HPC is a foundation uniting 51 interdisciplinary public and private organizations, driving substantial and sustainable innovations across various fields, from basic research to computational and experimental sciences, supporting education, and advocating responsible data management for open science.
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.
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 use deep learning with the aim of automatically finding products in grocery store shelves.
Food-475 database is one of the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases.
In this project we propose a new deep multibranch neural network to solve the tasks …
Food524DB is the largest publicly available food dataset with 524 food classes and 247,636 images by merging food classes from existing datasets in the state of the art. This database can be used for food recognition. The database is composed of 247,636 images belonging to 524 food categories.
In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict …
We propose a strategy for land use classification which exploits Multiple Kernel Learning (MKL) to automatically determine a suitable combination of a set of features without requiring any heuristic knowledge about the classification task.
We present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global, local, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes.
In this work we present an algorithm for the automatic detection of the image orientation that relies on the image content as described by Local Binary Patterns (LBP).
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.
Image quality assessment (IQA) is a multidimensional research problem and an active and evolving research …
Visual complexity perception plays an important role in the fields of both psychology and computer vision. We investigate image complexity of different kind of stimuli. We perform different types of psycho-physical experiments and correlate subjective data with objective measures.
We consider the problem of how to automatically create pleasing photo collages: given a set of photos and a canvas area, we want to arrange the photos on the canvas in a pleasant unsupervised manner.