Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"Georgia Tech" +contributor:("Narasimha, Rajesh"). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Georgia Tech

1. Ray, Soumitry J. Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators.

Degree: PhD, Computational Science and Engineering, 2014, Georgia Tech

Struck-by fatalities involving heavy equipment such as trucks and cranes accounted for 24.6% of the fatalities between 1997-2007 in the construction industry. Limited visibility due to blind spots and travel in reverse direction are the primary causes of these fatalities. Blind spots are spaces surrounding an equipment that are invisible to the equipment operator. Thus, a hazard is posed to the ground personnel working in the blind spaces of an equipment operator. This research presents a novel approach to intelligently identify potential hazards posed to workers operating near an equipment by determining the visible and blind space regions of an equipment operator in real-time. A depth camera is used to estimate the head posture of the equipment operator and continuously track the head location and orientation using Random Forests algorithm. The head posture information is then integrated with point cloud data of the construction equipment to determine both the visible and the blindspots region of the equipment operator using Ray-Casting algorithm. Simulation and field experiments were carried out to validate this approach in controlled and uncontrolled environment respectively. Research findings demonstrate the potential of this approach to enhance safety performance by detecting hazardous proximity situations. Advisors/Committee Members: DesRoches, Reginald (advisor), Chau, Duen Horng (Polo) (committee member), Vela, Patricio A. (committee member), Cho, Yong K. (committee member), Narasimha, Rajesh (committee member).

Subjects/Keywords: Head posture estimation; Vehicle blindspots; Proximity; Safety; Construction industry; Construction equipment Safety measures

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ray, S. J. (2014). Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51968

Chicago Manual of Style (16th Edition):

Ray, Soumitry J. “Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 06, 2020. http://hdl.handle.net/1853/51968.

MLA Handbook (7th Edition):

Ray, Soumitry J. “Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators.” 2014. Web. 06 Apr 2020.

Vancouver:

Ray SJ. Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Apr 06]. Available from: http://hdl.handle.net/1853/51968.

Council of Science Editors:

Ray SJ. Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51968

2. Appia, Vikram VijayanBabu. Non-local active contours.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

This thesis deals with image segmentation problems that arise in various computer vision related fields such as medical imaging, satellite imaging, video surveillance, recognition and robotic vision. More specifically, this thesis deals with a special class of image segmentation technique called Snakes or Active Contour Models. In active contour models, image segmentation is posed as an energy minimization problem, where an objective energy function (based on certain image related features) is defined on the segmenting curve (contour). Typically, a gradient descent energy minimization approach is used to drive the initial contour towards a minimum for the defined energy. The drawback associated with this approach is that the contour has a tendency to get stuck at undesired local minima caused by subtle and undesired image features/edges. Thus, active contour based curve evolution approaches are very sensitive to initialization and noise. The central theme of this thesis is to develop techniques that can make active contour models robust against certain classes of local minima by incorporating global information in energy minimization. These techniques lead to energy minimization with global considerations; we call these models  – 'Non-local active contours'. In this thesis, we consider three widely used active contour models: 1) Edge- and region-based segmentation model, 2) Prior shape knowledge based segmentation model, and 3) Motion segmentation model. We analyze the traditional techniques used for these models and establish the need for robust models that avoid local minima. We address the local minima problem for each model by adding global image considerations. Advisors/Committee Members: Dr. Yezzi, Anthony (Committee Chair), Dr. Barnes, Chris (Committee Member), Dr. Narasimha, Rajesh (Committee Member), Dr. Oshinski, John (Committee Member), Dr. Tannenbaum, Allen (Committee Member), Dr. Vela, Patricio (Committee Member).

Subjects/Keywords: Active geodesics; Fast marching; Active contour models; Imge segmentation; Motion segmentation; Optical flow; Shape priors; Localized principal component analysis; Pattern perception; Pattern recognition systems; Computer vision

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Appia, V. V. (2012). Non-local active contours. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44739

Chicago Manual of Style (16th Edition):

Appia, Vikram VijayanBabu. “Non-local active contours.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 06, 2020. http://hdl.handle.net/1853/44739.

MLA Handbook (7th Edition):

Appia, Vikram VijayanBabu. “Non-local active contours.” 2012. Web. 06 Apr 2020.

Vancouver:

Appia VV. Non-local active contours. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Apr 06]. Available from: http://hdl.handle.net/1853/44739.

Council of Science Editors:

Appia VV. Non-local active contours. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44739

.