The application of Artificial Intelligence (AI) techniques to ATR are also very welcomed, as it recently proved to represent an interesting and useful alternative processing strategy. The efforts in this field should highlight the capabilities and limitations of AI for effective application to ATR problems.
Latest trends in AI and machine learning converge on the theory of dynamic systems constrained on (sub-)manifolds. Both geometry and topology are intricated there on the basis of variational methods. Refer to :
- [Big picture] A.B. Hamza, " Geometric and Topological Variational Methods for Imaging and Computer Vision ", PhD Dissertation, 2003 - https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4317/etd.pdf?sequence=1
- [Latest trends] F. Barbaresco, " Lie Group Machine Learning and Gibbs Density on Poincaré Unit Disk from Souriau Lie Groups Thermodynamics and SU(1,1) Coadjoint Orbits ", 2019 -
Chapter Lie Group Machine Learning and Gibbs Density on Poincaré Uni...
Shape recognition greatly benefits from these advances in imagery.
Indeed, computer vision applications (object tracking) are more relevant to your query. Although Radar Technology is already based on machine learning., however., modern computation speeds enhanced the importance of AI to recognition via Radar and Drone technology. Computer vision applications, such as object tracking (as related to your query), clustering, classification and face recognition attracted dramatic popularity, mostly due to its representation via manifolds valued data which locally Euclidean and easy to process for the so-called computer vision applications, see for example : https://www.researchgate.net/project/Statistical-Learning-on-manifolds-with-its-applications-in-computer-vision
- Badges
- Science topic
- Similar topics
- Computer Science and Engineering
- Artificial Intelligence