Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Automatic detection of individual and touching moths from trap images by combining contour-based and region-based segmentation

Insect detection is one of the most challenging problems of biometric image processing. This study focuses on developing a method to detect both individual insects and touching insects from trap images in extreme conditions. This method is able to combine recent approaches on contour-based and region-based segmentation. More precisely, the two contributions are: an adaptive k-means clustering approach by using the contour's convex hull and a new region merging algorithm. Quantitative evaluations show that the proposed method can detect insects with higher accuracy than that of the most used approaches.

References

    1. 1)
      • 17. Li, X.L., Huang, S.G., Zhou, M.Q., et al: ‘KNN-spectral regression LDA for insect recognition’. Int. Conf. on Information Science and Engineering, 2009, pp. 13151318.
    2. 2)
      • 13. Wang, J., Lin, C., Ji, L., et al: ‘A new automatic identification system of insect images at the order level’, Knowl.-Based Syst., 2012, 33, pp. 102110.
    3. 3)
      • 2. Deriche, R.: ‘Using Canny's criteria to derive a recursively implemented optimal edge detector’, Int. J. Comput. Vis., 1987, 1, (2), pp. 167187.
    4. 4)
      • 30. Ramesh, R., Kulkarni, A.C., Prasad, N.R., et al: ‘Face recognition using snakes algorithm and skin detection based face localization’. Int. Conf. on Signal, Networks, Computing, and Systems, 2016, pp. 6171.
    5. 5)
      • 21. Achanta, R., Shaji, A., Smith, K., et al: ‘SLIC superpixels compared to state-of-the-art superpixel methods’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 22742282.
    6. 6)
      • 19. Ding, W., Taylor, G.W.: ‘Automatic moth detection from trap images for pest management’, Comput. Electron. Agric., 2016, 123, (C), pp. 1728.
    7. 7)
      • 26. Prakash, J., Vijayakumar, V.: ‘A new texture based segmentation method to extract object from background’, Glob. J. Comput. Sci. Technol. Graph. Vis., 2012, 12, (15), pp. 4753.
    8. 8)
      • 1. Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, (6), pp. 679698.
    9. 9)
      • 39. Stehman, S.V.: ‘Selecting and interpreting measures of thematic classification accuracy’, Remote Sens. Environ., 1997, 62, (1), pp. 7789.
    10. 10)
      • 35. Tibshirani, R., Walther, G., Hastie, T.: ‘Estimating the number of clusters in a data set via the gap statistic’, J. R. Stat. Soc., Ser. B (Stat. Methodol.), 2001, 63, (2), pp. 411423.
    11. 11)
      • 4. Wagstaff, K., Cardie, C., Rogers, S., et al: ‘Constrained k-means clustering with background knowledge’. Int. Conf. on Machine Learning, 2001, pp. 577584.
    12. 12)
      • 24. Luo, T.-H.: ‘Investigation on image threshold segmentation method of pests in stored grain’, J. Wuhan Polytech. Univ., 2006, 25, (1), pp. 58.
    13. 13)
      • 23. Zhang, H.T., Hu, Y.D., Qiu, D.Y.: ‘The stored-grain pest image segmentation algorithm based on the relative entropy threshold’, J. North China Inst. Water Conservancy Hydroelectr. Power, 2003, 24, (3), pp. 2729.
    14. 14)
      • 16. Kaya, Y., Kayci, L.: ‘Application of artificial neural network for automatic detection of butterfly species using color and texture features’, Vis. Comput., 2014, 30, (1), pp. 7179.
    15. 15)
      • 7. Deschamps, T., Cohen, L.: ‘Fast extraction of minimal paths in 3D images and applications to virtual endoscopy’, Med. Image Anal., 2001, 5, (4), pp. 281299.
    16. 16)
      • 25. Xinwen, Y., Zuorui, S.: ‘Segmentation technology for digital image of insects’, Trans. Chin. Soc. Agric. Eng., 2001, 17, (3), pp. 137141.
    17. 17)
      • 9. Schurischuster, S., Zambanini, S., Kampel, M., et al: ‘Sensor study for monitoring varroa mites on honey bees (apis mellifera)’. Visual Observation and Analysis of Vertebrate and Insect Behavior Workshop, 2016.
    18. 18)
      • 8. Chen, X.M., Geng, G.H., Zhou, M.Q., et al: ‘Applying expectation-maximization in insect image segmentation using multi-features’, Comput. Appl. Softw., 2009, 26, (2), pp. 2022.
    19. 19)
      • 33. Mele, K.: ‘Insect soup challenge: segmentation, counting, and simple classification’. IEEE Int. Conf. on Computer Vision Workshops, 2013, pp. 168171.
    20. 20)
      • 20. Vincent, L., Soille, P.: ‘Watersheds in digital spaces: an efficient algorithm based on immersion simulations’, IEEE Trans. Pattern Anal. Mach. Intell., 1991, 13, (6), pp. 583598.
    21. 21)
      • 38. Wang, Y., Weng, G.: ‘The monitoring population density of pests based on edge-enhancing diffusion filtering and image processing’. Int. Conf. on Computer and Computing Technologies in Agriculture, 2007, pp. 899907.
    22. 22)
      • 11. Wen, C., Wu, D., Hu, H., et al: ‘Pose estimation-dependent identification method for field moth images using deep learning architecture’, Biosyst. Eng., 2015, 136, pp. 117128.
    23. 23)
      • 18. Wen, C., Guyer, D.: ‘Image-based orchard insect automated identification and classification method’, Comput. Electron. Agric., 2012, 89, pp. 110115.
    24. 24)
      • 14. Wen, C., Guyer, D.E., Li, W.: ‘Local feature-based identification and classification for orchard insects’, Biosyst. Eng., 2009, 104, (3), pp. 299307.
    25. 25)
      • 6. Lowe, D.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91110.
    26. 26)
      • 31. Wang, Y., Peng, Y.: ‘Application of watershed algorithm in image of food insects’, J. Shandong Univ. Sci. Technol., Nat. Sci., 2007, 26, (2), pp. 7982.
    27. 27)
      • 3. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321331.
    28. 28)
      • 37. Yao, Q., Liu, Q., Dietterich, T.G., et al: ‘Segmentation of touching insects based on optical flow and Ncuts’, Biosyst. Eng., 2013, 114, (2), pp. 6777.
    29. 29)
      • 32. Zhang, W.F., Guo, M.: ‘Stored grain insect image segmentation method based on graph cuts’, Sci. Technol. Eng., 2010, 7, pp. 16611664.
    30. 30)
      • 27. Yuehua, C., Xiaoguang, H., Changli, Z.: ‘Algorithm for segmentation of insect pest images from wheat leaves based on machine vision’, Trans. Chin. Soc. Agric. Eng., 2007, 23, (12), pp. 187191.
    31. 31)
      • 22. Blasco, J., Gómez-Sanchís, J., Gutierrez, A., et al: ‘Automatic sex detection of individuals of ceratitis capitata by means of computer vision in a biofactory’, Pest Manage. Sci., 2009, 65, (1), pp. 99104.
    32. 32)
      • 36. Meyer, F.: ‘Color image segmentation’. IET Int. Conf. on Image Processing and its Applications, 1992, pp. 303306.
    33. 33)
      • 12. Larios, N., Soran, B., Shapiro, L.G., et al: ‘Haar random forest features and SVM spatial matching kernel for stonefly species identification’. IEEE Int. Conf. on Pattern Recognition, 2010, pp. 26242627.
    34. 34)
      • 34. Yalcin, H.: ‘Vision based automatic inspection of insects in pheromone traps’. IEEE Int. Conf. on Agro-Geoinformatics, 2015, pp. 333338.
    35. 35)
      • 10. Rashwan, H.A., Chambon, S., Gurdjos, P., et al: ‘Towards multi-scale feature detection repeatable over intensity and depth images’. IEEE Int. Conf. on Image Processing, 2016, pp. 3640.
    36. 36)
      • 29. Yogamangalam, R., Karthikeyan, B.: ‘Segmentation techniques comparison in image processing’, Int. J. Eng. Technol., 2013, 5, (1), pp. 307313.
    37. 37)
      • 15. Fedor, P., Malenovský, I., Vanhara, J., et al: ‘Thrips (thysanoptera) identification using artificial neural networks’, Bull. Entomol. Res., 2008, 98, (5), pp. 437447.
    38. 38)
      • 28. Zhao, J., Liu, M., Yao, M.: ‘Study on image recognition of insect pest of sugarcane cotton aphis based on rough set and fuzzy c-means clustering’. IEEE Int. Symp. on Intelligent Information Technology Application, 2009, vol. 2, pp. 553555.
    39. 39)
      • 5. Comaniciu, D., Meer, P.: ‘Mean shift: a robust approach toward feature space analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (5), pp. 603619.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0086
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0086
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address