Login | Register

Towards a data-driven object recognition framework using temporal depth-data


Towards a data-driven object recognition framework using temporal depth-data

Birkas, David (2015) Towards a data-driven object recognition framework using temporal depth-data. Masters thesis, Concordia University.

Text (application/pdf)
Birkas_MasterCompSc_F2015-1.pdf - Accepted Version


Object recognition using depth-sensors such as the Kinect device has received a lot of attention in recent years. Yet the limitations of such devices such as large noise and missing data makes the problem very challenging. In this work I propose a framework for data-driven object recognition that uses a combination of local and global features as well as time varying depth information.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science
Item Type:Thesis (Masters)
Authors:Birkas, David
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:15 December 2015
Thesis Supervisor(s):Popa, Tiberiu
Keywords:Object detection, Object retrieval, Sketch-based object detection, Sketch-based object retrieval
ID Code:980842
Deposited By: DAVID BIRKAS
Deposited On:16 Jun 2016 14:32
Last Modified:18 Jan 2018 17:52


[1] Trevor, a, Gedikli, S., Rusu, R. B., Christensen, H. I. (2013). Efficient organized point cloud segmentation with connected components. Proceedings of Semantic Perception Mapping and Exploration, 16.
[2] Xu, K. (2012). Sketch2Scene: Sketch-based Co-retrieval and Co-placement of 3D Models.
[3] Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., Alexa, M. (2012). Sketch-based shape retrieval. ACM Transactions on Graphics, 31(4), 110.
[4] Chen, D.-Y., Tian, X.-P., Shen, Y.-T., Ouhyoung, M. (2003). On Visual Similarity Based 3D Model Retrieval. Computer Graphics Forum, 22(3), 223232.
[5] DeCarlo, D., Finkelstein, A., Rusinkiewicz, S., Santella, A. (2003). Suggestive contours for conveying shape. ACM Transactions on Graphics, 22(3), 848.
[6] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679698.
[7] Lindeberg, T. (1996). Edge detection and ridge detection with automatic scale selection. Computer Vision and Pattern Recognition, 1996. Proceedings CVPR 96, 1996 IEEE Computer Society Conference on, 30(2), 465470.
[8] Squire, D. M., Mller, W., Mller, H., Pun, T. (2000). Content-based query of image databases: Inspirations from text retrieval. Pattern Recognition Letters, 21(13-14), 11931198.
[9] Sivic, J., Zisserman, A. (2003). Video Google: a text retrieval approach to object matching
in videos. IEEE International Conference on Computer Vision, 14701477. doi:10.1109/ICCV.2003.1238663
[10] Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129137.
[11] Zhang, Z. (2012). Microsoft kinect sensor and its effect. IEEE Multimedia, 19(2), 410.
[12] Felzenszwalb, P. F., Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167181.
[13] Hulik, R., Beran, V., Spanel, M., Krsek, P., Smrz, P. (2012). Fast and accurate plane segmentation in depth maps for indoor scenes. 2012 IEEE/RSJ International Conference on Intel- ligent Robots and Systems, 16651670.
[14] Holzer, S., Rusu, R. B., Dixon, M., Gedikli, S., Navab, N. (2012). Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images. IEEE International Conference on Intelligent Robots and Systems, 26842689.
[15] John A. Hartigan. 1975. Clustering Algorithms (99th ed.). John Wiley Sons, Inc., New York, NY, USA.
[16] Witten, I., Moffat, A., BELL, T. 1999. Managing giga- bytes: compressing and indexing documents and images. Mor- gan Kaufmann.
[17] Deza, M. M., Deza, E. (2009). Encyclopedia of Distances. Media (Vol. 2006).
[18] Besl, P., McKay, N. (1992). A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Systems, I. (1991). Object Modeling by Reg strat ion of Multiple Range Images*, (April), 27242729.
[20] Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision, 13(2), 119152.
[21] Pomerleau, F., Colas, F., Siegwart, R. (2015). A Review of Point Cloud Registration Algorithms for Mobile Robotics. Foundations and Trends in Robotics, 4(1-104).
[22] Rusinkiewicz, S. (2001). Efficient Variants of the ICP Algorithm a r c Levoy.
[23] Zhang, D., Lu, G. (2002). An Integrated Approach to Shape Based Image Retrieval. Computer, 23(January), 16.
[24] Silberman, Nathan; Hoiem, Derek; Fergus, Rob; Kohli, P. (2012). Indoor Segmentation and Support Inference from RGBD Images. Lecture Notes in Computer Science, 7576(Part 5), 746760.
[25] Schnabel, R., Wahl, R., Klein, R. (2007). Efficient RANSAC for point-cloud shape detec- tion. Computer Graphics Forum, 26(2), 214226.
[26] Hast, A., Nysj, J. (2013). Optimal RANSAC - Towards a Repeatable Algorithm for Find- ing the Optimal Set. Journal of WSCG, 21(1), 2130.
[27] Oehler, B., Stueckler, J., Welle, J., Schulz, D., Behnke, S. (2011). Efficient multi-resolution plane segmentation of 3D point clouds. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7102 LNAI, 145156.
[28] Rusu, R. B., Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). Proceedings - IEEE International Conference on Robotics and Automation, 14.
[29] Zhang, D., Lu, G. (2001). A comparison of shape retrieval using Fourier descriptors and short-time Fourier descriptors. Advances in Multimedia Information Processing PCM 2001, 24, 855860.
[30] Izadi, S., Davison, A., Fitzgibbon, A., Kim, D., Hilliges, O., Molyneaux, D., Freeman, D. (2011). Kinect Fusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Cam- era. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology - UIST 11, 559.
[31] Newcombe, R. a, Molyneaux, D., Kim, D., Davison, A. J., Shotton, J., Hodges, S., Fitzgib- bon, A. (2011). KinectFusion: Real-Time Dense Surface Mapping and Tracking. IEEE Interna- tional Symposium on Mixed and Augmented Reality, 127136.
[32] Li, Y., Dai, A., Guibas, L., Niener, M. (2015). Database-Assisted Object Retrieval for Real-Time 3D Reconstruction. Computer Graphics Forum, 34(2), 435446.
[33] Leng, B., Zeng, J., Yao, M., Xiong, Z. (2015). 3D Object Retrieval With Multitopic Model Combining Relevance Feedback and LDA Model. Image Processing, IEEE Transactions on, 24(1), 94105.
[34] Scovanner, P., Ali, S., Shah, M. (2007). A 3-dimensional sift descriptor and its application to action recognition. Proceedings of the 15th International Conference on Multimedia - MULTI- MEDIA 07, (c), 357.
[35] Davison, A. J. (2003). Real-time simultaneous localisation and mapping with a single cam- era. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, 2, 14031410.
[36] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. (2015). SSD: Single Shot MultiBox Detector. Arxiv.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top