신경망을 이용한 후판 선단부 경계면 검출

목차

1. Introduction

2. Problem Formulation

3. Determining Energy Function

4. Minimizing the Energy using a Neural Network

5. Application to Boundary Detection

7. Conclusion

본문내용

oduces better boundaries; the boundaries are smooth and correct.
Fig. 8 (a) and (b) upper and lower boundaries taken by plain Kalman filter.
Fig. 9 (a) and (b) upper and lower boundaries of the test images obtained by the proposed method.
7. Conclusion
This paper introduced a boundary tracking algorithm that consists of data association and a tracking filter. We applied the basic concept of the data association technique proposed in the field of multiple target tracking, to detecting object boundaries. After defining the data association as a constrained optimization problem, we introduced a new energy function and a Hopfield network as an efficient method for solving the energy function. The new algorithm shows good performance in finding object boundaries especially in IR images. An open problem is to include the constraints of parallel boundary condition for the upper and lower boundaries. Also another problem is to determine the system parameter included in the energy function automatically.
References
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