Probability-based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Zhi Zeng ◽  
Ting Wang ◽  
Fulei Ma ◽  
Liang Zhang ◽  
Peiyi Shen ◽  
...  
2012 ◽  
Author(s):  
Darya L. Zabelina ◽  
Emmanuel Guzman-Martinez ◽  
Laura Ortega ◽  
Marcia Grabowecky ◽  
Mark Beeman ◽  
...  

2010 ◽  
Vol 3 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Heike Baeskow

For many decades there has been a consensus among linguists of various schools that derivational suffixes function not only to determine the word-class of the complex expressions they form, but also convey semantic information. The aspect of suffix-inherent meaning is ignored by representatives of a relatively new theoretical direction – Neo-Construction Grammar – who consider derivational suffixes to be either purely functional elements of the grammar or meaningless phonological realizations of abstract grammatical morphemes. The latter view is maintained by adherents of Distributed Morphology, who at the same time emphasize the importance of conceptual knowledge for derivational processes without attempting to define this aspect. The purpose of this study is first of all to provide support for the long-standing assumption that suffixes are inherently meaningful. The focus of interest is on the suffixes -ship, -dom and -hood. Data from Old English and Modern English (including neologisms) will show that these suffixes have developed rich arrays of meaning which cannot be structurally derived. Moreover, since conceptual knowledge is indeed an important factor for word-formation processes, a concrete, theory-independent model for the representation of the synchronically observable meaning components associated with -ship, -dom and -hood will be proposed.


2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.


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