Image registration quality assessment with similarity measures - A research study

G, Sindhu Madhuri; Gandhi, M. P. Indra

doi:10.1109/iccsp.2015.7322613

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…In the past, a hybrid clustering-based approach was utilized, where the right number of clusters was decided upon first, followed by clustering. The data was grouped using the Auto encoder technique, and the genetic method was used to estimate the ideal number of clusters [43]. Due to the limitations of the prior study, we chose to use more advanced clustering approaches that produce superior outcomes to the earlier work.…”

Section: Proposed Methodologymentioning

confidence: 99%

Implementation of Machine Learning-Based Data Mining Techniques for IDS

Mahesh

Vivek

Kumar

2023

International Journal of Information Technology, Research and A

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user behaviour patterns from network traffic data. Association rules and sequence rules are the main foundations of data mining used for intrusion detection. Given the Auto encoder algorithm's traditional method's bottleneck of frequent itemsets mining, we provide a Length-Decreasing Support to Identify Intrusion based on Data Mining, which is an upgraded Data Mining Techniques based on Machine Learning for IDS. Based on test results, it appears that the suggested strategy is successful

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Section: Proposed Methodologymentioning

confidence: 99%

Implementation of Machine Learning-Based Data Mining Techniques for IDS

Mahesh

Vivek

Kumar

2023

International Journal of Information Technology, Research and A

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“…𝐷: Λ 𝑀 → 𝑅 3 (𝛼 1 , … , 𝛼 𝑁 , 𝑀(𝐼 1 , 𝐼 2 ; 𝛼 1 , … , 𝛼 𝑁 )) ↦ (𝛼 𝑖 , 𝛼 𝑗 , 𝑀(𝐼 1 , 𝐼 2 ; 𝛼 𝑖 , 𝛼 𝑗 )).…”

Section: Dimensionality Reductionunclassified

“…One classical approach simply uses the value of the match metric that is used as part of the registration algorithm. Diverse match metrics have been developed [3], but as part of the registration algorithm, they offer no independent registration verification. Bootstrapping approaches have been used [6] in which registration solutions over subsets of pixels are generated.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to image registration verification

DelMarco,

Webb,

Stolle

et al. 2023

Multimodal Image Exploitation and Learning 2023

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Image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. Image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification.

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“…The new image registration technique that emerged with Radon and Slant transformation, and by considering Cameraman, Lena and VegCrop image sets with Rotation, Translation and Scaling as geometrical transformations [4,5,6,7]. The performance of new image registration technique is evaluated with RMSE, PSNR and Entropy measures, and tested for a specific application.…”

Section: Development Of New Image Registration Techniquementioning

confidence: 99%

Development of New Image Registration Techniques – A Research Framework

Madhuri

2018

IJARCSSE

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Image is a basic and fundamental data source for the digital image processing. This image data source is required to be processed into information or intelligence and further to knowledge levels where it is required to understand and migrate into knowledge economy systems. Image registration is one of such key and most important process already identified in the digital image processing domain. Image registration is a process of bringing the reference image and sensed image into a common co-ordinate system, and application of complex transformation techniques for necessary comparison of reference with sensed images obtained from different -views, times, spaces, etc., in order to extract the valuable information and intelligence embedded in them. Due to the complexity of overall image registration process, it is difficult to suggest a single transformation technique even for a specific application. In addition, it is highly impossible to suggest one single transformation technique for comparison of various sensed images with a reference image during the image registration process. This research gap calls for the development of new image registration techniques for the application of more than one transformation technique during the image registration process for the necessary comparisons with reference image & sensed images, those are obtained from the available heterogeneous sources or sensors, based on the requirement. In addition, it is a basic need to attempt for the measurement of effectiveness of the image registration process also. Therefore, a research framework is developed for image registration process and attempted for the measurement of its effectiveness also. This new research area is a novel idea, and is expected to emerge as a provision for the knowledge computations with creative thinking through the embedded intelligence extraction during the complex image registration process.

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Image registration quality assessment with similarity measures - A research study

Cited by 9 publications

References 24 publications

Implementation of Machine Learning-Based Data Mining Techniques for IDS

Implementation of Machine Learning-Based Data Mining Techniques for IDS

A machine learning approach to image registration verification

Development of New Image Registration Techniques – A Research Framework

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