Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator

Wang, Zhibin; Wang, Kaiyi; Yang, Feng; Pan, Shouhui; Han, Yanyun

doi:10.1016/j.inpa.2017.09.005

Cited by 51 publications

(36 citation statements)

References 23 publications

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“…There are several researches about image segmentations with overlapping objects. Wang, et al proposed a segmentation method of overlapping plant leaves by using the Chan-Vese method and the Sobel operator [8]. This method gives good performance to separate objects in natural images.…”

Section: Related Workmentioning

confidence: 99%

“…The ME calculates the ratio between the number of misclassified pixels and the number of pixels in the image, according to Eq. (8). ME measurement only calculated on 24 test images that contain the overlapping teeth area.…”

Section: Error Measurement Of the Segmented Overlapping Areamentioning

confidence: 99%

“…This problem makes the interpretation process take a lot of time and require highly trained personnel to avoid high error rate [7]. In image segmentation, the overlapping objects could cause inaccurate segmentation result because there are parts of an object that shadow other objects, therefore produces a wrong contour [8].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

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Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

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Section: Related Workmentioning

confidence: 99%

Section: Error Measurement Of the Segmented Overlapping Areamentioning

confidence: 99%

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Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

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show abstract

“…Considering the still existing complexities of leaf segmentation, number of segmentation techniques needed to be combined. Z. Wang et al [5] presented an overlapping leaves image segmentation technique based on the Chan Vese model and Sobel operator. In [6], Cerutti et al retrieved the leaf contour of image from a complex natural background by applying a two-step active contour algorithm using polygonal leaf model.…”

Section: Introductionmentioning

confidence: 99%

“…But, there exists some still unsolved issues. Some models like [5], work with only one species of leaf which does not ensure the performance of model for leaves of different species found in different circumstance. Also, in real-life, randomly captured image can compromise the performance of some models [7,8] that usually works with high resolution image captured with powerful camera.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Single and Overlapping Leaves by Extracting Appropriate Contours

Khan¹,

Debnath²

2019

6th International Conference on Computer Science, Engineering and Information Technology (CSEIT-2019)

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Leaf detection and segmentation is a complex image segmentation problem as leaves are most often found in groups with natural background. Edges of leaves cannot be clearly defined from image because of their color similarities.Also,separating every single as well as overlapping leaf individually is even more challenging as leaves share almost same color, texture and shape. In this paper, we propose a new automatic approach for leaf segmentation from image. Our leaf segmentation process uses efficient techniques for processing an image to obtain contours of every individual objects. Then, it selects the best appropriate connected contours that represent region of every leaves appearing in an image. Our model archives an overall 90.46% segmentation rate where segmentation rates for single and overlapping leaves are 95. 34% and 86.73%, respectively.

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Application of the Watershed Segmentation Method in the Separation and Identification of Individual Leaves in Potato Crops

Carrillo

Fuentes

2020

Advances in Automation and Robotics Research

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Image segmentation of overlapping leaves based on Chan–Vese model and Sobel operator

Cited by 51 publications

References 23 publications

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Segmentation of Single and Overlapping Leaves by Extracting Appropriate Contours

Application of the Watershed Segmentation Method in the Separation and Identification of Individual Leaves in Potato Crops

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