Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features

Author(s):  
Jienan ZHANG ◽  
Shouyi YIN ◽  
Peng OUYANG ◽  
Leibo LIU ◽  
Shaojun WEI
ROBOT ◽  
2010 ◽  
Vol 32 (2) ◽  
pp. 241-247 ◽  
Author(s):  
Changfeng NIU ◽  
Dengfeng CHEN ◽  
Yushu LIU

2020 ◽  
Vol 3 (2) ◽  
pp. 128-143
Author(s):  
Giuditta Cirnigliaro

The present article combines an individual, object-based approach with digital technologies with the aim to define the relation of verbal and visual inscriptions in Leonardo da Vinci’s technical-scientific and literary-artistic works. By conducting a comparative analysis of Leonardo’s folios featuring fables, emblems, and engineering projects,I identify the archetypes of this interaction in the books contained in his personal library and examine the convergence of his use of empirical, diagrammatic, and pictorial strategies toward the investigation of nature. The material component of this study consists in a series of analytical drawing tables which examine recurrent patterns, and textual and visual connections in Leonardo’s manuscripts. The identified patterns are subsequently cataloged and examined through the web-publishing platform “LILeo” created in collaboration with the Rutgers Digital Humanities Laboratory as part of my dissertation project. By digitally highlighting the interaction of elements on the space of the page, and enabling the layering of drafts belonging to similar projects in Leonardo’s works and sources, this study traces the formal patterns of the artist’s analytical thinkingin order to uncover the origins of his interdisciplinary research.


2014 ◽  
Vol 1044-1045 ◽  
pp. 1302-1308
Author(s):  
Shao Mei Li ◽  
Kai Wang ◽  
Chao Gao ◽  
Ya Wen Wang

To improves tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, tracking object frame by frame via color histogram and particle filtering. Secondly, reversely validating the tracking result based on particle filtering. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.


1978 ◽  
Vol 85 (3) ◽  
pp. 192-206 ◽  
Author(s):  
David M. Green ◽  
Theodore G. Birdsall

Author(s):  
Catherine M. Arrington ◽  
Dale Dagenbach ◽  
Maura K. McCartan ◽  
Thomas H. Carr
Keyword(s):  

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