Segmentation Algorithm for Apple Recognition using Image Features and Artificial Neural Network

Yajing, 张亚静 Zhang; Minzan, 李民赞 Li; Jun, 乔军 Qiao; Gang, 刘刚 Liu

doi:10.3788/aos20082811.2104

Cited by 11 publications

(4 citation statements)

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“…algorithm (Zhang et al, 2008) and support vector machine (SVM) algorithm (Wang et al, 2009), which not only increase the identification of parameters but also increase the time consumption to process these images.…”

Section: Resultsmentioning

confidence: 99%

“…For the identification of apples, many in-depth studies have been performed (Zhang et al, 2008;Si et al, 2009;Tu et al, 2010;Si et al, 2015), however, which merely pursue the identification numbers including all the apples in the machine vision, while those studies do not consider the fact that apple-picking robots pick one fruit at a time, and overlook the fact that background targets not only can cause difficulties for foreground target extraction but also seriously affect the efficiency of foreground apple's localization for picking robots. Meanwhile, some studies introduce the neural network Because H could make the apple target more striking, it becomes the qualitative similarity condition to control the seed image growth in the HSV color space.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al (2008) used a neural network for apple image segmentation by selecting the color feature (R/B value) and texture features as input nodes. Wang et al (2009) extracted the color features of apple color images and used the support vector machine method to identify apple targets.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Shang

Shao

et al. 2015

Span. j. agric. res.

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Accurate segmentation of apple targets is one of the most important problems to be solved in the vision system of apple picking robots. This work aimed to solve the difficulties that background targets often bring to foreground targets segmentation, by fusing the visual attention mechanism and the growth rule of seed points. Background targets could be eliminated by extracting the ROI (region of interest) of apple targets; the ROI was roughly segmented on the HSV color space, and then each of the pixels was used as a seed growing point. The growth rule of the seed points was adopted to obtain the whole area of apple targets from seed growing points. The proposed method was tested with 20 images captured in a natural scene, including 54 foreground apple targets and approximately 84 background apple targets. Experimental results showed that the proposed method can remove background targets and focus on foreground targets, while the k-means algorithm and the chromatic aberration algorithm cannot. Additionally, its average segmentation error rate was 13.23%, which is 2.71% higher than that of the k-means algorithm and 2.95% lower than that of the chromatic aberration algorithm. In conclusion, the proposed method contributes to the vision system of apple-picking robots to locate foreground apple targets quickly and accurately under a natural scene.Additional key words:picking robots; Malus domestica; ROI; Itti model.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Shang

Shao

et al. 2015

Span. j. agric. res.

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show abstract

“…Qiu Daoyin uses BP neural network technology to distinguish pests in stored grains with a recognition rate of 100% [7]. Zhang Yading proposes an apple image segmentation algorithm based on image features and neural network sorter with a precision rate over 87.6% [8]. Qi Long, etc.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

Song

2010

AMM

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Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

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A Segmentation Method Based on SE Attention and U-Net for Apple Image

Gao,

He,

Zhai

et al. 2023

Lecture Notes in Computer Science

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Segmentation Algorithm for Apple Recognition using Image Features and Artificial Neural Network

Cited by 11 publications

References 0 publications

Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Segmentation of foreground apple targets by fusing visual attention mechanism and growth rules of seed points

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

A Segmentation Method Based on SE Attention and U-Net for Apple Image

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