Locating robust patterns based on invariant of LTP-based features

Thanh Tuan Nguyen (1, 2, 3) , Thanh Phuong Nguyen (1, 2) , Nadège Thirion-Moreau (1, 2, 4)

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
2 SIIM - Signal et Image
3 hcmc - University of Technology and Education, Faculty of IT, Thu Duc City, Ho Chi Minh City
4 UTLN SeaTech - Université de Toulon - École d’ingénieurs SeaTech

Efficiently representing Dynamic Textures (DTs) based on salient features is one of the considerable challenges in computer vision. Locating these features can be obstructed due to the impact of encoding factors. In this article, a novel concept of Robust Local Ternary Patterns (RLTP) is introduced in consideration of the invariance of Local Ternary Patterns (LTP) subject to the deviation of thresholds. Our locating process is able to simultaneously encapsulate the discrimination of local features, and deal with the noise sensibility caused by a small gray-scale change of local neighbors. RLTP is then adapted to the completed LTP model to form an efficient operator for capturing completely properties of RLTP. Finally, RLTP is taken into account for the DT description, where the robust patterns of spatial-temporal features and optical-flow-based motions are exploited to improve the performance. Experiments have clearly corroborated the efficacy of our proposed approach.

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Thanh Tuan Nguyen, Thanh Phuong Nguyen, Nadège Thirion-Moreau. Locating robust patterns based on invariant of LTP-based features. Pattern Recognition Letters, 2023, 165, pp.9-16. ⟨10.1016/j.patrec.2022.11.008⟩. ⟨hal-03963344⟩