SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Sadek, Rowayda A.

doi:10.48550/arxiv.1211.7102

Cited by 10 publications

(9 citation statements)

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“…These results are statistically significant in the p < 0.05 range using Mann-Whitney tests. Al- To explain SVD's good performance in the online study, we first note that SVD is considered as a leading algorithm in the domain of recommendation systems (Sadek 2012). Second, in our setting, SVD tended to generate recommendations that participants had not heard about before which seemed to resonate with many participants.…”

Section: Online Studymentioning

confidence: 88%

Intelligent Recommendations for Citizen Science

Zaken

Gal

Shani

et al. 2021

AAAI

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Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users' interactions in the SciStarter platform - a leading citizen science site -as well as their contributions to different projects. The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using non-personalized recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.

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Section: Online Studymentioning

confidence: 88%

Intelligent Recommendations for Citizen Science

Zaken

Gal

Shani

et al. 2021

AAAI

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“…In image processing techniques, SVD is an attractive algebraic transformation used to manipulate an image in two distinctive sub-spaces, i.e., data spaces and noise spaces. [26] If we treat the CSI profile as a matrix H of dimension t×N subcarriers (denoted as d), after applying SVD, H is factorized into three matrices, U, S and V , where U is a t×t orthogonal matrix, V is a d×d orthogonal matrix, and S is a t×d matrix with the diagonal elements representing the singular values of H.…”

Section: Location Dependency Removal Methodsmentioning

confidence: 99%

WiFi action recognition via vision-based methods

Chang

Lee

Lin

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recently, Wi-Fi has caught tremendous attention for its ubiquity. Motivated by Wi-Fi's low cost and privacy preservation, researchers have been putting lots of investigation into its potential on action recognition and even person identification. In this paper, for bringing a new modality for multimedia community, we offer an comprehensive overview on these two topics in Wi-Fi. Also, through looking at these two topics from an unprecedented perspective, we could achieve generality instead of designing specific ad-hoc features for each scenario. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on computer vision methods. To minimize the effect of location dependency embedded in CSI, we propose a novel de-noising method based on Singular Value Decomposition (SVD) to eliminate the background energy and effectively extract the channel information of signals reflected by human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed methods. Also, we conclude the factors that would affect the performance and highlight a few potential issues that require further deliberations.

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“…In other words, an image's contrast expresses the feature of the image. The singular values of the image have different features on the color channels, and these are variously used in the image processing area [23][24][25][26][27][28]. Li et al denoised the image using SVD [28].…”

Section: Efficient Color Correctionmentioning

confidence: 99%

Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

Lee¹

2022

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A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

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SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Cited by 10 publications

References 0 publications

Intelligent Recommendations for Citizen Science

Intelligent Recommendations for Citizen Science

WiFi action recognition via vision-based methods

Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

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