Detection and segmentation of underwater CW-like signals in spectrum image under strong noise background

Xiao, Zhaolin; Zhang, Meng; Chen, Lisheng; Jin, Haiyan

doi:10.1016/j.jvcir.2019.02.036

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…This information is however of limited resolution due to the fixed size of the sliding window. The STFT is applied to underwater continuous-wave (CW)-like signals to acquire the feature vectors, which is energy intensity to be used as the sample space binary clustering of Gaussian mixture model [60]. In [25], the STFT was used to create whistle contour in a denoised spectrogram of whistles of long-finned pilot whales and killer whales.…”

Section: A Short Time Fourier Transform (Stft)mentioning

confidence: 99%

“…The Expectation Maximization (EM) algorithm [98], [99] is used to obtain the best Gaussian mixture parameters for a given set of feature vectors. The EM algorithm is an iterative algorithm which is based on maximizing the log-likelihood of the training data with respect to some parameters such as the means, covariances of the matrix and the mixture coefficient [60]. The maximization is attained when the algorithm converges after iterative update of Θ where Θ = (µ i , i , k i ) [100].…”

Section: ) Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

“…Detection and segmentation of continuous-wave (CW) underwater signals in the presence of strong background noise was carried out using a spectral feature analysis centered on soft clustering of GMM in [60]. The nature of the targeted signals are continuous, broad spectrum and the frequency is close to the frequency of background noise.…”

Section: ) Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

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Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

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Section: A Short Time Fourier Transform (Stft)mentioning

confidence: 99%

Section: ) Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

Section: ) Gaussian Mixture Models (Gmm)mentioning

confidence: 99%

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Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

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“…In recent years, the development and utilization of the ocean have gradually become an important development direction. Since underwater vision research is the basis of marinerelated disciplines, the rapid development of underwater image processing technology is inevitable [1,2]. Image segmentation is a basic method of target extraction, which aims to partition an image into several constituent regions.…”

Section: Introductionmentioning

confidence: 99%

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Feng

2022

Applied Sciences

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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“…In recent years, the development and utilization of the ocean have gradually become an important development direction. Since underwater vision research is the basis of marine-related disciplines, the rapid development of underwater image processing technology is inevitable [1][2] . Image segmentation is a basic method of target extraction, which aims to partition an image into several meaningful and constituent regions and each region has coherent features such as intensities, colors, and textures [3] .…”

Section: Introductionmentioning

confidence: 99%

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Chen

et al. 2021

Preprint

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is of great significance to target tracking and visual information mining. Aiming at the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information, and extract salient target contours by fusing Chan–Vese and local binary ﬁtting models. Then, the original underwater images are used to supplement the missing contour information by using the local image ﬁtting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours.

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Detection and segmentation of underwater CW-like signals in spectrum image under strong noise background

Cited by 4 publications

References 14 publications

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

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