Feature Matching Improvement through Merging Features for Remote Sensing Imagery

Karim, Shahid; Zhang, Ye; Brohi, Ali Anwar; Asif, Muhammad Rizwan

doi:10.1007/s13319-018-0203-x

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…To make correspondence between image features, a similarity measure between their descriptors is used. Karim et al [19] proposed to combine SURF features with FAST [20] or BRISK [20] descriptors to provide an optimal solution for reliable and efficient feature matching. Since different image features may have similar descriptors, a robust matching algorithm needs to be adopted.…”

Section: Feature Extraction and Matching Algorithmsmentioning

confidence: 99%

Analysis of Different Image Enhancement and Feature Extraction Methods

et al. 2022

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This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

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Section: Feature Extraction and Matching Algorithmsmentioning

confidence: 99%

Analysis of Different Image Enhancement and Feature Extraction Methods

et al. 2022

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“…A robust "coarse-to-fine registration (CCFR)" image registration algorithm is proposed [29]. A feature matching algorithm using the combination of FAST feature points and SURF descriptors is proposed in [30].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid approach for Image Acquisition Methods based on Feature Based Image Registration (FBIR)

Kumawat

Panda

2022

Preprint

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In this paper, the problem of Feature-Based Image Registration (FBIR) has been addressed using a novel hybrid feature detector. To prove the efficiency of the proposed hybrid feature detector, its performance is compared with that of the state-of-the-art feature detectors like BRISK, FAST, ORB, Harris, MinEigen and MSER. Three different image acquisition methods are choosen for comparison. They are (i) Rotation transformation (ii) Scene to model transformation and (iii) Scaling transformation. Taking into consideration the above three types of image acquisition methods, a FBIR method is proposed for different types of remote sensing images. This paper proposed a hybrid feature detection algorithm, that consumes less time to detect the feature key points and it is also rotation and scale invariant. In addition to this the proposed algorithm performs well in terms of match points and match rate. This paper also focuses on a comparative analysis of BRISK, FAST, Harris, MinEigen, MSER and ORB with the proposed algorithm in terms of time to detect feature keypoints. It can be observed from the results and tables that in the case of hybrid feature detectors,It takes less time to detect the feature key points.

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“…The quality of images is the vital aid towards cloud computing and high resolution imagery which is being observed by quality of experience (QoE) [25,26] and quality of service (QoS) [27]; the image quality can also be enhance by using optimal feature assessment technique [28,29] and pre-processing [30][31][32]. Nevertheless, the performance based on image quality is also enhanced with the help of post-processing [33,34] and feature merging methods [35][36][37] . Close infrared light is coordinated towards the focal point of the eyes; causing noticeable disparity in the cornea which is the external optical component of the eye.…”

Section: Overview Of Basic Technologymentioning

confidence: 99%

A Novel Eye-Tracking Device Designed with a Head Gesture Control Module

Karim¹,

Shakir²,

Sheikh³

et al. 2018

ICST Transactions on Scalable Information Systems

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In this paper a novel eye-tracking device designed which uses tiny CCD cameras to capture the eye image from the screen with the help of a video capture card. In the head gesture control mode, a light source projector is turned on and the CCD camera detects the position of the light source. The locations of the spots on the screen and on the image pupil of the eye image are calculated, compared with the previous point and are subsequently mapped to the point on the screen. The movement increment-coordinate control is also discussed which could improve the ease of use of the computer. We investigate the use of non-rigid head fixation using a helmet that constrains only general head orientation and allows some freedom of movement. Device results simulated with the help of software which achieves excellent timing performance due to the use of live data streaming, instead of the traditionally employed data storage mode for processing analogous eye position data.

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Feature Matching Improvement through Merging Features for Remote Sensing Imagery

Cited by 4 publications

References 20 publications

Analysis of Different Image Enhancement and Feature Extraction Methods

Analysis of Different Image Enhancement and Feature Extraction Methods

A Hybrid approach for Image Acquisition Methods based on Feature Based Image Registration (FBIR)

A Novel Eye-Tracking Device Designed with a Head Gesture Control Module

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