Robust SIFT-based feature matching using Kendall's rank correlation measure

Kordelas, Georgios; Daras, Petros

doi:10.1109/icip.2009.5413514

Cited by 13 publications

(10 citation statements)

References 10 publications

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“…It is easy to prove that (2) is equivalent to (1). Suppose G is an image with the same size of F, then we have the local entropy absolute difference vector…”

Section: )mentioning

confidence: 98%

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Parallel image matching for aircraft navigation

Dong

Sun

Chi

et al. 2010

2010 3rd International Congress on Image and Signal Processing

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Image matching is an important technique for aircraft navigation. This paper presents a novel parallel image matching algorithm based on local entropy vector (LEV). Spatial information is introduced into the definition of image entropy and the proposed algorithm confirms the robustness of LEV to irradiation distortion, geometric distortion, local noise interference and partial occlusion. An equivalent transform of the image entropy definition is also employed to make it suitable for parallel processing. The matching results of the proposed algorithm are validated by experiments on a variety of images in natural environment.

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“…It is easy to prove that (2) is equivalent to (1). Suppose G is an image with the same size of F, then we have the local entropy absolute difference vector…”

Section: )mentioning

confidence: 98%

“…It has been widely used in aircraft navigation, resource analysis, weather forecast and medical diagnosis [1], [2]. In aircraft navigation system, aerial photographs or satellite images of the terrains where an aircraft is going to fly over are taken and stored in the computer memory on aircraft as reference image before the flight.…”

Section: Introductionmentioning

confidence: 99%

Parallel image matching for aircraft navigation

Dong

Sun

Chi

et al. 2010

2010 3rd International Congress on Image and Signal Processing

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show abstract

“…Furthermore, we only focus on the relative positions of the elements in a sequence in this field, i.e., focus on their rank correlation. With this concern, Kendall rank correlation coefficient is widely used and proved to have great sensitiveness and discriminative ability [21,22].…”

Section: Kendall Rank Correlation Coefficientmentioning

confidence: 99%

Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping

Guan

Feng

et al. 2018

Energies

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In regional power grids with high wind power penetration, wind turbine tripping poses great challenges to short-term voltage stability. Dynamic reactive power (VAR) compensation (DVC) plays an important role in securing wind farm operation. To address short-term voltage stability issues, voltage disturbance index (DI) and voltage supporting index (SI) are defined to evaluate the degree of voltage fluctuation and voltage supporting ability of a bus, respectively. Then corresponding vector-type features, called disturbance vector (DV) and supporting vector (SV) are proposed based on the defined indexes. The Kendall rank correlation coefficient is adopted to evaluate the matching degree of DV and SV, so as to determine the influenced area of each wind farm. Candidate locations for DVC are determined sequentially. By comprehensively considering the probability of combined disturbance in each wind farm, a site selection method is proposed and then genetic algorithm is applied to optimize the DVC capacity considering short-term voltage security. The proposed method is applied on a modified NE 39-bus system and a real power grid. Comparison with the engineering practice-based method validates its effectiveness.

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“…(b) Estimate model parameters of every SSM-SOE. In parameters estimation process of SSM-SOE, ( ) ( ) ( ) proportional to the correlation between x and () j y according to Kendall rank correlation definition [1]. The weight () j  can be calculated as…”

Section: Parameter Estimationmentioning

confidence: 99%

A State-Space Degradation Model with Multiple Observations and Different Sampling Times

Ni¹

2018

IJPE

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A traditional state-space model (SSM) only contains one observation equation. There are some restricted conditions when using the traditional SSM to describe the evolution process between a state indicator and multiple observation indicators instantaneously. In order to solve this problem, this paper puts forward an SSM that has multiple observation equations, which can be applied to multiple observation indicators with different sampling times. The modeling process and parameters evaluation approach of the proposed SSM are studied and given. A simulation study is conducted to indicate advantages of the proposed SSM when sampling times and observation equations are not the same for different observation indicators. Simulation results show that the proposed SSM is more accurate than the traditional SSM in system degradation prediction.

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Robust SIFT-based feature matching using Kendall's rank correlation measure

Cited by 13 publications

References 10 publications

Parallel image matching for aircraft navigation

Parallel image matching for aircraft navigation

Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping

A State-Space Degradation Model with Multiple Observations and Different Sampling Times

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