SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

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Robust Wide Baseline Feature Point Matching Based on Scale Invariant Feature Descriptor

Lecture Notes in Computer Science - Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues ◽

10.1007/978-3-540-87442-3_42 ◽

2008 ◽

pp. 329-336 ◽

Cited By ~ 2

Author(s):

Sicong Yue ◽

Qing Wang ◽

Rongchun Zhao

Keyword(s):

Feature Point ◽

Feature Descriptor ◽

Scale Invariant ◽

Point Matching ◽

Invariant Feature ◽

Feature Point Matching ◽

Scale Invariant Feature

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Face image abstraction by Ford-Fulkerson algorithm and invariant feature descriptor for human identification

2014 International Carnahan Conference on Security Technology (ICCST) ◽

10.1109/ccst.2014.6987037 ◽

2014 ◽

Author(s):

Dakshina Ranjan Kisku ◽

Debanjan Chatterjee ◽

S. Trivedy ◽

Massimo Tistarelli

Keyword(s):

Human Identification ◽

Face Image ◽

Feature Descriptor ◽

Invariant Feature ◽

Image Abstraction

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Different Viewpoints Image Registration for Remote Sensing Based on Multiple Image Features

10.20944/preprints201705.0027.v1 ◽

2017 ◽

Author(s):

Kun Yang ◽

Anning Pan ◽

Yang Yang ◽

Su Zhang ◽

Sim Heng Ong

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Geometric Structure ◽

Remote Sensing Image ◽

Image Features ◽

Google Earth ◽

Reference Image ◽

Feature Descriptor ◽

Multiple Image ◽

Invariant Feature

Remote sensing image registration with different viewpoints plays an important role in the field of geographic information system. However, when there exists ground relief variations and imaging viewpoint changes, non-rigid distortion occurs thus the registration becomes increasingly challenging. The current methods will suffer from missing true correspondences when non-rigid geometric distortion occurs. To address the problem, we propose a robust remote sensing image registration method based on SIFT feature distance and geometric structure features. At first, the scale-invariant feature transform (SIFT), a partial intensity invariant feature descriptor is used to extract reliable feature point set from sensed and reference image respectively. Secondly, a novel algorithm based on multiple image features which constrains the geometric structure during transformation is used to estimate exact correspondences between point sets. Finally, an accurate alignment is achieved by mapping the sensed image to reference image using thin-plate spline. We evaluated the performances of the proposed method by three sets of remote sensing images obtained from the unmanned aerial vehicle (UAV) and the Google earth, and compared with five state-of-the-art methods where our algorithm solved the non-rigid registration problem of remote sensing image with different viewpoints and showed the best alignments in most cases.

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