Simulation of moiré effects for counterfeit prevention
EPFL and startup company Innoview Sàrl have developed counterfeit prevention features.
The proposed project consists of simulating a setup comprising lenslets and reflective elements and of verifying the quality of the resulting moiré shapes. This reflective moiré setup can be simulated by applying simple ray-tracing techniques and/or by using the Blender computer graphics rendering software.
Deliverables: Report and framework for running simulations
– coding skills in Matlab or Java.
– basic knowledge in 3D graphics
Level: BS, MS semester project
Recovery of a watermark hidden within a color image by an Android smartphone
Startup company Innoview Sàrl has developed software to recover by smartphone a watermark hidden into a grayscale image that displays simple graphical elements such as a logo. The current project aims at carrying out a similar recovery, but for a watermark that is hidden into a full color image (e.g. the color photograph of the holder of a document).
Deliverables: Report and running prototype (Matlab and/or Android).
– knowledge of image processing / computer vision
– basic coding skills in Matlab and/or Java Android
Level: BS or MS semester project or possibly master project
Classification of Movies
Description: The goal of this project is to predict the specific tasks for each scene in a film by using neural networks. We use a three channel approach where the training is performed on video, audio, and text channels. The details for the specific tasks will be shared after the application.
– Understand the literature and state of art
– Revise the training set.
– Revise our multi-channel network and make parameter tuning.
– Validate the model and improve the accuracy.
– Correlate the age profiles with the trends in the industry.
Sivaraman, K. S., and Gautam Somappa. “MovieScope: Movie trailer classification using Deep Neural Networks.” University of Virginia (2016).
Simões, Gabriel S., et al. “Movie genre classification with convolutional neural networks.” Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016.
Deliverables: At the end of the semester, the student should provide a framework that gives the predictions of scenes for the given task.
Prerequisites: Experience in deep learning and computer vision, experience in Python, experience in Keras, Theano, or TensorFlow
Type of work: 40% research, 60% development and testing
Supervisor: Sami Arpa ([email protected])
Deep Learning based Movie Recommendation System
Description: In this project, you will work on the recommendation system of Sofy.tv. Sofy.tv uses a recommendation system based on the recipes of the movies. These recipes are found through our multi-channel deep learning system. The goal of this project is to improve the recommendations by finding the best fits for the user taste.
– Understand the literature and our framework
– Revise our taste clustering system
– Revise our matchmaking system between the users and films.
– Test the revised model.
Deliverables: At the end of the semester, the student should provide an enhanced framework for the recommendation.
Prerequisites: Experience in deep learning and computer vision, experience in Python, experience in Keras, Theano, or TensorFlow. Basic experience in web programming.
Type of work: 50% research, 50% development and testing
Supervisor: Sami Arpa ([email protected])
Longitudinal chromatic aberration assessment tool
Description: The goal of this project is to evaluate longitudinal chromatic aberration from a single photo in the presence of lateral chromatic aberration. You will be acquiring a set of photos of printed edges placed at different depths in the scene and the objective is to remove the lateral chromatic aberration across the image to be able to use its full width for assessing longitudinal chromatic aberration, which you will work on in the second part of the project. Your final results should then be compared to prior work results to evaluate the matching.
Review the literature; PSF estimation protocols, lens assessment and chromatic aberration.
Capture a dataset of edge images.
Remove lateral chromatic aberration across the image.
Evaluate longitudinal chromatic aberration from a single image where the lateral chromatic aberration was corrected.
Evaluate the results on different lenses/cameras and compare to prior work.
Deliverables: Report, dataset, implementation codes for lateral chromatic aberration removal and for single-image longitudinal chromatic aberration assessment.
Prerequisites: Comfortable reading codes in MATLAB (and writing), strong mathematical signal processing background, experience with hardware and (professional) image acquisition techniques.
Type of work: 80% research, 20% development and testing
Supervisor: Majed El Helou