COMPRESSED TRANSMISSION OF DEPTH MAPS IN 3D STREAM SERVICES FOR ROBOTICS & SURVEILLANCE

Ankush Rai; Jagadeesh Kannan R

doi:10.22159/ajpcr.2017.v10s1.19644

Authors

Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19644

Keywords:

Network transmission, Video sequences, Depth determination

Abstract

Building high end processing hardware for depth mapping in mobile robotics is a major drawback. The problem could be addressed by processing the
scene through one end and then streaming it to the other robotic mobile platforms or actuators to perform physical operations; thereby rendering
global depth map for all the arbitrary viewpoints of the robots. In this study, we present the algorithm for compressed transmission of depth maps
over a network and provide a synthetic viewpoint with low geometric distortions.

Downloads

Download data is not yet available.

References

Fehn C, Schuur K, Kauff P, Smolic A. Coding Results for EE4 in MPEG 3DAV. Vol. 9561. Pattaya: ISO/IEC JTC1/SC29/WG11 M; 2003.

Howard P, Kossentini F, Martins B, Forchhammer S, Rucklidge W. The emerging JBIG2 standard. IEEE Trans Circuits Syst Video Technol 2002;8(7):838-48.

Krishnamurthy R, Chai B, Tao H, Sethuraman S. Compression and transmission of depth maps for image-based rendering. In: Proceedings of International Conference on Image Processing. Vol. 3. IEEE; 2001.

p. 828-31.

Liu S, Lai P, Tian D, Gomila C, Chen C. Sparse dyadic mode for depth map compression. In: 17

IEEE International Conference on Image Processing (ICIP). IEEE; 2010. p. 3421-4.th

Morvan Y, de With P, Farin D. Platelet-based coding of depth maps for the transmission of multiview images. In: Proceedings of SPIE, Stereoscopic Displays and Applications. Vol. 6055; 2006. p. 93-100.

Sarkis M, Zia W, Diepold K. Fast depth map compression and meshing with compressed tritree. Computer Vision-ACCV 2009. Berlin: Springer; 2010. p. 44-55.

Shen G, Kim W, Ortega A, Lee J, Wey H. Edge-aware intra prediction for depth-map coding. In: Image Processing (ICIP), 2010 17 th IEEE International Conference on IEEE; 2010. p. 3393-6.

Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 2011;33(5):898-916.

Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A. The PASCAL Visual Object Classes Challenge 2008 (VOC2008). Available from: http://www.pascalnetwork.org/challenges/VOC/voc2008.

Shotton J, Winn J, Rother C, Criminisi A. Textonboost: Joint appearance,shape and context modeling for multi-class object recognition and segmentation. In: ECCV. Heidelberg: Springer; 2006.

Silberman N, Fergus R. Indoor scene segmentation using a structured light sensor. In: IEEE Workshop on 3D Representation and Recognition (3dRR); 2011.

Rai A. Shell implementation of neural net over the UNIX environment for file management: A step towards automated operating system. J Oper Syst Dev Trends 2014;1(2):10-4.

Rai A. Dynamic pagination for efficient memory management over distributed computational architecture for swarm robotics. J Adv Shell Program 2014;1(2):1-4.

Rai A. Attribute based level adaptive thresholding algorithm (ABLATA) for image compression and transmission. J Math Comput Sci 2014;12:211-8.