Automatic segmentation of fruits in CIELuv color space image using hill climbing optimization and fuzzy C-Means clustering

Ganesan, P.; Sathish, B. S.; Sajiv, G

doi:10.1109/startup.2016.7583960

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Wang et al used a natural statistical visual attention model to remove the background and combined the information with the global probability of the Otsu algorithm to detect saliency contours, which addressed the problems of uneven and mutual occlusion of apple images in orchard environments [9]. Ganesan et al proposed a method that combines mountain climbing and the MFCM to segment fruits in RGB and CLELuv color spaces; this method solves the problem of the mountain climbing method falling into local optima [10]. Xavier Soria et al proposed an edge detector based on deep learning, which can be used for any edge detection task by combining holistically nested edge detection (HED) and Xception networks without pre-training or fine-tuning processes [11].…”

Section: Introductionmentioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

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Section: Introductionmentioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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“…So, the use of automated machinery has finally decreased the processing time and increased the quality of fruits/vegetables and products manufactured with this [3]- [5]. In spite of the fact that automatic machines are available to ease the issue, the problem is that they have a very extensive set up which raises the cost [7]- [10]. Hence my vision is to develop a system that is economical and efficient so that every single fruit/vegetable farmer can set up his own fruit/vegetable sorting and packaging facility and will TELKOMNIKA Telecommun Comput El Control  Customized sorting and packaging machine (Ashish Bhatnagar) 1327 avail himself of his own market, bringing self-reliability to farmers.…”

Section: Introductionmentioning

confidence: 99%

Customized sorting and packaging machine

Bhatnagar¹,

Patel²,

Gupta³

et al. 2021

TELKOMNIKA

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India is a country which has a cornerstone of agriculture. And as it comes to fruit/vegetable sorting and packaging in India, human labor has been a vital part. With manual hand picking, it is a very laborious task to classify the quality of fruits/vegetables and simultaneously pack them. One leading-edge technology for the fulfilment of this purpose is 'Image Processing' technology which is extremely fast and cost-efficient. Our whole idea revolves around the fact that each fruit will be inspected, sort and simultaneously packed. For the same, a low cost automated mechatronic system has designed consisting of a solitary mechanical arrangement, which is controlled and synchronized through electronic components. Fruits/vegetables are sorted as high-quality and low-quality on the basis of physical appearance and weight. For this, a suitable algorithm is designed using the Open CV library. And the sorting is done using Arduino Uno and Raspberry pi. Hence the aim is to develop a sorting and packaging facility that can be established at the very root level itself which will be economically compact and accurate and will give more justice to farmers.

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“…1 and 3 were selected from the histogram, whereas 2 can be calculated using an equation. Hill climbing optimization technique [28,29] was implemented to obtain the first and second intensity peaks which were labelled as 1 and 3 , respectively.…”

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confidence: 99%

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Tan¹,

Mustafa²,

Mashor³

et al. 2021

Preprint

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Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages: (1) color normalization, (2) segmentation and removal of nucleus cells, (3) SNIC, (4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in \(Acc\), \(F1\), \(AOM\), and \(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.

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Automatic segmentation of fruits in CIELuv color space image using hill climbing optimization and fuzzy C-Means clustering

Cited by 7 publications

References 8 publications

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Customized sorting and packaging machine

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

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