Abstract
In this paper, a novel method to recognize defect regions of apples based on Gabor wavelet transformation and SVM using machine vision is proposed. The method starts with background removal and object segmentation by threshold. Texture features are extracted from each segmented object by using Gabor wavelet transform, and these features are introduced to support vector machines (SVM) classifiers. Experimental results exhibit correctly recognized 85% of the defect regions of apples.
| Original language | English |
|---|---|
| Title of host publication | Computer and Computing Technologies in Agriculture V - 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Proceedings |
| Pages | 343-350 |
| Number of pages | 8 |
| Edition | PART 3 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 5th International Conference on Computer and Computing Technologies in Agriculture, CCTA 2011 - Beijing, China Duration: 29 Oct 2011 → 31 Oct 2011 |
Publication series
| Name | IFIP Advances in Information and Communication Technology |
|---|---|
| Number | PART 3 |
| Volume | 370 AICT |
| ISSN (Print) | 1868-4238 |
Conference
| Conference | 5th International Conference on Computer and Computing Technologies in Agriculture, CCTA 2011 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 29/10/11 → 31/10/11 |
Keywords
- Gabor wavelet
- SVM
- apple quality grading
- defect identification
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Huang, W., Zhang, C., & Zhang, B. (2012). Identifying apple surface defects based on gabor features and SVM using machine vision. In Computer and Computing Technologies in Agriculture V - 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Proceedings (PART 3 ed., pp. 343-350). (IFIP Advances in Information and Communication Technology; Vol. 370 AICT, No. PART 3). https://doi.org/10.1007/978-3-642-27275-2_39