Results of automatic interpretation of AVHRR images. A symbolic and connectionist approach

Author(s):  
J.A. Torres ◽  
F. Guindos ◽  
M. Peralta ◽  
M. Canton
1991 ◽  
Author(s):  
George W. Rogers ◽  
Jeffrey L. Solka ◽  
Donald R. Vermillion ◽  
Carey E. Priebe

2020 ◽  
Vol 10 (1) ◽  
pp. 642-648
Author(s):  
Anna-Mari Wartiainen ◽  
Markus Harju ◽  
Satu Tamminen ◽  
Leena Määttä ◽  
Tuomas Alatarvas ◽  
...  

AbstractNon-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. Thus, the microscopic analysis of test specimens is an important part of the quality control. This steel purity analysis produces a large amount of individual inclusion information for each test specimen. The interpretation of the results is laborious and the comparison of larger product groups practically impossible. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. The developed tool can be used to improve the controlling of the steel purity or for automatic production of new inclusion cluster features that can be utilised further in quality prediction models, for example.


1994 ◽  
Vol 5 (3) ◽  
pp. 12-22 ◽  
Author(s):  
Christian Jacquemin

2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.


2007 ◽  
Vol 30 (10) ◽  
pp. 2236-2247 ◽  
Author(s):  
Danielo G. Gomes ◽  
Nazim Agoulmine ◽  
Younès Bennani ◽  
J. Neuman de Souza

2011 ◽  
Author(s):  
Jun Guo ◽  
Kun Yang ◽  
Jie Sun ◽  
Gang Wang ◽  
Wen-sheng Wang

Sign in / Sign up

Export Citation Format

Share Document