Attentive Monitoring of Multiple Video Streams
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.
Benchmarking Algorithms for Food Localization and Semantic Segmentation
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.
Deep Learning for Logo Recognition
In this project we present a method for logo recognition based on deep learning. Our recognition …
Deep Learning for Product Detection
In this project we use deep learning with the aim of automatically finding products in grocery store shelves.
Fast Scene Understanding
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.
Local Detectors and Compact Descriptors for Visual Search
A review of existing methods for local visual detectors and descriptors.
Real Time Semantic Segmentation
Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling …
Semi-automatic and Automatic Video Annotation
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.