Noise Estimation by Utilizing Mean Deviation of Smooth Region in Noisy Image

Sari, Suhaila; Roslan, Hazli; Shimamura, Tetsuya

doi:10.1109/cimsim.2012.89

Cited by 18 publications

(12 citation statements)

References 16 publications

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“…Noise information is collected by estimating the noise properties from a specified smooth region. One such method is the mean absolute deviation technique (MADT) [13]. Assuming the noisy 1-D signal is represented by f with n samples and its mean by µ f , then the mean deviation (MD) is presented as…”

Section: Noise Removal With Audacitymentioning

confidence: 99%

An automated snick detection and classification scheme as a cricket decisionreview system

Khan¹,

Hussain²,

Waleed³

et al. 2019

Turk J Elec Eng & Comp Sci

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Umpire decisions can greatly affect the outcome of a cricket game. When there is doubt about the umpire's call for a decision, a decision review system (DRS) may be brought into play by a batsman or bowler to validate the decision. Recently, the latest technologies, including Hotspot, Hawk-eye, and Snickometer, have been employed when there is doubt among the on-field umpire, batsman, or bowlers. This research is a step forward in gaging the true class of a snick generated from the contact of the cricket ball with either (i) the bat, (ii) gloves, (iii) pad, or (iv) a combination of bat and pad. Preprocessing included noise removal from the snick audios using the Audacity program.Machine learning-based classification is achieved by training neural networks with audio features of the snick. A support vector machine was employed to enhance the classification system. Twenty-one features comprising time and frequency domain characteristics were compiled. After one-way analysis of variance was employed and multicomparison analysis was performed, a set of seventeen features was selected and utilized to increase the accuracy of the proposed DRS.The system was trained on indigenous snick data gathered by ourselves and then tested for real cricket snick scenarios.The system achieves a classification rate of 98.3 percent for the self-collected data while presenting an accuracy of 85.7 percentage for the real cricket snick scenarios. The research also developed a dataset of snick audios comprising 132 signals for the four categories to aid researchers working in the same field.

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Section: Noise Removal With Audacitymentioning

confidence: 99%

An automated snick detection and classification scheme as a cricket decisionreview system

Khan¹,

Hussain²,

Waleed³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…The median absolute differences (MAD) may be used instead of the average of absolute differences so that the effect of the noises is reduced on the dissimilarity measure. Although, salt and pepper noise has a considerable effect on the Manhattan measure, it has minimal effect on MAD [36]. MAD is mathematically defined as finding out the differences between the absolute intensities of the corresponding pixels of two images and then taking the median of the orderly data as the dissimilarity measure.…”

Section: Median Absolute Differencementioning

confidence: 99%

FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels

Mukhopadhaya

Kumar

Stein³

2018

Remote Sensing

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In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m*) and also at different α-cuts. The two best single measures obtained were combined to study the effect of composite measures on the datasets used. An image-to-image accuracy check was conducted to assess the accuracy of the classified images. Fuzzy error matrix (FERM) was applied to measure the accuracy assessment outcomes for a Landsat-8 dataset with respect to the Formosat-2 dataset. To conclude, FCM classifier with Cosine measure performed better than the conventional Euclidean measure. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.

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“…The median absolute differences (MAD) may be used instead of the average of absolute differences so that the effect of the noises is reduced on the dissimilarity measure. Although, salt and pepper noise has a considerable effect on the Manhattan measure, but it has minimal effect on MAD [36]. MAD is mathematically defined as finding out the differences between the absolute intensities of the corresponding pixels of two images and then taking the median of the orderly data as the dissimilarity measure.…”

Section: Median Absolute Differencementioning

confidence: 99%

An Effective FCM Approach of Similarity and Dissimilarity Measures with Alpha-Cut

Mukhopadhaya¹,

Kumar²,

Stein³

2018

Preprint

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In this study, the fuzzy c- means classifier has been studied with nine other similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray-Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m) and also at different α-cuts. The two best single norms obtained were combined to study the effect of composite norms on the datasets used. An image to image accuracy check was conducted to assess the accuracy of the classified images. Fuzzy Error Matrix (FERM) was applied to measure the accuracy assessment outcomes for a Landsat-8 dataset with respect to the Formosat-2 dataset. To conclude FCM classifier with Cosine norm performed better than the conventional Euclidean norm. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.

show abstract

Noise Estimation by Utilizing Mean Deviation of Smooth Region in Noisy Image

Cited by 18 publications

References 16 publications

An automated snick detection and classification scheme as a cricket decisionreview system

An automated snick detection and classification scheme as a cricket decisionreview system

FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels

An Effective FCM Approach of Similarity and Dissimilarity Measures with Alpha-Cut

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