention and language abilities in 4-6 year old children. Videos of these children during the intervention sessions were recorded using a web camera. We aim to quantify the children’s movement levels along the intervention sessions in order to explore an early bio-marker for ADHD and its relation to school readiness.
The project’s main objectives are -
1. Detect children from the video and follow their movements.
2. Operate a motion parameter to receive a movement score for each child over each intervention session.
3. Compare the motion parameter with different behavioral tests scores preformed with this cohort of children.
• Courses: one of the following - Computer Vision, Image Processing, Excellence students
Analysis of teachers movements in classroom
Using videos of apprentice teachers in classroom, we will inspect their body movements and face orientation. Quantitative and qualitative results will help them perform better in class.
Computer Vision Based Bike Fit
In this project we will implement a system that can find the right parameters for adapting the bicycle to the rider. We will use 3D or 2D camera to capture the scene and than analyze the angles and distances.
Ron Kimmel
- Projects:
- Proposed Projects
- Last Projects
