Curvelet-based geodesic snakes for image segmentation with multiple objects

2010 ◽  
Vol 31 (5) ◽  
pp. 355-360 ◽  
Author(s):  
Hao Shan ◽  
Jianwei Ma
Keyword(s):  
Image Segmentation ◽  
Multiple Objects

scholarly journals An improved model for joint segmentation and registration based on linear curvature smoother

2016 ◽  
Vol 10 (4) ◽  
pp. 314-324 ◽  
Author(s):  
Mazlinda Ibrahim ◽  
Ke Chen ◽  
Lavdie Rada
Keyword(s):  
Image Segmentation ◽  
Medical Imaging ◽  
Large Deformation ◽  
Active Contour ◽  
Numerical Results ◽  
Variational Model ◽  
Multiple Objects ◽  
Proposed Model ◽  
Challenging Tasks ◽  
Improved Model

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope.

An evaluation metric for image segmentation of multiple objects

2009 ◽  
Vol 27 (8) ◽  
pp. 1223-1227 ◽  
Author(s):  
Mark Polak ◽  
Hong Zhang ◽  
Minghong Pi
Keyword(s):  
Image Segmentation ◽  
Multiple Objects ◽  
Evaluation Metric

scholarly journals A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Chaolu Feng ◽  
Jinzhu Yang ◽  
Chunhui Lou ◽  
Wei Li ◽  
Kun Yu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Bias Correction ◽  
Active Contour Model ◽  
Multiple Objects ◽  
Level Set Function ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Term Energy ◽  
Set Function

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

scholarly journals LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

2010 ◽  
Vol 29 (12) ◽  
pp. 2023-2037 ◽  
Author(s):  
Yin Yin ◽  
Xiangmin Zhang ◽  
Rachel Williams ◽  
Xiaodong Wu ◽  
Donald D Anderson ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Knee Joint ◽  
Multiple Objects ◽  
Optimal Graph

scholarly journals Analysis and tracking of animal movements through granulation of temporal domain (GTD)

2018 ◽  
Vol 7 (4.5) ◽  
pp. 501
Author(s):  
Neelam Rawat ◽  
J. S. Sodhi ◽  
Rajesh K. Tyagi
Keyword(s):  
Image Segmentation ◽  
Granular Computing ◽  
Computing Methodology ◽  
Movement Patterns ◽  
Technological Advancement ◽  
Tracking Systems ◽  
Multiple Objects ◽  
Temporal Domain ◽  
Moving Patterns

Thanks to technological advancement in tracking systems, observing the moving patterns of a population is easier now. Capturing the movement patterns is mostly done through various tracking technologies like GPS, GIS etc. but these tracking systems basically require one or more combination of the same, and also, a node or tag to be placed on the moving object. Tracking of movement patterns basically employs a motion model which describes how the image of the object might change in different possible positions of the object during video surveillance. To track multiple objects without deformation during movement, the granulation of temporal domain (GTD) is pro- posed.GTD method is for demodulation of granule adaptation of video image segmentation on the basis of granular computing methodology. In image segmentation, i.e., image granulation through temporal domain, it improves the quality of surveillance important for wild-life protec- tion, and decision making in terms of security.  

Stereo modeling of HVEM specimens by serial tilting and computer graphics

1986 ◽  
Vol 44 ◽  
pp. 34-35
Author(s):  
J.R. McIntosh ◽  
D.L. Stemple ◽  
William Bishop ◽  
G.W. Hannaway
Keyword(s):  
Computer Graphics ◽  
Analytical Methods ◽  
Photographic Film ◽  
Complex Structures ◽  
Multiple Objects ◽  
Multiple Images ◽  
3 Dimensional

EM specimens often contain 3-dimensional information that is lost during micrography on a single photographic film. Two images of one specimen at appropriate orientations give a stereo view, but complex structures composed of multiple objects of graded density that superimpose in each projection are often difficult to decipher in stereo. Several analytical methods for 3-D reconstruction from multiple images of a serially tilted specimen are available, but they are all time-consuming and computationally intense.

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