Sparse Lp-Norm Based ICP for 3D Registration

Article Preview

Abstract:

In order to uncover truths to serve justice, case-related data collected from a digital investigation requires substantial resources to analyze, especially in time-critical situations. At present, however, digital forensics has not evolved to meet this ever-increasing demand. Digital forensic triage is a promising solution, as it is designed to maximize the use of resources according to a system of priorities, and hence the efficiency and effectiveness of forensic examinations can be increased. Nevertheless, the lack of concrete methods limits efforts to implement triage. This paper presents a practical approach that is designed to build a prioritizing solution. In this work a newprocess model is derived based on the presented approach, and it is particularly suited to scenarios where forensic examiners do not have enough time and resources to conduct a full examination and analysis. An example is described to demonstrate how this approach can be used to meet the requirements of network forensic investigations.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

433-436

Citation:

Online since:

March 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Besl P, Mckay H: A method for registration of 3D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence (1992), p.239–256.

DOI: 10.1109/34.121791

Google Scholar

[2] Rusinkiewicz S, Levoy M: Efficient variants of the ICP algorithm. 3-D Digital Imaging and Modeling (2001), p.145–152.

DOI: 10.1109/im.2001.924423

Google Scholar

[3] Zhang Z: Iterative point matching for registration of free-form curves and surfaces. Int. J. Computer Vision (1994), p.119–152. (in Chinese).

DOI: 10.1007/bf01427149

Google Scholar

[4] Masuda T, Yokoya N: A robust method for registration and segmentation of multiple range images. Computer Vision and Image Understanding (1995), p.295–307.

DOI: 10.1006/cviu.1995.1024

Google Scholar

[5] Candes E J, Romberg J, TAO T: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory (2006), p.489–509.

DOI: 10.1109/tit.2005.862083

Google Scholar

[6] Marjanovic G, Solo V: On lq optimization and matrix completion. IEEE Trans. Signal Process (2012), p.5714–5724.

DOI: 10.1109/tsp.2012.2212015

Google Scholar

[7] Sofien B, Andrea T. Sparse Iterative Closest Point. Symposium on Geometry Processing (2013).

Google Scholar

[8] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning (2011), p.1–122.

DOI: 10.1561/2200000016

Google Scholar

[9] Parikh N, Boyd S. Proximal Algorithms. Foundations and Trends in Optimization (2013), pp.6-11.

Google Scholar

[10] Wang X, Zhang M M, Yu X, Zhang M C. Point cloud registration based on improved iterative closest point method [J]. Opt. Precision Eng (2012). (in Chinese).

Google Scholar