Diagnosis of Arbitrary Defects Using Neighborhood Function Extraction

2005 ◽  
Author(s):  
Rao Desineni ◽  
R.D. Blanton
Keyword(s):  
Neighborhood Function

Color and Texture Feature Based Scene Classification

2020 ◽  
Vol 17 (11) ◽  
pp. 4897-4901
Author(s):  
V. Gayathri ◽  
Eric Clapten ◽  
S. Mahalakshmi ◽  
S. Rajes Kannan
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Texture Feature ◽  
Rotation Invariance ◽  
Scene Classification ◽  
Work Of Art ◽  
Image Brightness ◽  
Spatial Features ◽  
Neighborhood Function ◽  
Feature Based

Right now, overall trademark based multiscale multiresolution multistructure (M3LBP) neighborhood parallel example and nearby characteristic based totally min blend feature extraction is proposed for scene category. To extract international functions, characterize the leading spatial features in a couple of scale, a couple of choice, more than one structure way. The micro/macro shape facts and rotation invariance are guaranteed inside the worldwide function extraction approach. Neighborhood function extraction, coloration histogram characteristic (CHF) can thoroughly explain the spatial coloration statistics of an image. It also describes the image brightness, color statistics of a photo, which encompass the picture coloration distribution, photo assessment. The CHF can be computed from the min max shade quantizes. Ultimately Fused feature instance amongst nearby and international capabilities because the scene descriptor to prepare a portion based absolutely extreme finding a workable pace for scene style is outfitted. The proposed strategy is radically assessed on benchmark scene datasets (the 21 magnificence land use scene), and the trial results show that the proposed procedure prompts predominant kind standard execution as contrasted and the realm of-work of art style systems.

scholarly journals Approximating the Temporal Neighbourhood Function of Large Temporal Graphs

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 211 ◽  
Author(s):  
Pierluigi Crescenzi ◽  
Clémence Magnien ◽  
Andrea Marino
Keyword(s):  
Public Transportation ◽  
Arrival Time ◽  
Time Interval ◽  
Temporal Network ◽  
Temporal Networks ◽  
Starting Time ◽  
Neighborhood Function ◽  
Temporal Graphs ◽  
Temporal Path

Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.

scholarly journals Ordering process of self-organizing maps improved by asymmetric neighborhood function

2008 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Takaaki Aoki ◽  
Kaiichiro Ota ◽  
Koji Kurata ◽  
Toshio Aoyagi
Keyword(s):  
Self Organizing Maps ◽  
Neighborhood Function ◽  
Self Organizing

The Neighborhood Function and Its Application to Identifying Large‐Scale Structure in the Comoving Universe Frame

2007 ◽  
Vol 669 (2) ◽  
pp. 692-713 ◽  
Author(s):  
Y.‐P. Qin ◽  
L.‐Z. Lu ◽  
F.‐W. Zhang ◽  
B.‐B. Zhang ◽  
J. Zhang
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Neighborhood Function
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