Segmentation algorithm of cucumber leaf disease image based on saliency detection

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Volumn 47, Issue 9, 2016, Pages 11-16

  Ren, Shougang ^a   Lu, Haifei ^a   Yuan, Peisen ^a   Xue, Wei ^a   Xu, Huanliang ^a,b  

^a NANJING AGRICULTURAL UNIVERSITY   (China)

^b The State Information of Agricultural Engineering Technology Center   (China)

Author keywords

Cucumber; Disease image; Image segmentation; Manifold ranking; Saliency detection

Indexed keywords

AUTOMATION; BINS; EDGE DETECTION; ERROR ANALYSIS; IMAGE PROCESSING; MATHEMATICAL MORPHOLOGY; PIXELS;

AUTOMATIC IDENTIFICATION SYSTEM; AVERAGE EXECUTION TIME; CUCUMBER; MANIFOLD RANKING; MORPHOLOGICAL OPERATIONS; SALIENCY DETECTION; SEGMENTATION ALGORITHMS; SUPERPIXEL SEGMENTATIONS;

IMAGE SEGMENTATION;

EID: 84988660002     PISSN: 10001298     EISSN: None     Source Type: Journal    
DOI: 10.6041/j.issn.1000-1298.2016.09.002     Document Type: Article

References (16)

1. MAO Hanping, ZHANG Yancheng, HU Bo. Segmentation of crop disease leaf images using fuzzy C-means clustering algorithm [J]. Transactions of the CASE, 2008, 24(9): 136-140. (in Chinese)
2. WANG Haiqing, JI Changying, GU Baoxing, et al. Cucumber image segmentation based on weighted connection coefficient pulse coupled neural network [J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(3):204-208. (in Chinese)
3. TIAN Jie, HAN Dong, HU Qiuxia, et al. Segmentation of wheat rust lesion image using PCA and Gaussian mix model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(7):267-271. (in Chinese)
4. ZHAO Yaochi, HU Zhuhua. Image segmentation of fruits with diseases in natural scenes based on logarithmic similarity constraint Otsu [J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(11):9-15. (in Chinese)
5. PAN C, PARK D S, YANG Y, et al. Leukocyte image segmentation by visual attention and extreme learning machine[J]. Neural Computing & Applications, 2011, 21(6):1217-1227.
6. LI J, LEVINE M D, AN X, et al. Visual saliency based on scale-space analysis in the frequency domain[J]. Pattern Analysis and Machine Intelligence, 2013, 35(4):996-1010.
7. ZHOU D, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[J]. Advances in Neural Information Processing Systems, 2004, 16(4):321-328.
8. FU K, GONG C, GU Y H, et al. Normalized cut-based saliency detection by adaptive multi-level region merging[J]. IEEE Transactions on Image Processing, 2015, 24(12):5671-5683.
9. DOKMANIC I, PARHIZKAR R, RANIERI J, et al. Euclidean distance matrices: a short walk through theory, algorithms and applications[J]. IEEE Signal Processing Magazine, 2015, 32(6):12-30.
10. LI H, WU W, WU E. Robust salient object detection and segmentation[M]. Germany: Springer International Publishing, 2015.
11. ZHOU J, REN Y, YAN Y, et al. Salient object detection: manifold-based similarity adaptation approach [J]. Journal of Electronic Imaging, 2014, 23(6):704-714.
12. WANG J, LU H, LI X, et al. Saliency detection via background and foreground seed selection[J]. Neurocomputing, 2015, 152:359-368.
13. ZHANG C, TAO Z, WEI X, et al. A flexible framework of adaptive method selection for image saliency detection[J]. Pattern Recognition Letters, 2015, 63:66-70.
14. YANG C, ZHANG L, LU H. Graph-regularized saliency detection with convex-hull-based center prior[J]. IEEE Signal Processing Letters, 2013, 20(7):637-640.
15. ZHANG Zhibin, LUO Xiwen, ZANG Ying, et al. Segmentation algorithm based on color feature for green crop plants[J]. Transactions of the CASE, 2011, 27(7):183-189. (in Chinese)
16. DENG Xiaozheng, JIAO Licheng, LU Shan. Spectral clustering ensemble applied to SAR image segmentation using nonegative matrix factorization [J]. Acta Electronica Sinica, 2011, 39(12): 2905-2909. (in Chinese)