A novel segmentation algorithm for clustered slender-particles

Zhong, Qin; Zhou, Ping; Yao, Qingxing; Mao, Kejun

doi:10.1016/j.compag.2009.06.015

Cited by 45 publications

(34 citation statements)

References 11 publications

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“…Thus, the touching position is actually concave or nearly concave. According to this precondition, all the concavity points in the image can be detected using circular mask based method (Zhong et al, 2009). In order to make a self-contained section, this method is briefly explained below:…”

Section: Concavity Points Foundation and Detectionmentioning

confidence: 99%

“…Thus, four restrictive elements were developed (Zhong et al, 2009) to delete these redundant points. The connection lines of each two points from Fig.…”

Section: Optimization For Concavity Pointsmentioning

confidence: 99%

“…Based on the concavities information, we used the method proposed by Zhong (Zhong et al, 2009), the segmentation errors are shown in Fig. 1b.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed segmentation algorithm could accurately detect and effectively separate each of the touching citrus mass with a relatively fast speed. An algorithm based on watershed and concavities information to segment the clustered rice kernels was invented (Zhong et al, 2009). However, the two methods are inefficient to the flexional aquacultural products especially with large contact surfaces formed between every two flexional aquacultural products in the touching location.…”

Section: Introductionmentioning

confidence: 99%

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A novel segmentation algorithm for clustered flexional agricultural products based on image analysis

Liu

Fang

Zhang

2016

Computers and Electronics in Agriculture

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Section: Concavity Points Foundation and Detectionmentioning

confidence: 99%

“…Thus, four restrictive elements were developed (Zhong et al, 2009) to delete these redundant points. The connection lines of each two points from Fig.…”

Section: Optimization For Concavity Pointsmentioning

confidence: 99%

“…Based on the concavities information, we used the method proposed by Zhong (Zhong et al, 2009), the segmentation errors are shown in Fig. 1b.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel segmentation algorithm for clustered flexional agricultural products based on image analysis

Liu

Fang

Zhang

2016

Computers and Electronics in Agriculture

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“…Qufa Zhong [14] proposed a novel algorithm based on watershed and concavities to segment the clustered slender particles. The mask is used to determine the concaveness along the whole boundary of touching particles.…”

Section: Figure 1 Occluded Seeds and Concavity Pointsmentioning

confidence: 99%

Digital Separation of Occluded Seeds using Image Analysis

Chaugule¹,

Mali²

2015

IJCA

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The shape and size estimation done without separating the touching and overlapping seeds lead to inaccurate values of size and shape. The watershed segmentation and morphological processing suffers from problems like over segmentation and large processing times respectively. An algorithm based on concavities is developed and tested for segmentation of occluded paddy grains. The first step distinguished each seed in a binary image of a grain sample as either an isolated seed or a group of occluded seeds by using the shape properties. The next few steps separated individual seeds in binary images of occluded kernels. Split lines were drawn by the algorithm through the split points which were determined by evaluating the concavity of the corner points detected along the boundary, and selecting those points at which the concavity is highest. This approach is compared with morphological operations and watershed segmentation and the obtained results show that this method is effective in separating the touching seeds. And also after the separation the shape features extracted do not differ a lot from the actual shape features.

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Automated Counting of Sex-Pheromone Attracted Insects Using Trapped Images

Chen

et al. 2019

Computer and Computing Technologies in Agriculture XI

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In this paper, an automatic segmentation and counting method for insect monitoring in orchard was proposed. The method based on image processing consisted of: (1) touching insect detection, (2) local segmentation points search using boundary tracking and morphological thinning operation, recursively, (3) segmentation lines implementation using the shortest distance idea. Algorithm performance was evaluated in terms of segmentation ratio and segmentation accuracy. Compared with the watershed method, the proposed method had improvement in evaluation criteria. Its average segmentation ratio was 1.03 and average segmentation accuracy was 96.7%, respectively. The results demonstrate the proposed method is an alternative solution for insect monitoring in integrated pest management.

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A novel segmentation algorithm for clustered slender-particles

Cited by 45 publications

References 11 publications

A novel segmentation algorithm for clustered flexional agricultural products based on image analysis

A novel segmentation algorithm for clustered flexional agricultural products based on image analysis

Digital Separation of Occluded Seeds using Image Analysis

Automated Counting of Sex-Pheromone Attracted Insects Using Trapped Images

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