Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Indoor Space Recognition using Super-pixel and DNN

DNN과 슈퍼픽셀을 이용한 실내 공간 인식

  • Received : 2018.01.24
  • Accepted : 2018.04.09
  • Published : 2018.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an indoor-space recognition using DNN and super-pixel. In order to recognize the indoor space from the image, segmentation process is required for dividing an image Super-pixel is performed algorithm which can be divided into appropriate sizes. In order to recognize each segment, features are extracted using a proposed method. Extracted features are learned using DNN, and each segment is recognized using the DNN model. Experimental results show the performance comparison between the proposed method and existing algorithms.

본 논문은 DNN(Deep Neural Network)와 슈퍼픽셀을 이용한 실내 공간 인식 알고리즘을 제안한다. 영상으로부터 실내 공간 인식을 위해 우선 영상 분할을 위한 세그멘테이션 프로세스가 필요하다. 이를 위해 본 논문에서는 적당한 크기로 나눌 수 있는 슈퍼 픽셀 알고리즘을 이용해 세그멘테이션을 수행한다. 각 세그먼트를 인식하기 위해 세그먼트마다 제안하는 방법을 이용하여 특징을 추출한다. 추출된 특징들을 DNN을 이용하여 학습하고, 학습으로부터 추출된 DNN모델을 이용하여 각 세그먼트를 인식한다. 실험 결과를 통해 제안하는 방법과 기존의 알고리즘과의 성능 비교 분석을 한다.

Keywords

References

  1. Techemergence, Everyday Examples of Artificial Intelligence and Machine Learning, 2017. https://www.techemergence.com/everyday-examples-of-ai/
  2. Structure, 3D Scanner. https://structure.io/
  3. IKEA, IKEA Place. http://www.ikea.com/au/en/apps/IKEAPlace.html
  4. Microsoft, HoloLens. https://www.microsoft.com/en-us/hololens
  5. S. Liu, M. Atia, T. Karamat and A. Noureldin, "A LiDAR-Aided Indoor Navigation System for UGVs", The Journal of Navigation, Vol.68, No.2, pp.253-273, 2015. https://doi.org/10.1017/S037346331400054X
  6. D. Lee, M. Hebert and T. Kanade, "Geometric reasoning for single image structure recovery", Computer Vision and Pattern Recognition, pp.2136-2143, 2009. https://doi.org/10.1109/CVPR.2009.5206872
  7. A. Gupta, M. Hebert, T. Kanade and D. Blei, "Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces", Advances in neural information processing systems, pp.1288-1296, 2010. http://papers.nips.cc/paper/4120-estimating-spatial-layout-of-rooms-using-volumetric-reasoning-about-objects-and-surfaces
  8. K. Kim and H. Choi, "Ensemble of fuzzy decision tree for efficient indoor space recognition", The Korean Society Of Computer And Information, Vol.22, No.4, pp.33-39, 2017. https://doi.org/10.9708/jksci.2017.22.04.033
  9. X. Yang, X. Gao, D. Tao, X. Li and J. Li, "An Efficient MRF Embedded Level Set Method for Image Segmentation", IEEE Transactions on Image Processing, Vol.24, No.1, pp.9-21, 2015. https://doi.org/10.1109/TIP.2014.2372615
  10. C. Carson, S. Belongie, H. Greenspan and J. Malik, "Blobworld: image segmentation using expectation-maximization and its application to image querying", IEEE Transactions on Pattern Analysis and MAchine Intelligence, Vol.24, No.8, pp.1026-1038, 2002. https://doi.org/10.1109/TPAMI.2002.1023800
  11. M. Zheng and Z. Zhou, "A k-nearest neighbor based algorithm for multi-label classification", Granular Computing, IEEE International Conference on, Vol.2, pp.718-721, 2005. https://doi.org/10.1109/GRC.2005.1547385
  12. D. Hoiem, A. Efros and M. Hebert, "Recovering surface layout from an image", International Journal of Computer Vision, Vol.75, No.1, pp.151-172, 2007. https://doi.org/10.1007/s11263-006-0031-y