Extending Dynamic Time Warping to Directed Graphs and DAGs for Improved Pattern Recognition and Alignment
Organisatsiooni nimi
ITS Lab
Kokkuvõte
Extending Dynamic Time Warping to Directed Graphs and DAGs for Improved Pattern Recognition and Alignment
This thesis proposal addresses the need for advanced pattern recognition and alignment techniques in domains such as image recognition, map matching, and multi-sequence alignment. Current methods struggle to capture complex relationships within directed graphs and Directed Acyclic Graphs (DAGs). To bridge this gap, the proposal aims to extend the Dynamic Time Warping (DTW) technique for handling these structures, resulting in more accurate pattern recognition.
- Research Objectives:
1. Develop algorithms to adapt DTW for directed graphs.
2. Extend the proposed model to work with Directed Acyclic Graphs.
3. Design a suitable training process for the extended model.
4. Compare the performance of the proposed model with Hidden Markov Models (HMMs) in various application domains.
5. Compare with image template matching techniques.
- Training and Learning:
1. Utilize supervised learning techniques with relevant datasets for each application domain.
2. Focus on optimizing model-specific parameters for enhanced performance.
- Comparison with HMMs:
Benchmark the proposed model against HMMs in terms of accuracy, robustness, and efficiency.
This thesis proposal addresses the need for advanced pattern recognition and alignment techniques in domains such as image recognition, map matching, and multi-sequence alignment. Current methods struggle to capture complex relationships within directed graphs and Directed Acyclic Graphs (DAGs). To bridge this gap, the proposal aims to extend the Dynamic Time Warping (DTW) technique for handling these structures, resulting in more accurate pattern recognition.
- Research Objectives:
1. Develop algorithms to adapt DTW for directed graphs.
2. Extend the proposed model to work with Directed Acyclic Graphs.
3. Design a suitable training process for the extended model.
4. Compare the performance of the proposed model with Hidden Markov Models (HMMs) in various application domains.
5. Compare with image template matching techniques.
- Training and Learning:
1. Utilize supervised learning techniques with relevant datasets for each application domain.
2. Focus on optimizing model-specific parameters for enhanced performance.
- Comparison with HMMs:
Benchmark the proposed model against HMMs in terms of accuracy, robustness, and efficiency.
Lõputöö kaitsmise aasta
2023-2024
Juhendaja
Kaveh Khoshkhah
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Magister
Märksõnad
Kandideerimise kontakt
Nimi
Kaveh Khoshkhah
Tel
E-mail