Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain
Egyptian Journal of Remote Sensing and Space Sciences, 2011 This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT) features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images.
Xiangzeng Liu +3 more
doaj +1 more source
Automatic registration of remote sensing images based on SIFT and fuzzy block matching for change detection [PDF]
International Journal of Computational Intelligence Systems, 2011 This paper presents an automated image registration approach to detecting changes in multi-temporal remote sensing images. The proposed algorithm is based on the scale invariant feature transform (SIFT) and has two phases. The first phase focuses on SIFT
Cai Guo-Rong +4 more
doaj +1 more source
Comparison of spatial domain optimal trade-off maximum average correlation height (OT-MACH) filter with scale invariant feature transform (SIFT) using images with poor contrast and large illumination gradient [PDF]
, 2015 A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the ...
Alkandri, A +5 more
core +1 more source
SAR image matching based on rotation-invariant description
Scientific Reports, 2023 The utilization of scale invariant feature transform algorithm in synthetic-aperture radar images (SAR–SIFT) to match image features may lead to principal orientation assignments of descriptors being affected by speckle noise, thereby diminishing ...
Yunhao Chang +5 more
doaj +1 more source
ICA-ASIFT-Based Multi-Temporal Matching of High-Resolution Remote Sensing Urban Images
Cybernetics and Information Technologies, 2016 While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low ...
Huang Fenghua, Mao Zhengyuan, Shi Wenzao
doaj +1 more source
Environmental target image recognition of coal mine rescue robot
Gong-kuang zidonghua, 2017 In view of problems that traditional scale-invariant feature transform（SIFT） algorithm has calculation redundancy in searching key points of vision images and poor real-time performance in target recognition, an improved SIFT algorithm was proposed which
GONG Gu, ZHU Hua
doaj +1 more source
Local descriptor for retinal fundus image registration
IET Computer Vision, 2020 A feature‐based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre‐processing, feature point extraction, feature descriptor, matching and geometrical transformation.
Roziana Ramli +7 more
doaj +1 more source
Scale variant vehicle object recognition by CNN module of multi-pooling-PCA process
Journal of Intelligent and Connected Vehicles, 2023 The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances. The premise of advanced driver assistance system (ADAS) system for safety surveillance and safe driving is early identification of ...
Yuxiang Guo, Itsuo Kumazawa, Chuyo Kaku
doaj +1 more source
PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines ...
O. G. Ajayi
doaj +1 more source
Performance of Interpolated Histogram of Oriented Gradients on the Feature Calculation of SIFT
Advances in Electrical and Computer Engineering, 2022 Scale Invariant Feature Transform (SIFT) is the most dominant and robust object detection algorithm. It utilizes the Histogram of Oriented Gradients (HOG) method for feature computation.
OZTURK, A., CAYIROGLU, I.
doaj +1 more source