A GMM-Based Segmentation Method for the Detection of Water Surface Floats

Jin, Xianli; Niu, Peiwen; Liu, Linfeng

doi:10.1109/access.2019.2937129

Cited by 16 publications

(9 citation statements)

References 23 publications

(25 reference statements)

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“…Jin et al [40] presented an Improved GMM-based automatic segmentation method (IGASM) by improving the for updating the background so that it can more effectively segment the floats on the sea surface. Following the mapping of the GMM's findings into an HSV colour space, a light-shadow classifier function is implemented to address the problems associated with shadows.…”

Section: ì Issn: 2252-8938mentioning

confidence: 99%

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Aris

Jamaian

2023

IJ-AI

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<p>Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.</p>

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Section: ì Issn: 2252-8938mentioning

confidence: 99%

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Aris

Jamaian

2023

IJ-AI

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“…But it does not improve the object detection performance of the algorithm itself. Jin, Niu & Liu (2019) proposed an improved GMM based on the automatic segmentation method to detect floating objects on water surfaces. In this method, a new strategy is used to update the background model in order to segment the objects floating on water surfaces more effectively.…”

Section: Introductionmentioning

confidence: 99%

Global vision object detection using an improved Gaussian Mixture model based on contour

Sun

2024

PeerJ Computer Science

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Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

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“…Thus, image segmentation is transformed into a parameter estimation. The statistical model-based method conveniently models the spatial information of pixels in a probabilistic way, greatly improving the accuracy of image segmentation [10,11]. Given the advantages of the statistical modelbased method, this segmentation approach has been used, analyzed, and improved in numerous studies [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing

et al. 2023

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.

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A GMM-Based Segmentation Method for the Detection of Water Surface Floats

Cited by 16 publications

References 23 publications

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Global vision object detection using an improved Gaussian Mixture model based on contour

Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing

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