國立虎尾科技大學 |

Image Segmentation Using Markov Random Field.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Image Segmentation Using Markov Random Field./
作者:	Tatavarti, Aparna.
面頁冊數:	1 online resource (62 pages)
附註:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369758245

Image Segmentation Using Markov Random Field.
Tatavarti, Aparna.

Image Segmentation Using Markov Random Field. - 1 online resource (62 pages)

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2017.

Includes bibliographical references

This thesis describes an algorithm for efficient segmentation of point cloud data into local planar surface regions. This is a problem of generic interest to researchers in the computer graphics, computer vision, artificial intelligence and robotics community where it plays an important role in applications such as object recognition, mapping, navigation and conversion from point clouds representations to 3D surface models. Prior work on the subject is either computationally burdensome, precluding real time applications such as robotic navigation and mapping, prone to error for noisy measurements commonly found at long range or requires availability of co-registered color imagery. The approach we describe consists of 3 steps: (1) detect a set of candidate planar surfaces, (2) cluster the planar surfaces merging redundant plane models, and (3) segment the point clouds by imposing a Markov Random Field (MRF) on the data and planar models and computing the Maximum A-Posteriori (MAP) of the segmentation labels using Bayesian Belief Propagation (BBP). In contrast to prior work which relies on color information for geometric segmentation, our implementation performs detection, clustering and estimation using only geometric data. Novelty is found in the fast clustering technique and new MRF clique potentials that are heretofore unexplored in the literature. The clustering procedure removes redundant detections of planes in the scene prior to segmentation using BBP optimization of the MRF to improve performance. The MRF clique potentials dynamically change to encourage distinct labels across depth discontinuities. These modifications provide improved segmentations for geometry-only depth images while simultaneously controlling the computational cost. Algorithm parameters are tunable to enable researchers to strike a compromise between segmentation detail and computational performance. Experimental results apply the algorithm to depth images from the NYU depth dataset which indicate that the algorithm can accurately extract large planar surfaces from depth sensor data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369758245Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Image Segmentation Using Markov Random Field.
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500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: Andrew Willis.
502 $a Thesis (M.S.)--The University of North Carolina at Charlotte, 2017.
504 $a Includes bibliographical references
520 $a This thesis describes an algorithm for efficient segmentation of point cloud data into local planar surface regions. This is a problem of generic interest to researchers in the computer graphics, computer vision, artificial intelligence and robotics community where it plays an important role in applications such as object recognition, mapping, navigation and conversion from point clouds representations to 3D surface models. Prior work on the subject is either computationally burdensome, precluding real time applications such as robotic navigation and mapping, prone to error for noisy measurements commonly found at long range or requires availability of co-registered color imagery. The approach we describe consists of 3 steps: (1) detect a set of candidate planar surfaces, (2) cluster the planar surfaces merging redundant plane models, and (3) segment the point clouds by imposing a Markov Random Field (MRF) on the data and planar models and computing the Maximum A-Posteriori (MAP) of the segmentation labels using Bayesian Belief Propagation (BBP). In contrast to prior work which relies on color information for geometric segmentation, our implementation performs detection, clustering and estimation using only geometric data. Novelty is found in the fast clustering technique and new MRF clique potentials that are heretofore unexplored in the literature. The clustering procedure removes redundant detections of planes in the scene prior to segmentation using BBP optimization of the MRF to improve performance. The MRF clique potentials dynamically change to encourage distinct labels across depth discontinuities. These modifications provide improved segmentations for geometry-only depth images while simultaneously controlling the computational cost. Algorithm parameters are tunable to enable researchers to strike a compromise between segmentation detail and computational performance. Experimental results apply the algorithm to depth images from the NYU depth dataset which indicate that the algorithm can accurately extract large planar surfaces from depth sensor data.
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538 $a Mode of access: World Wide Web
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690 $a 0544
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