You cannot wait for the future to happen? You will use computer vision to identify food products from a printed receipt.
Computer Vision Group - Research Areas
You will use computer vision and machine learning to predict for a product image i. Therefore, you can use existing computer vision tools, e. Keras, PyTorch, Tensorflow. You will use computer vision to identify food products and build a Unity application for Hololens or Magic Leap one in which users will become able to see the health impact of ingredient data. Roberto Cipolla thanks!
- AI Scientist - Deep Learning in Computer Vision / Medical Imaging - Covera Health.
- Semester and Master Projects at BIWI.
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When I began reading computer vision literature, deep learning had just become popular in image classification, following inspiring breakthroughs on the ImageNet dataset. But it was yet to become ubiquitous in the field and be used in richer computer vision tasks such as scene understanding. What excited me about deep learning was that it could learn representations from data that are too complicated to hand-design.
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I initially focused on building end-to-end deep learning models for computer vision tasks which I thought were most interesting for robotics, such as scene understanding SegNet and localisation PoseNet. In my thesis, I argue that we can do better than naive end-to-end convolutional networks. Especially with limited data and compute, we can form more powerful computer vision models by leveraging our knowledge of the world.
Specifically, I focus on two ideas around geometry and uncertainty. Over the last three years, I have had the pleasure of working with some incredibly talented researchers, studying a number of core computer vision problems from localisation to segmentation to stereo vision.
Master Thesis project in Robotics & Computer Vision (f/m/d)
This thesis consists of six chapters. Each of the main chapters introduces an end-to-end deep learning model and discusses how to apply the ideas of geometry and uncertainty. Chatper 1 - Introduction.
Motivates this work within the wider field of computer vision. Chapter 2 - Scene Understanding.
Introduces SegNet, modelling aleatoric and epistemic uncertainty and a method for learning multi-task scene understanding models for geometry and semantics. Chapter 3 - Localisation. Describes PoseNet for efficient localisation, with improvements using geometric reprojection error and estimating relocalisation uncertainty.
Chapter 4 - Stereo Vision. Designs an end-to-end model for stereo vision, using geometry and shows how to leverage uncertainty and self-supervised learning to improve performance. Chapter 5 - Video Scene Understanding. Illustrates a video scene understanding model for learning semantics, motion and geometry. This thesis explains how to extract a robust state of the world — semantics, motion and geometry — from video.
And the source code for the latex document is here.
The abstract Deep learning and convolutional neural networks have become the dominant tool for computer vision. Geometry is all about leveraging structure of the world. This is useful for improving architectures and learning with self-supervision.
Age Detection by NN trained LBP features
This is useful for robust learning, safety-critical systems and active learning. Bayesian deep learning for modelling uncertainty in semantic segmentation. The science This thesis consists of six chapters. Chapter 6 - Conclusions.
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