Research on Feature Description Based on Global Gray Order Pattern

Author(s):  
Chunrong Zhou
Author(s):  
Nur Farhana Rosli ◽  
◽  
Musab Sahrim ◽  
Wan Zakiah Wan Ismail ◽  
Irneza Ismail ◽  
...  

2002 ◽  
Vol 39 (5) ◽  
pp. 749-764 ◽  
Author(s):  
Nicholas Culshaw ◽  
Peter Reynolds ◽  
Gavin Sinclair ◽  
Sandra Barr

We report amphibole and mica 40Ar/39Ar ages from the Makkovik Province. Amphibole ages from metamorphic rocks decrease towards the interior of the province, indicating a first-order pattern of monotonic cooling with progressive migration of the province into a more distal back-arc location. The amphibole data, in combination with muscovite ages, reveal a second-order pattern consisting of four stages corresponding to changing spatial and temporal configurations of plutonism and deformation. (1) The western Kaipokok domain cooled through muscovite closure by 1810 Ma, long after the cessation of arc magmatism. (2) The Kaipokok Bay shear zone, bounding the Kaipokok and Aillik domains, cooled through amphibole closure during 1805–1780 Ma, synchronous with emplacement of syn-tectonic granitoid plutons. (3) Between 1740 and 1700 Ma, greenschist-facies shearing occurred along the boundary between the Kaipokok domain and Nain Province synchronous with A-type plutonism and localized shearing in the western Kaipokok domain, cooling to muscovite closure temperatures in the Kaipokok Bay shear zone, and A-type plutonism and amphibole closure or resetting in the Aillik domain. (4) In the period 1650–1640 Ma, muscovite ages, an amphibole age from a shear zone, and resetting of plutonic amphibole indicate a thermal effect coinciding in part with Labradorian plutonism in the Aillik domain. Amphibole ages from dioritic sheets in the juvenile Aillik domain suggest emplacement between 1715 and 1685 Ma. Amphibole ages constrain crystallization of small mafic plutons in the Kaipokok domain (reworked Archean foreland) to be no younger than 1670–1660 Ma. These ages are the oldest yet obtained for Labradorian plutonism in the Makkovik Province.


Author(s):  
Ghazali Syamni

This paper examines the relationship of behavior trading investor using data detailed transaction history-corporate edition demand and order history in Indonesia Stock Exchange during period of March, April and May 2005. Peculiarly, behavior placing of investor order at trading volume. The result of this paper indicates that trading volume order pattern to have pattern U shape. The pattern happened that investors have strong desires to places order at the opening and close of compared to in trading periods. While the largest orders are of market at the opening indicates that investor is more conservatively when opening, where many orders when opening has not happened transaction to match. In placing order both of investor does similar strategy. By definition, informed investors’ orders more large than uninformed investors. If comparison of order examined hence both investors behavior relatively changes over time. But, statistically shows there is not ratio significant. This implies behavior trading of informed investors and uninformed investors stable relative over time. The result from regression analysis indicates that informed investors to correlate at trading volume in all time intervals, but not all uninformed investors correlates in every time interval. This imply investor order inform is more can explain trading volume pattern compared to uninformed investor order in Indonesia Stock Exchange. Finally, result of regression also finds that order status match has greater role determines trading volume pattern intraday especially informed buy match and informed sale match. While amend, open and withdraw unable to have role to determine intraday trading volume pattern.


2019 ◽  
Vol 55 (7) ◽  
pp. 476-483
Author(s):  
Shohei WAKITA ◽  
Takayuki NAKAMURA ◽  
Hirotaka HACHIYA
Keyword(s):  

Author(s):  
L. Chen ◽  
F. Rottensteiner ◽  
C. Heipke

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.


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