PIXANO is an open-source solution for large-scale web annotation of pictures and videos for computer vision applications. PIXANO helps humans label images using semi-automated annotation.
Computer vision is one of the best-known applications of AI, enabling computers to automatically recognize objects in real and computer-generated images and in videos. Most perception methods currently in use require prior annotation of a large corpus of images used as examples during the learning phase.
PIXANO (Pixel Annotation) is an open-source solution facilitating large-scale web annotation of images and videos. It consists of a set of software tools and computer vision and machine learning algorithms that help humans optimize the process of creating labels and placing them on images.
PIXANO also includes powerful data analysis tools, enabling it to quickly search through a database of annotated images and select the data most relevant for any given task.
PIXANO has been used in projects with our R&D partners and in multi-partner projects like the EU H2020 CloudLSVA project. The solution has proven to be extremely effective in tests with major industrial companies, including in the automotive industry.
With its modular architecture and ability to integrate new, customizable smart components, PIXANO can help create a whole new range of solutions to meet AI developers’ needs.
UCP-Net: Unstructured Contour Points for Instance Segmentation, C. Dupont, Y. Ouakrim, Q. C. Pham, IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021.
Classifying All Interacting Pairs in a Single Shot, S. Chafik, A. Orcesi, R. Audigier and B. Luvison. IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.
PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation, A. Benzine, F. Chabot, B. Luvison, Q. C. Pham, C. Achard, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image, F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking, L. Fagot-Bouquet, R. Audigier, R. Dhome, F. Lerasle. European Conference on Computer Vision (ECCV), 2016.
Fast and accurate video annotation using dense motion hypotheses, L. Fagot-Bouquet, J. Rabarisoa, Q.C. Pham. IEEE International Conference on Image Processing (ICIP), 2014.
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