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作者: Basavaraj Anami>
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摘要: Neovascular glaucoma (NVG) is a human eye disease due to diabetes that leads to permanent vision loss. Early detection and treatment of it prevent further vision loss. Hence the development of an automated system is more essential to help the ophthalmologist in detecting NVG at an earlier stage. In this paper, a novel approach is used for detection of Neovascular glaucoma using fractal geometry concepts. Fractal geometry is a branch of mathematics. It is useful in computing fractal features of i关键词: Glaucoma;Fractal Dimension (FD);Box Counting;Segmentation;Texture Features;Retina.
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摘要: This paper presents a method for classification of vegetables based on RGB colour and local binary pattern (LBP) texture features. The feature vector comprises of the combination of colour and texture features that contribute to the classification. Leafy and non-leafy vegetable images are deployed. In this work 18 varieties of vegetables are considered by choosing nine leafy and nine non-leafy vegetables. A multilayer neural network is used for the classification. The experimental results demons关键词: RGB colour features; LBP features; vegetable classification; neural networks; recognition systems; local binary pattern; vegetable identification; vegetable images; texture features.
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Research Article
摘要: This paper presents the suitability of feature extraction methods for the identification and classification of certain agriculture and horticulture crops. Primarily, agriculture/horticulture crops are recognized based on their shape, size, color, texture and the like. When crops exhibit different shapes and sizes, it is customary to choose the shape and size as the basic features. Certain crops are easily identified simply by color; for example, with crops like jowar, ground nut, pomegranate and关键词: color features;textural features;morphological features;recognition and classification
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Research Article
摘要: The paper presents an artificial neural network based approach to corroborate the effect of variations in illumination, distance of image acquisition and size on recognition and classification accuracies of bulk food grain image samples. Different food grains samples like Wheat, Groundnut, Green gram and Jowar are considered. The image samples are taken by varying acquisition distances, illumination and sizes. The natural light source and the minimum distance of 40 centimeters are ideal for imag关键词: feature extraction;food grain samples;neural networks;varying light source and illumination
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Research Article
摘要: In this paper, we have presented different methodologies devised for recognition and classification of images of agricultural/horticultural produce. A classifier based on BPNN is developed which uses the color, texture and morphological features to recognize and classify the different agricultural/horticultural produce. Even though these features have given different accuracies in isolation for varieties of food grains, mangoes and jasmine flowers, the combination of features proved to be very 关键词: colour features;textural features;bulk food grain recognition;bulk fruits recognition;agricultural/horticultural produce
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