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Annealed Hopfield Neural Network for Recognizing Partially Occluded Objects

부분적으로 가려진 물체 인식을 위한 어닐드 홉필드 네트워크

  • Yoon, Suk-Hun (Department of Industrial and Information Systems Engineering, Soongsil University)
  • Received : 2021.04.29
  • Accepted : 2021.05.16
  • Published : 2021.05.31
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Abstract

The need for recognition of partially occluded objects is increasing in the area of computer vision applications. Occlusion causes significant problems in identifying and locating an object. In this paper, an annealed Hopfield network (AHN) is proposed for detecting threat objects in passengers' check-in baggage. AHN is a deterministic approximation that is based on the hybrid Hopfield network (HHN) and annealing theory. AHN uses boundary features composed of boundary points and corner points which are extracted from input images of threat objects. The critical temperature also is examined to reduce the run time of AHN. Extensive computational experiments have been conducted to compare the performance of the AHNwith that of the HHN.

컴퓨터 비전 적용 분야에서 부분적으로 가려진 물체 인식의 필요성은 증가하고 있다. 물체를 확인하고 위치를 지정하는 데에 물체가 가려진 것은 심각한 문제를 야기한다. 이 논문은 여행자 소지 수하물에서 위험 물건을 발견하기 위하여 어닐드 홉필드 네트워크를 제안한다. 어닐드홉필드 네트워크는 하이브리드 홉필드 네트워크와 어닐링 이론에 기초한 확정적 근사방법이다. 하이브리드 홉필드 네트워크는 위험 물체의 이미지에서 발췌한 경계 점들과 코너 점들을 이용한다. 또한 어닐드 홉필드 네트워크의 런타임을 줄이기 위해 임계 온도를 조사하였다. 어닐드 홉필드 네트워크와 하이브리드 홉필드 네트워크의 성능을 비교하기 위하여 광범위한 컴퓨터 실험이 실행되었다.

Keywords

References

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