scholarly journals Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization

2013 ◽  
pp. 185-208 ◽  
Author(s):  
Neil Birkbeck ◽  
Michal Sofka ◽  
Timo Kohlberger ◽  
Jingdan Zhang ◽  
Jens Wetzl ◽  
...  
Keyword(s):  
Level Set ◽  
Set Optimization ◽  
Multi Stage ◽  
Robust Segmentation

scholarly journals GPU-accelerated image segmentation based on level sets and multiple texture features

2020 ◽  
Author(s):  
Daniel Reska ◽  
Marek Kretowski
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Level Set ◽  
High Performance ◽  
Texture Features ◽  
Extraction Methods ◽  
Multi Stage ◽  
Multiple Feature ◽  
Feature Extraction And Selection ◽  
Contour Evolution

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

An Interactive Satisficing Method Based on Level Set Optimization for Fuzzy Random Multiobjective Linear Programming Problems

2009 ◽  
Author(s):  
Hideki Katagiri ◽  
Masatoshi Sakawa ◽  
Kosuke Kato ◽  
Ichiro Nishizaki ◽  
Sio-Iong Ao ◽  
...  
Keyword(s):  
Linear Programming ◽  
Level Set ◽  
Set Optimization ◽  
Multiobjective Linear Programming ◽  
Linear Programming Problems ◽  
Fuzzy Random

scholarly journals Object Tracking using Supervised Level_Set Model (SLSM)

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1712-1714
Keyword(s):  
Object Tracking ◽  
Level Set ◽  
Test Results ◽  
Multiple Target ◽  
Two Stage ◽  
Item Order ◽  
Multi Stage ◽  
Level Set Model ◽  
Item Location

An item discovery framework discovers objects in this present reality in an advanced picture or video, in which the article can have a place with any of articles to be specific people, vehicles, and so on. So as to distinguish an article in a picture or video the frameworkneeds couple of parts so as to finish the errand of recognizing an item, an element finder, a theorem and theorem checker.In this work survey of different strategies which are utilized to distinguish an article, limit an item, order an item, extricate highlights, appearance data in pictures and recordings. The remarks are dependent on the considered writing and major problems are likewise recognized significant to the item location. A thought regarding the conceivable answer for multiple class_object identification is likewise exhibited. This work is appropriate for specialists who are learners in this area.. We initially portray the proposed system of two-stage supervised level set model in target following, at that point give summed up multi-stage adaptation for managing multiple-target . Positive decline is utilized to modify the learning after some time, empowering following to proceed under fractional and add up to impediment. Test results in various testing arrangements approve the viability inproposed strategy

scholarly journals Object Tracking using Supervised Level_Set Model (Slsm)

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1181-1183
Keyword(s):  
Object Tracking ◽  
Level Set ◽  
Test Results ◽  
Multiple Target ◽  
Two Stage ◽  
Item Order ◽  
Multi Stage ◽  
Level Set Model ◽  
Item Location

An item discovery framework discovers objects in this present reality in an advanced picture or video, in which the article can have a place with any of articles to be specific people, vehicles, and so on. So as to distinguish an article in a picture or video the framework needs couple of parts so as to finish the errand of recognizing an item, an element finder, a theorem and theorem checker.In this work survey of different strategies which are utilized to distinguish an article, limit an item, order an item, extricate highlights, appearance data in pictures and recordings. The remarks are dependent on the considered writing and major problems are likewise recognized significant to the item location. A thought regarding the conceivable answer for multiple class_object identification is likewise exhibited. This work is appropriate for specialists who are learners in this area.. We initially portray the proposed system of two-stage supervised level set model in target following, at that point give summed up multi-stage adaptation for managing multiple-target . Positive decline is utilized to modify the learning after some time, empowering following to proceed under fractional and add up to impediment. Test results in various testing arrangements approve the viability in proposed strategy.

scholarly journals A soft sensor for property control in multi-stage hot forming based on a level set formulation of grain size evolution and machine learning

2021 ◽  
Vol 2 ◽  
pp. 100041
Author(s):  
Markus Bambach ◽  
Muhammad Imran ◽  
Irina Sizova ◽  
Johannes Buhl ◽  
Stephan Gerster ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Level Set ◽  
Soft Sensor ◽  
Hot Forming ◽  
Multi Stage ◽  
Size Evolution ◽  
Property Control ◽  
Level Set Formulation

scholarly journals Multi-Stage Liver Segmentation in CT Scans Using Gaussian Pseudo Variance Level Set

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101414-101423
Author(s):  
Lifang Zhou ◽  
Lu Wang ◽  
Weisheng Li ◽  
Bangjun Lei ◽  
Jianxun Mi ◽  
...  
Keyword(s):  
Level Set ◽  
Ct Scans ◽  
Liver Segmentation ◽  
Multi Stage

scholarly journals Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization

2016 ◽  
Vol 100 ◽  
pp. 57-77 ◽  
Author(s):  
Deepak N. Subramani ◽  
Pierre F.J. Lermusiaux
Keyword(s):  
Path Planning ◽  
Level Set ◽  
Optimal Path ◽  
Set Optimization ◽  
Optimal Path Planning
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