3차원 점군 데이터를 이용한 수정 구형 특징 표현기 개발 및 도시 구조물 분류를 위한 컨볼루션 신경망의 응용

서지정보

ㆍ발행기관 : 한국기계기술학회 ㆍ수록지정보 : 한국기계기술학회지 / 20권 / 1호
ㆍ저자명 : 이언호, 이세진

목차

1. 서 론
1.1 연구 배경
1.2 연구 동향
2. 수정 구형 특징 표현기
2.1 문제 정의
2.2 격자 점유 확률 갱신
3. 컨볼루션 신경망
4. 실 험
4.1 실험 데이터
4.2 모델 파라미터
4.3 실험 결과
5. 결 론
후 기
References

영어 초록

This paper presents the novel observation model, called Modified Spherical Signature Descriptor(MSSD), capable of representing 2D image generated from 3D point cloud data. The Modified Spherical Signature Descriptor has a uniform mesh grid to accumulate the occupancy evidence caused by neighbor point cloud data. According to a kind of area such as wall, road, tree, car, and so on, the evidence pattern of 2D image looks so different each other. For the parameter learning of Convolutional Neural Network(CNN) layers, these 2D images were applied as the input layer. The Convolutional Neural Network, one of the deep learning methods and familiar with the image analysis, was utilized for the urban structure classification. The case study on CNN practice was introduced in detail in this paper. The simulation results shows that the classification accuracy of CNN with 2D images of the proposed MSSD was improved more than the traditional methods' one.

참고 자료

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