Browsing by Author "Lee, Won Suk"
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Item Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions(Elsevier, 2011-09) Lee, Won Suk; Kurtulmuş, Ferhat; Vardar, Ali; Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; AAH-5008-2021; R-8053-2016; 15848202900; 15049958800A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. A total of 96 images were acquired in October 2010 from an experimental citrus grove in the University of Florida, Gainesville, Florida. Thirty-two of the total 96 images were selected randomly and used for training the algorithm, and 64 images were used for validation. Color, circular Gabor texture analysis and a novel 'eigenfruit' approach (inspired by the 'eigenface' face detection and recognition method) were used for green citrus detection. A shifting sub-window at three different scales was used to scan the entire image for finding the green fruits. Each sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Majority voting was performed to determine the results of the sub-window classifiers. Blob analysis was performed to merge multiple detections for the same fruit. For the validation set, 75.3% of the actual fruits were successfully detected using the proposed algorithm.Item Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network(Springer, 2014-02) Lee, Won Suk; Kurtulmuş, Ferhat; Vardar, Ali; Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; 0000-0001-6349-9687; R-8053-2016; AAH-5008-2021; 15848202900; 15049958800Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors' knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and 'eigenfruit' approach (inspired by the 'eigenface' face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.