A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image

Li, Jianguo; Ma, Quanhai

doi:10.1155/2020/9343461

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Eq. ( 7) gives time-efficient Matrix Multiplication DFT (MM-DFT) sub-pixel level cross-correlation is done in a 1.5 * 1.5-pixel neighborhood around the initial peak of Rft peak with an up-sampling factor of k to attain a sub-pixel resolution [13][14][15][16][17].…”

Section: Improved Up Sampled Cross-correlation Algorithmmentioning

confidence: 99%

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Ganesan¹,

Sankaranarayanan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image. A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation (UCC) algorithm. The vibration measurement is calculated using the Finite-Difference Algorithm (FDA), a machine vision system fitted with a macro lens sensor that is capable of capturing the image at a closer range, which does not affect the quality of displacement measurement from the video frames. The field test was conducted on the TAFE (Tractors and Farm Equipment Limited) tractor parts, and the percentage of error was recorded between 30% and 50% at very low vibration values close to zero, whereas it was recorded between 5% and 10% error in most high-accelerations, the essential range for vibration analysis. Finally, the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges. The use of a macro lens enables to capture of image frames at very close-ups. A 30% to 50% error percentage has been reported when the vibration amplitude is very small. Therefore, this study is not suitable for Nano vibration analysis.

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Section: Improved Up Sampled Cross-correlation Algorithmmentioning

confidence: 99%

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Ganesan¹,

Sankaranarayanan²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Image registration is important in medical image analysis [12,13,14]. Based on similarity metrics, registration methods could be generally classified into intensity-and feature-based methods.…”

Section: Related Workmentioning

confidence: 99%

To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information

et al. 2020

BioMed Research International

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To align multimodal images is important for information fusion, clinical diagnosis, treatment planning, and delivery, while few methods have been dedicated to matching computerized tomography (CT) and magnetic resonance (MR) images of lumbar spine. This study proposes a coarse-to-fine registration framework to address this issue. Firstly, a pair of CT-MR images are rigidly aligned for global positioning. Then, a bending energy term is penalized into the normalized mutual information for the local deformation of soft tissues. In the end, the framework is validated on 40 pairs of CT-MR images from our in-house collection and 15 image pairs from the SpineWeb database. Experimental results show high overlapping ratio (in-house collection, vertebrae 0.97±0.02, blood vessel 0.88±0.07; SpineWeb, vertebrae 0.95±0.03, blood vessel 0.93±0.10) and low target registration error (in-house collection, ≤2.00±0.62 mm; SpineWeb, ≤2.37±0.76 mm) are achieved. The proposed framework concerns both the incompressibility of bone structures and the nonrigid deformation of soft tissues. It enables accurate CT-MR registration of lumbar spine images and facilitates image fusion, spine disease diagnosis, and interventional treatment delivery.

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Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

Abraham

2023

Biomedical Signal Processing and Control

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A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image

Cited by 5 publications

References 23 publications

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information

Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

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