A level set framework using a new incremental, robust Active Shape Model for object segmentation and tracking

2009 ◽  
Vol 27 (8) ◽  
pp. 1157-1168 ◽  
Author(s):  
Michael Fussenegger ◽  
Peter Roth ◽  
Horst Bischof ◽  
Rachid Deriche ◽  
Axel Pinz
Keyword(s):  
Level Set ◽  
Object Segmentation ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape ◽  
Level Set Framework ◽  
Segmentation And Tracking

A Fully Automated Active Shape Model for Segmentation and Tracking of Unknown Objects in a Cluttered Environment

2006 ◽  
pp. 161-187 ◽  
Author(s):  
Besma Rouai-Abidi ◽  
Sangkyu Kang ◽  
Mongi Abidi
Keyword(s):  
Objective Function ◽  
Prior Knowledge ◽  
Optimization Criterion ◽  
Active Shape Model ◽  
Shape Model ◽  
Cluttered Environment ◽  
Active Shape ◽  
Actual Shape ◽  
Initial Target ◽  
Segmentation And Tracking

The segmentation of shapes is automated using a new objective function to deform and move a contour toward the actual shape. New profile modeling and optimization criterion to automatically find corresponding points are also applied for segmentation and tracking of people in cluttered backgrounds. The proposed framework utilizes a Pan-Tilt-Zoom (PTZ) camera and automatically performs the initial target acquisition through motion and color-based segmentation. Successful results are presented for within and between frame segmentation and tracking. This algorithm presents a major extension to the state of the art and the original active shape model (ASM) which was designed for known objects in smooth non-changing backgrounds and where the landmark points need to be manually picked off-line. This is a fully automated, real time ASM that deals with changing backgrounds and does not require prior knowledge of the object to be segmented and tracked.

Segmentation of Bladder and Prostate Cancer with Biological Materials Based on Center-Focus Active Shape Model

2012 ◽  
Vol 459 ◽  
pp. 103-109
Author(s):  
Yuan Tian
Keyword(s):  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Object Segmentation ◽  
Active Shape Model ◽  
Promising Technique ◽  
Shape Model ◽  
Active Shape ◽  
Model Technique ◽  
Ct Slices ◽  
Prior Task

Cancer boundary detection is an important prior task in cancer diagnosis, which will help to manage the next treatment and operation. Active shape model is proposed to solve this problem as a promising technique. But for segmentation tasks with background interference or cases such as organs with similar gray scale values, it does not always match the target contours as well as expect. This paper proposes an active shape model technique using center-focus method to robustly match the target boundaries. The algorithm has been applied on CT slices containing prostate or bladder to demonstrate the good performance of object segmentation

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape

Echocardiogram segmentation using active shape model and mean squared eigenvalue error

2021 ◽  
Vol 69 ◽  
pp. 102807
Author(s):  
Yasser Ali ◽  
Soosan Beheshti ◽  
Farrokh Janabi-Sharifi
Keyword(s):  
Active Shape Model ◽  
Shape Model ◽  
Active Shape

Three-dimensional active shape model matching for left ventricle segmentation in cardiac CT

2003 ◽  
Author(s):  
Hans C. van Assen ◽  
Rob J. van der Geest ◽  
Mikhail G. Danilouchkine ◽  
Hildo J. Lamb ◽  
Johan H. C. Reiber ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Ct ◽  
Three Dimensional ◽  
Active Shape Model ◽  
Model Matching ◽  
Shape Model ◽  
Active Shape ◽  
Ventricle Segmentation
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