Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Edge strength functions as shape priors in image segmentation
Date
2005-01-01
Author
Erdem, E
Erdem, A
Tari, S
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
149
views
0
downloads
Cite This
Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Crimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator function, known as the "edge strength function", which provides a distance-like surface for the shape boundary. Our method can handle arbitrary deformations due to shape variability as well as plane Euclidean transformations. The method is also robust with respect to noise and missing parts. Furthermore, this formulation does not require simple closed curves as in a typical level set formulation.
Subject Keywords
Active Contours
URI
https://hdl.handle.net/11511/66853
Journal
ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Edge strength functions as shape priors in image segmentation
Erdem, Erkut; Erdem, Aykut; Tarı, Zehra Sibel (2005-12-01)
Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Grimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator functi...
Object recognition and segmentation via shape models
Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016)
In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
Visual detection and tracking of moving objects
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2007-06-13)
In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...
3D Extended Object Tracking Using Recursive Gaussian Processes
Kumru, Murat; Özkan, Emre (2018-07-10)
In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can joi...
Visual similarity for hdr images with applications to tone mapping
Aydınlılar, Merve; Akyüz, Ahmet Oğuz; Tarı, Zehra Sibel; Department of Computer Engineering (2021-2-15)
Assessing visual similarity between images is important for many computer vision applications. So far, investigations on visual similarity have been confined to low dynamic range images. However, recently, there is a growing interest to high dynamic range (HDR) imaging. In this thesis, the aim is to shed light on visual image similarity for HDR images by following an experimental approach. To this end, a user experiment is conducted through a novel web-based interface, in which the participants assess the p...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Erdem, A. Erdem, and S. Tari, “Edge strength functions as shape priors in image segmentation,”
ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS
, pp. 490–502, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66853.