| Summary: | 碩士 === 國立高雄應用科技大學 === 資訊工程系 === 102 === A local orientation descriptor (LOD) for nutrition analysis by quantity estimation is proposed. By observing nutrition properties, a texture-based LOD is designed to extract discriminant information, frequency and length among food items. Prior to classification, food detection is a challenging problem due to significant variety of backgrounds and containers. Thus, two food region detectors are designed in this study. In addition, nutrition quantity is estimated using coins as reference objects. Three types of features, normalized colour, density, and symmetry properties are extracted for coin classification. Experimental results show that the proposed LOD outperforms existing object recognition features.
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