Automatic detection and count of apples using N-adic overlapped object separation technology

Yeon, Hanbyul; Yoo, Seong-Joon; Han, Dongil; Gu, Yeonghyeon; Lim, Jin-Hee

doi:10.2495/icie20130401

Future Information Engineering

2014

DOI: 10.2495/icie20130401

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Automatic detection and count of apples using N-adic overlapped object separation technology

Hanbyul Yeon

Seong-Joon Yoo

Dongil Han

et al.

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A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

Kim¹,

Choi²,

Choi³

et al. 2015

IEIE Transactions on Smart Processing and Computing

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Abstract:This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE L*a*b* color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.

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A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

Kim¹,

Choi²,

Choi³

et al. 2015

IEIE Transactions on Smart Processing and Computing

Self Cite

View full text Add to dashboard Cite

show abstract

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Automatic detection and count of apples using N-adic overlapped object separation technology

Cited by 1 publication

References 0 publications

A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

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