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An enhanced markov random field (MRF) based approach for image segregation

Doctor of Philosophy in Computer Engineering

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Bibliographic Details
Main Author: Akbah A, Kalifa
Other Authors: Shahrul Nizam, Yaakob, Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Markov random fields
Image segmentation
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76657
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spelling my.unimap-766572022-11-01T01:30:38Z An enhanced markov random field (MRF) based approach for image segregation Akbah A, Kalifa Shahrul Nizam, Yaakob, Dr. Markov random fields Image segmentation Doctor of Philosophy in Computer Engineering The main objective of this thesis is to present an alternative technique to enhance the depth segregation of a 2D monocular image using Markov Random Field (MRF). Depth segregation considered challenging task due to an extensive segment of nonrigidity and textural contrasts among objects. Object appearance in various shape and location in the scene, depth segregation is likewise made difficult due to extra components, occluded objects, which can be either visible or totally invisible from the scene, variation in light distribution in image can give rise to a significant change in the aspect of the objects in the image most likely to increase the difficulty of the process. Here is what gives the rise to the problem of developing image segregation tools that can deal with this variation in images, this tool required to be flexible and robust to successfully segregate objects in to layers from a 2D monocular image. This thesis shows that monocular depth segregation can be successfully used in edge region based image segmentation. Depth image segregation enhanced technique has been proposed to search for depth cues in the image regions, the image regions corresponding to a specified level of depth cue in occluding and occluded objects such as T-junction and L-junction can be identified and classified. The proposed technique initially executes image segmentation; to identify region in the image, morphological operation; to eliminate any error pixel in the region, then edge detection; to identify the boundaries of the regions, and use these information to perform depth segregation processes to identify and label the objects in depth labeled order. The experimental result shows that the technique has successfully segregate the regions in depth order from a 2D monocular image. The segmented image has been combined with the edge detection image to perform the depth segregation process and the result was efficient in terms of object labeling order. 2017 2022-11-01T01:30:38Z 2022-11-01T01:30:38Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76657 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Markov random fields
Image segmentation
spellingShingle Markov random fields
Image segmentation
Akbah A, Kalifa
An enhanced markov random field (MRF) based approach for image segregation
description Doctor of Philosophy in Computer Engineering
author2 Shahrul Nizam, Yaakob, Dr.
author_facet Shahrul Nizam, Yaakob, Dr.
Akbah A, Kalifa
format Thesis
author Akbah A, Kalifa
author_sort Akbah A, Kalifa
title An enhanced markov random field (MRF) based approach for image segregation
title_short An enhanced markov random field (MRF) based approach for image segregation
title_full An enhanced markov random field (MRF) based approach for image segregation
title_fullStr An enhanced markov random field (MRF) based approach for image segregation
title_full_unstemmed An enhanced markov random field (MRF) based approach for image segregation
title_sort enhanced markov random field (mrf) based approach for image segregation
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76657
_version_ 1751537935820259328
score 13.222552

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