This paper corresponds to the solution of some problems realized during ragweed identification experiments, namely the samples collected on the field by botanical experts did not match the initial conditions expected. Reflections and shadows appeared on the image, which made the segmentation more difficult, therefore also the classification was not efficient in previous study. In this work, unlike those solutions, which try to remove the shadow by restoring the illumination of image parts, the focus is on separating leaf and background points based on chromatic information, basically by examining the histograms of the full image and the border. This proposed solution filters these noises in the subspaces of hue, saturation and value space and their combination. It also describes a qualitative technique to select the appropriate values from the filtered outputs. With this method, the results of segmentation improved a lot.
Szkordilisz F. Mitigation of urban heat island by green spaces, Pollack Periodica, Vol. 9, No. 1, 2014, pp. 91−100.
Schiffer A. , Sari Z., Müller P., Jancskar I., Varady G., Ercsey Zs. Ragweed detection based on SURF features, Technical Gazette, Vol. 24. No. 5, 2017, pp. 1519–1524.
Jancskar I. , Sari Z., Schiffer A., Várady G. Phase plane tuning of fuzzy controller for 1 DoF helicopter model, Pollack Periodica, Vol. 10, No. 2, 2015, pp. 3–15.
Storcz T. , Ercsey Zs. Ragweed recognition: statistical feature extraction and CNN classification, (in Hungarian) Pollack Press, 2015.
Hu M. K. Visual pattern recognition by moment invariants, IRE Transactions on Information Theory, Vol. 8, No. 2, 1962, pp. 179−187.
Sabhara R. K. , Lee C. P., Lim K. M. Comparative study of Hu and Zernike moments on object recognition, Smart Computing Review, Vol. 3, No. 3. 2013, pp. 172−177.
Mitchell T. M. Machine learning, McGraw-Hill, 1997.
Wu S. G. , Bao F. S., Xu E. Y., Wang Y. X., Chang Y. F., Xiang Q. L. A leaf recognition algorithm for plant classification using probabilistic neural network, 7th IEEE International Symposium on Signal Processing and Information Technology, Cairo, Egypt, 15-18 December 2007, pp. 11−16.
Tree Leaf Database MEW2010, Department of Image Processing, Institute of Information Theory and Automation, Academy of the Sciences of Czech Republic, http://zoi.utia.cas.cz/tree_leaves (last visited 27 December 2016).
Deb K. , Suny A. H. Shadow detection and removal based on YCrCb color space, Smart Computing Review, Vol. 4, No. 1, 2014, pp. 23−33.
Noor A. I., Mokhtar M. H. , Rafiqul Z. K., Pramod K. M. Understanding color models, A Review, ARPN Journal of Science and Technology, Vol. 2, No. 3 2012 pp. 256−275.
Fredembach C. , Finlayson G. Simple shadow removal, Proc. of the 18th International Conference on Pattern Recognition, Hong-Kong, China, 20-24 August 2006, Vol. 1, pp. 832–835.
Xu L. , Qi F., Jiang R. Shadow removal from a single image, Proc. of 6th International Conference on Intelligent System Design and Applications, Washington DC, USA, 16-18 October 2006, Vol. 2. pp. 1049−1054.
Sharma P. , Sharma R. Shadow detection and its removal from images using strong edge detection method, Journal of Electronics and Communication Engineering, Vol. 10, No. 4, Ver. II, 2015. pp. 72−75.
Barnard K. , Finlayson G. D. Shadow identification using color ratios, Proc. of 8th Color Imaging Conference, Scottsdale, Arizona, USA, 7-10 November 2000, pp. 97−101.
Levine M. D. , Bhattacharyya J. Removing shadows, Pattern Recognition Letters, Vol. 26, No. 3, 2005. pp. 251−265.
Khan S. H. , Bennamoun M., Sohel F., Togneri R. Automatic feature learning for robust shadow detection, Proc. of IEEE Conference on Computer Vision & Pattern Recognition, Columbus, Ohio, USA, 23-28 June 2014, pp. 4321−4328.
Rashmi V. , Srinivas R. V., Srinivas K. Comparative analysis of shadow detection and removal methods on an image, Special Issue International Journal of Computer Science and Information Security, Vol. 14, 2016, pp. 152−156.
Song S. , Huang B., Zhang K. Shadow detection and reconstruction in high-resolution satellite images via morphological filtering and example-based learning, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 5, 2014, pp. 2545−2554.
Salih N. M. , Kadhim M., Mourshed M., Bray M. T. Shadow detection from very high resolution satellite image using grab-cut segmentation and ratio-band algorithms, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL, No. 3/W2, 2015, pp. 95−101.
Jancskar I. IR-image based inverse radiative heat transfer problem, Pollack Periodica, Vol. 8, No. 1, 2013, pp. 75−87.
Chondagar V. , Pnadya H., Pnachal M., Patel R., Sevak D., Jani K. A review: shadow detection and removal, International Journal of Computer Science and Information Technologies, Vol. 6, No. 6, 2015, pp. 5536−5541.
Digarse D. , Chauhan K. Shadow detection by local color constancy, International Journal of Computer Applications, Vol. 124, No. 14, 2015, pp. 36−41.