___

• Eskandari, A.R. and Kouchaki, Z., (2012). Regular Shapes
• Detection in Satellite Images, Malaysian Journal of Computer
• Science, vol:25, no:1, pp:56-66.
• Lai, K. and Fox, D., (2010). Object Recognition in 3D Point
• Clouds Using Web Data and Domain Adaptation, The International
• Journal of Robotics Research, vol:29, no:8, pp:1019-1037.
• Schnabel, R., Wahl, R., Klein, R., (2007). Efficient RANSAC for
• Point-Cloud Shape Detection, Computer Graphics Forum,
• vol:26, no: 2, pp:214-226.
• Hao, W., Wang, Y., Ning, X., Zhao, M., Zhang, J., Shi, Z.,
• and Zhang, X., (2013). Automatic Building Extraction from
• Terrestrial Laser Scanning Data, Advances in Electrical and
• Computer Engineering, vol:13, no:3, pp:11-16.
• Zhang, Z., (2012). Microsoft Kinect Sensor and Its Effect,
• MultiMedia, IEEE, vol:19, no:2, pp:4-12.
• Zhang, M., Zhang, Z., Chang, Y., Esche, S. K., Chassapis, C.,
• (2015). Kinect-based Universal Range Sensor and its Application
• in Educational Laboratories, International Journal of Online
• Engineering (IJOE), vol:11, no:2, pp:26-35.
• Khoshelham, K. and Elberink, S.O., (2012). Accuracy and
• Resolution of Kinect Depth Data for Indoor Mapping Applications,
• Sensors, Vol:12, No:2, pp:1437-1454.
• Ozbay, E. and Cinar, A., (2013). 3D Reconstruction Technique
• With Kinect and Point Cloud Computing, in Proc. 3rd World
• Conference on Information Technology, vol:3, pp:1748-1754.
• Ozbay, E., Cinar, A., (2013). A Novel Approach to Smoothing on
• d Structured Adaptive Mesh of The Kinect-Based Models, in Proc.
• th International Conference on Advanced Information
• Technologies and Applications, pp:13-22.
• Atmosukarto, I. and Shapiro, L.G., (2008). A Learning Approach
• to 3D Object Representation for Classification, in Proc.
• Structural, Syntactic, and Statistical Pattern Recognition,
• Pp:267-276.
• Wei, X., Phung, S.L., and Bouzerdoum, A., (2014). Object
• segmentation and classification using 3-D range camera, Journal
• of Visual Communication and Image Representation, Vol:25, No:1,
• pp:74-85.
• Berger, K., Ruhl, K., Schroeder, Y., Bruemmer, C., Scholz, A.,
• and Magnor, M.A., (2011). Markerless Motion Capture using
• multiple Color-Depth Sensors, In VMV pp:317-324.
• Zalevsky, Z., Shpunt, A., Malzels, A., and Garcia, J., (2013).
• Method and system for object reconstruction” Patent No
• ,400,494, Washington Patent and Trademark Office.
• Smisek, J., Jancosek, M., Pajdla, T., (2013). 3D with Kinect,
• In Consumer Depth Cameras for Computer Vision Springer London,
• Pp:3-25.
• Rusu, R.B. and Cousins, S., (2011). 3D is here: Point Cloud
• Library (PCL), in Proc. 2011 IEEE International Conference on
• Robotics and Automation (ICRA), pp. 1-4.
• Khoshelham, K., (2011). Accuracy Analysis Of Kinect Depth Data.
• ISPRS workshop laser scanning, vol:38, no:5, in Proc.
• ISPRS Workshop Laser Scanning.
• Tombari, F. and Stefano, L.D., (2011). 3d Data Segmentation by
• Local Classification and Markov Random Fields, in Proc.
• International Conference on 3D Imaging, Modeling, Processing,
• Visualization and Transmission, pp:212-219.
• Weinberger, K.Q., Blitzer, J., and Saul, L.K., (2005). Distance
• Metric Learning for Large Margin Nearest Neighbor
• Classification. The Journal of Machine Learning Research,
• vol:10, pp:207-244.
• Altman, N.S., (1992). An Introduction to Kernel tnd Nearest-
• Neighbor Nonparametric Regression, The American Statistician,
• vol:46, no:3, pp:175-185.
• Cheng, Y., (1995). Mean Shift, Mode Seeking, and Clustering,
• Pattern Analysis and Machine Intelligence, IEEE Transactions,
• Vol:17, no:8, pp:790-799.
• Jaskowiak, P.A. and Campello, R.J.G.B., (2011). Comparing
• Correlation Coefficients as Dissimilarity Measures for Cancer
• Classification in Gene Expression Data, in Proc. Brazilian
• Symposium on Bioinformatics, pp. 1-8.
• Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C.,
• Benitez, S., and Breitkopf, U., (2012). The ISPRS Benchmark on
• Urban Object Classification and 3d Building Reconstruction,
• ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, Vol:1, No:3,
• pp:293-298.
• Klasing, K., Wollherr, D., and Buss, M., (2008). A Clustering
• Method for Efficient Segmentation of 3d Laser Data, in Proc.
• IEEE International Conference on Robotics and Automation (ICRA),
• , pp:4043-4048.
• Kim, T.K., Kee, S.C., and Kim, S.R., (2001). Real-time
• Normalization and Feature Extraction of 3D Face Data Using
• Curvature Characteristics, in Proc. 10th IEEE International
• Workshop on Robot and Human Interactive Communication,
• Pp:74-79.
• Hwang, J.P., Baek, J., Choi, B., and Kim, E., (2015). A Novel
• Part-Based Approach to Mean-Shift Algorithm for Visual Tracking,
• International Journal of Control, Automation and Systems,
• Vol:13, No:2, pp:443-453.
• Talu, M.F., Türkoğlu, İ., and Cebeci, M., (2010). Real-time
• Kernel Based Object Tracking Using Mean Shift, in Proc. 18th
• IEEE Signal Processing and Communications Applications (SIU),
• Pp:328-331.
• Yao, W., Hinz, S., and Stilla, U., (2009). Object Extraction
• Based on 3D-segmentation of Lidar Data by Combining Mean Shift
• with Normalized Cuts: Two Examples from Urban Areas, in Proc.
• IEEE Urban Remote Sensing Event, pp:1-6.