OnFoods is a foundation that brings together, coordinates and amplifies the work of 26 public and private organisations, leaders in scientific research and sustainable innovation of food systems.
In this project we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem.
We create a new dataset composed of 120,000 images of 50 diverse food categories. The images are accurately annotated with pixel-wise annotations. The dataset is augmented with the same 5,000 images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur.
In this project we present a method for logo recognition based on deep learning. Our recognition …
In this project we use deep learning with the aim of automatically finding products in grocery store shelves.
We propose a network architecture to perform efficient scene understanding. This work presents three main …
Automatic food recognition is an important task to support the users in their daily dietary monitoring and to keep tracks of their food consumption. We have designed datasets and algorithms for automatic dietary monitoring of canteen customers based on robust computer vision techniques.
We address the task of classifying car images at multiple levels of detail, ranging from …
A review of existing methods for local visual detectors and descriptors.
Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling …
We have developed the iVAT: an interactive Video Annotation Tool. It supports manual, semi-automatic and automatic annotations through the interaction of the user with various detection algorithms.