Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.16MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features
Mehdi Khazaeli, Leili Javadpour, Gerald Knapp
Pages - 1 - 13     |    Revised - 29-02-2016     |    Published - 01-04-2016
Volume - 10   Issue - 1    |    Publication Date - April 2016  Table of Contents
MORE INFORMATION
KEYWORDS
Graph-based Segmentation, Normals, RANSAC, Surface Detection, Occlusion.
ABSTRACT
In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 S. Izadi, D. Kim, O. Hilliges, D. Molyneauz, R. Newcombe, P. Kohli, J. Shotton, S. Hodege, D. Freeman, A. Davison, A. Fitzgibbon. “KinectFusion: Real-Time 3D Reconstruction and Interaction Using a Moving Depth Camera”. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, 2011 pp. 559-568.
2 K. K. Singh and A. Singh. “A Study of Image Segmentation Algorithms for Different Types of Images”. IJCSI International Journal of Computer Science, vol. 7, pp. 414-417, Sep. 2010.
3 J. Pont-Tuset, P. Arbelaez, J. T. Barron, F. Marques, and J. Malik. (2015, Mar.) “Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation”. In arXiv preprint arXiv:1503.00848.
4 C. Liu, J. Yuen and A. Torralba. “Nonparametric scene parsing: Label transfer via dense scene alignment”. Computer Vision and Pattern Recognition, 2009, pp. 1972-1979.
5 A. Zlateski, and H. S. Seung. (2015, May). “Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph”. In arXiv preprint arXiv:1505.00249.
6 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, pp. 1026-1038, Aug. 2002.
7 M. Paulinas, and A. Ušinskas. “A survey of genetic algorithms applications for image enhancement and segmentation”. Information Technology and control, vol. 36, pp. 278-284, Apr. 2015.
8 H. Feng and T. S. Chua, “A Bootstrapping Approach to Annotating Large Image Collection”. 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, 2010, pp. 55-62.
9 C. H. Chen, L. Potdat, and R. Chittineni, “Two Novel ACM (Active Contour Model) Methods for Intravascular Ultrasound Image Segmentation”. AIP Conference Proceedings, 2010, pp. 735-741.
10 J. Shi and J. Malik. “Normalized Cuts and Image Segmentation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 888-905, Aug. 2000.
11 C. T. Zahn. “Graph-Theoretic Methods for Detecting and Describing Gestalt Clusters”. IEEE Transactions on Computing, vol. 20, pp. 68-86, Jan. 1971.
12 K. Lai and D. Fox. “Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation”. International Journal of Robotics Research, vol. 29, pp. 1019-1037, Jul. 2010.
13 A. Richtsfeld, T. Mörwald, J. Prankl, J. Balzer, M. Zillich, and M. Vincze. “Towards Scene Understanding–Object Segmentation Using RGBD-Images”. In Proceedings of the 2012 Computer Vision Winter Workshop (CVWW), 2012.
14 R.A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A.w J. Davison, P. Kohli, J.e Shotton, S. Hodges, and A. Fitzgibbon. “KinectFusion: Real-Time Dense Surface Mapping and Tracking”. In Mixed and augmented reality (ISMAR), 10th IEEE international symposium, 2011, pp. 127-136.
15 K. Lai, L. Bo, X. Ren, and D. Fox. “A Large-Scale Hierarchical Multi-View RGB-D Object Dataset”. International Conference on Robotics and Automation, 2011, pp. 1817-1824.
16 N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. “Indoor Segmentation and Support Inference from RGBD Images”. ECCV Proceedings of the 12th European Conference on Computer Vision, 2012, pp. 746-760.
17 P. F. Felzenszwalb and D. P. Huttenlocher. “Efficient Graph-Based Image Segmentation”. International Journal of Computer Vision, vol. 59, pp. 167-181, Sep. 2004.
18 N. Burrus, “RGBDemo,” http://rgbdemo.org/, 2011 [Aug. 4, 2014].
19 R. C. Bolles, and M. A. Fischler. “A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data”. International Joint Conference of Artificial Intelligence, v1981, pp. 637-643.
20 C. Rother. “A New Approach to Vanishing Point Detection in Architectural Environments”, Image and Vision Computing, vol. 20, pp. 647-655, Aug. 2002.
21 J. Kosecka and W. Zhang. “Video Compass”. In ECCV, Berlin, Springer, 2002, pp. 476-491.
22 R. R. Bouckaert, E. Frank, M. A. Hall, G. Holmes, B.d Pfahringer, P. Reutemann, I. H. Witten. “WEKA-Experiences with a Java Open-Source Project”. The Journal of Machine Learning Research, vol.11, pp. 2533–2541, Mar. 2010.
23 D. Narayan, S. K. Murthy, and G. H. Kumar. “Image Segmentation Based on Graph Theoretical Approach to Improve the Quality of Image Segmentation”. World Academy of Science, Engineering and Technology, vol. 42, pp.35-38, 2008.
Dr. Mehdi Khazaeli
Department of Civil Engineering University of the Pacific Stockton, 95211 - United States of America
mkhazaeli@pacific.edu
Dr. Leili Javadpour
Department of Computer Science University of the Pacific Stockton, 95211 - United States of America
Associate Professor Gerald Knapp
Department of Mechanical and Industrial Engineering Louisiana State University Baton Rouge, 70803 - United States of America