scholarly journals Unsupervised segmentation of MRI knees using image partition forests

2016 ◽  
Author(s):  
Marija Marčan ◽  
Irina Voiculescu
Keyword(s):  
Unsupervised Segmentation ◽  
Image Partition

scholarly journals Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4818
Author(s):  
Nils Mandischer ◽  
Tobias Huhn ◽  
Mathias Hüsing ◽  
Burkhard Corves
Keyword(s):  
Machine Learning ◽  
Shop Floor ◽  
Unsupervised Segmentation ◽  
User Friendliness ◽  
Industrial Environment ◽  
Machine Learning Methods ◽  
Eu Project ◽  
Detection And Localization ◽  
Industrial Context ◽  
The Eu

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at 0.85 Hz.

scholarly journals Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

2017 ◽  
Vol 81 ◽  
pp. 223-243 ◽  
Author(s):  
Thaína A. Azevedo Tosta ◽  
Paulo Rogério Faria ◽  
Leandro Alves Neves ◽  
Marcelo Zanchetta do Nascimento
Keyword(s):  
Genetic Algorithm ◽  
Computational Method ◽  
Unsupervised Segmentation ◽  
Histological Images

Unsupervised Segmentation of Stereoscopic Video Objects: Constrained Segmentation Fusion Versus Greedy Active Contours

2014 ◽  
Vol 81 (2) ◽  
pp. 153-181 ◽  
Author(s):  
Klimis S. Ntalianis ◽  
Anastasios D. Doulamis ◽  
Nikolaos D. Doulamis ◽  
Nikos E. Mastorakis ◽  
Athanasios S. Drigas
Keyword(s):  
Active Contours ◽  
Stereoscopic Video ◽  
Unsupervised Segmentation ◽  
Video Objects

scholarly journals Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization

2014 ◽  
Vol 28 (4) ◽  
pp. 499-514 ◽  
Author(s):  
Qaiser Mahmood ◽  
Artur Chodorowski ◽  
Andrew Mehnert ◽  
Johanna Gellermann ◽  
Mikael Persson
Keyword(s):  
Source Localization ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Eeg Source Localization

scholarly journals Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1193 ◽  
Author(s):  
Yongji Wang ◽  
Qingwen Qi ◽  
Ying Liu
Keyword(s):  
Distance Measures ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Two Measures ◽  
Multiresolution Segmentation ◽  
Adjacent Segments

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

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