Global vision object detection using an improved Gaussian Mixture model based on contour
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 p...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1812.pdf |
| _version_ | 1827378023217758208 |
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| author | Lei Sun |
| author_facet | Lei Sun |
| author_sort | Lei Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T12:49:28Z |
| format | Article |
| id | doaj.art-f30fe732f2ee4bbdac8f2cc87f67de37 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-08T12:49:28Z |
| publishDate | 2024-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-f30fe732f2ee4bbdac8f2cc87f67de372024-01-20T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922024-01-0110e181210.7717/peerj-cs.1812Global vision object detection using an improved Gaussian Mixture model based on contourLei SunObject 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.https://peerj.com/articles/cs-1812.pdfObject detectionImproved gaussian mixture modelOtsu methodFeatures fusion |
| spellingShingle | Lei Sun Global vision object detection using an improved Gaussian Mixture model based on contour PeerJ Computer Science Object detection Improved gaussian mixture model Otsu method Features fusion |
| title | Global vision object detection using an improved Gaussian Mixture model based on contour |
| title_full | Global vision object detection using an improved Gaussian Mixture model based on contour |
| title_fullStr | Global vision object detection using an improved Gaussian Mixture model based on contour |
| title_full_unstemmed | Global vision object detection using an improved Gaussian Mixture model based on contour |
| title_short | Global vision object detection using an improved Gaussian Mixture model based on contour |
| title_sort | global vision object detection using an improved gaussian mixture model based on contour |
| topic | Object detection Improved gaussian mixture model Otsu method Features fusion |
| url | https://peerj.com/articles/cs-1812.pdf |
| work_keys_str_mv | AT leisun globalvisionobjectdetectionusinganimprovedgaussianmixturemodelbasedoncontour |