A novel image enhancement technique for tunnel leakage image detection

Dai, Chunquan; Lv, Yunlong; Long, Yanxia; Sui, Hongtao

doi:10.3166/ts.35.209-222

Cited by 8 publications

(8 citation statements)

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“…Moreover, the proposed method requires minimal computation time than the method proposed by Dong and Li [64]. As the method [64,65] has been developed based on the deep convolutional neural networks, it could be involved in a good number iterations so that it consumes more time for computation. Figure 9.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

Kalakoti¹,

Prabakaran²

2020

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This paper presents a method, which is developed based on the Discrete Cosine (DC) coefficient and multivariate parametric statistical tests, such as tests for equality of mean vectors and the covariance matrices. Background scenes and forefront objects are separated from the key-frame, and the salient features, such as colour and Gabor texture, are extracted from the background and forefront components. The extracted features are formulated as a feature vector. The feature vector is compared to that of the feature vector database, based on the statistical tests. First, the feature vectors are compared with respect to covariance. If the feature vector of the key-frame and the feature vector of the feature vector database pass the test, then the test for equality of mean vector is performed; otherwise, the testing process is stopped. If the feature vectors pass both tests, then it is inferred that the query key-frame represents the target video in the video database. Otherwise, it is concluded that the query key-frame not representing the video; and the proposed system takes the next feature vector for matching. The proposed method results in an average retrieval rate of 97.232%, 96.540%, and 96.641% for CC_WEB, UCF101, and our newly constructed database, respectively. Further, the mAP scores computed for each video datasets, which resulted in 0.807, 0.812, and 0.814 for CC_WEB, UCF101, and our newly constructed database, respectively. The output results obtained by the proposed method are comparable to the existing methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

Kalakoti¹,

Prabakaran²

2020

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“…Qualitative performance comparison of the proposed HGHS technique with the recent BCCHS technique [20] is also carried out on images from NASA database [30] and the enhanced images are also shown in Figure 11 and Figure 12. From the images shown in Figure 11 (2,16,17,19) it is observed that the images enhanced by BCCHS technique using brightness parameter (PB) and contrast parameter (PC) ended up with brightness degradation and annoying artifacts. Whereas the same images enhanced using the proposed HGHS technique are found to be better enhanced without such defects.…”

Section: Qualitative Assessmentmentioning

confidence: 99%

“…Image enhancement is a basic key practice that increases the image quality by modifying certain characteristics of the image such as contrast, brightness, etc. to transform the image more suitable than the original for the intended application [1,2]. Contrast Enhancement (CE) techniques by histogram processing are classified into two categories, namely, Histogram Equalization (HE) and Histogram Specification (HS).…”

Section: Introductionmentioning

confidence: 99%

Histogram Shape Based Gaussian Histogram Specification for Contrast Enhancement

Jayasankari¹,

Domnic²

2019

RIA

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Contrast enhancement a critical component in image processing is a vital integral part of computer vision in all fields of engineering including surveillance, medical, agricultural, aerospace, electrical, mechanical, etc. Although the existing contrast enhancement methods achieved satisfactory enhancement, they can produce annoying side effects due to variation of intensity levels. In this article, a new model for contrast enhancement that makes use of the given image's histogram shape to capture the variation in the intensity distribution to avoid annoying side effects is anticipated. In the proposed Histogram shape based Gaussian Histogram Specification technique, the desired histogram is obtained by dynamically controlling the parameters, mean and standard deviation. Using the images taken from standard and NASA database along with quality metrics such as contrast, entropy and gradient, the proposed Gaussian Histogram Specification technique performed better than that of the existing contrast enhancement techniques.

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“…The negative impacts on water environment can be determined through the judgement of water environment damages caused by human activities and their impacts (Dai and Lv, 2018). In this paper, the analytical hierarchy process (AHP) is employed to scientifically assess water environment problems induced by tunnel construction (Dai and Zhao, 2018).…”

Section: Analysis On the Negative Impacts On Water Environmentmentioning

confidence: 99%

Analysis and Prevention of the Impacts of Metro Construction on Groundwater Environment

Dai¹

2019

Appl. Ecol. Env. Res.

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Despite its positive impacts on traffic convenience and resource delivery, metro construction tilts the balance of groundwater resources and destabilizes the groundwater environment. This paper explores the impacts of shield tunnel construction on groundwater environment from the design and construction to an operating metro project, and verifies these impacts through an analysis on the tunnel project in Jinan Metro Line R3. The research shows that the grouting in shield tunneling has a local influence on the groundwater quality and pH, which attenuates with the increase of distance to the tunnel axis; the scope of influence is constrained by the formation lithology. Then, the backpropagation (BP) neural network was adopted to predict the ejection volume of groundwater, and the main influencing factors of water environment were disclosed by analytical hierarchy process (AHP). Finally, the author discussed the key techniques and prevention systems for prevention of the impacts of shield tunnel construction on water environment.

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A novel image enhancement technique for tunnel leakage image detection

Cited by 8 publications

References 0 publications

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

Histogram Shape Based Gaussian Histogram Specification for Contrast Enhancement

Analysis and Prevention of the Impacts of Metro Construction on Groundwater Environment

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