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研究生:林鏗為
研究生(外文):Ken-Wei Lin
論文名稱:研究與實作一基於影像之食物辨識系統
論文名稱(外文):A Research and Implementation of Image-based Food Recognition System
指導教授:陳洳瑾
指導教授(外文):Ju-Chin Chen
口試委員:陳洳瑾陳朝鈞張雲龍王鼎超
口試委員(外文):Ju-Chin ChenChao-Chun ChenWeng-Long ChangDing-Chau Wang
口試日期:2014-07-03
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:20
中文關鍵詞:食物辨識
相關次數:
  • 被引用被引用:0
  • 點閱點閱:1162
  • 評分評分:
  • 下載下載:87
  • 收藏至我的研究室書目清單書目收藏:1
本論文中我們提出一個飲食分析與飲食份量估計系統,透過觀察營養特性,提出一種基於紋理分析的特徵,擷取食物區塊的頻率與長度資訊。然而,在分類之前,食物偵測是一項具有挑戰性的問題,由於大量不同背景與容器,增加食物偵測困難度。因此,根據背景與前景紋理差異性,提出二個食物區域偵測器。並使用快速標記演算法,將偵測到的飲食區域進一步切割成多個飲食子區域;最後結合所提出的紋理特徵與顏色資訊,對每一飲食子區域進行類別辨識。此外,使用台灣流通的硬幣作為參照物,以估算每一飲食子區域大小,提供飲食份量評估。透過多個實驗證明所提出系統的可靠性。
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.
摘 要 i
ABSTRACT ii
目錄 iii
表目錄 iv
圖目錄 v
一.緒論 1
二.相關文獻探討 2
三.系統流程 4
四.系統介紹 6
4.1 飲食區域偵測 6
4.2 飲食區域切割 7
4.3 飲食區域特徵擷取 10
4.4 飲食份量估計 13
五.實驗結果與分析 15
5.1 資料庫內容介紹 15
5.2營養分析效能評估 16
5.3硬幣辨識效能評估 17
5.4整體系統效能評估 17
六. 結論 19
參考文獻 20

[1]C.K. Martin, H. Han, S.M. Colon, H.R. Allen, C.M. Champagne, and S.D. Anton, “A novel method to remotely measure food intake of free-living individuals in real time: The remote food photography method,” British Journal of Nutrition, vol. 101, pp. 446-456, 2009.
[2]N. Khanna, C.J. Boushey, D. Kerr, M. Okos, D.S. Ebert, and E.J. Delp, “An overview of the technology assisted dietary assessment project at Purdue University,” pp. 290-295, IEEE International Symposium on Multimedia, 2010.
[3]K. Aizawa, Y. Maruyama, H. Li, and C. Morikawa, “Food balance estimation by using personal dietary tendencies in a multimedia food log,” IEEE Transactions on Multimedia, vol. 15, no. 8, pp. 2176-2185, 2013.
[4]F. Zhu, M. Bosh, N. Khanna, C.J. Boushey, and E.J. Delp, “Multiple hypotheses image segmentation and classification with application to dietary assessment,” IEEE Journal of Biomedical and Health Informatics, 2014.
[5]A. Mariappan, M. Bosch, F. Zhu, C.J. Boushey, D.A. Kerr, D.S. Ebert, and E.J. Delp, “Personal dietary assessment using mobile devices,” IS&T/SPIE Electronic Imaging, vol. 7246, pp. 72460Z-72460Z-12, 2009.
[6]F. Zhu, M. Bosch, C.J. Boushey, and E.J. Delp, “An image analysis system for dietary assessment and evaluation,” IEEE Conference on Image Processing, pp. 1853-1856, 2010.
[7]N.K. Patil, V.S. Malemath, and R.M. Yadahalli, “Color and texture based identification and classification of food grains using different color models and haralick features,” International Journal of Computer Science and Engineering, vol. 3, no. 12, pp. 3669-3680, 2011.
[8]M.M. Zhang, “Identifying the cuisine of a plate of food,” Technical report, UC San Diego, 2011.
[9]M.Y. Chen, Y.H. Yang, C.J. Ho, S.H. Wang, S.M. Liu, E. Chang, C.H. Yeh, and M. Ouhyoung, “Automatic Chinese food identification and quantity estimation,” Siggraph Asia, 2012.
[10]M. Puri, Z. Zhu, Q. Yu, A. Divakaran, and H. Sawhney, “Recognition and volume estimation of food intake using a mobile device,” Workshop on Application of Computer Vision, pp. 1-8, 2009.
[11]K. Kitamura, C. de Silva, T. Yamasaki, and K. Aizawa, “Image processing based approach to food balance analysis for personal food logging,” IEEE International Conference onMultimedia and Expo, pp. 625-630, 2010.
[12]W. Wu and J. Yang, “Fast food recognition from videos of eating for calorie estimation,” IEEE Conference on Multimedia & Expo, pp. 1210-1213, 2009.
[13]S. Yang, M. Chen, D. Pomerleau, and R. Sukthanker, “Food recognition using statistics of pairwise local features,” IEEE Computer Vision and Pattern Recognition, 2010.
[14]T. Joutou and K. Yanai, “A food image recognition system with multiple kernel learning,” IEEE International Conference on Image Processing, pp. 285-255, 2009.
[15]H. Hoashi, T. Joutou, and K. Yanai, “Image recognition of 85 food categories by feature fusion,” IEEE Symposium on Multimedia, pp. 296-301, 2010.
[16]M. Bosch, F. Zhu, N. Khanna, C.J. Boushey, and E.J. Delp, “Food texture descriptors based on fractal and local gradient information,” European Signal Processing Conference, pp. 764-768, 2011.
[17]Y. He, C. Xu, N. Khanna, C.J. Boushey, and E.J. Delp, “Context based food image analysis,” IEEE International Conference on Image Processing, pp. 2748-2752, 2013.
[18]T.F. Wu, “Cafeteria vision: Identification and amount measurement of foods in a plate,” http://mabelsfoodrecognition.blogspot.tw/2011_03_01_archive.html
[19]Ministry of Agriculture, Forestry and Fisheries of Japan, Food Balance Guide [Online]. Available: http://www.maff.go.jp/j/balance_guide (in Japanese)
[20]United States Department of Agriculture, My Plate & Food Pyramid Resources [Online]. Available: http://fnic.nal.usda.gov/dietary-guidance/
[21]Y. Matsuda, H. Hoashi, and K. Yanai, “Recognition of multiple-food images by detecting candidate regions,” International Conference on Multimedia and Expo, 2012.
[22]Y. Matsuda and K. Yanai, “Multiple-food recognition considering co-occurrence employing manifold ranking,” International Conference on Pattern Recognition, 2012.
[23]M. Bosch, F. Zhu, N. Khanna, C.J. Boushey, and E.J. Delp, “Combining global and local features for food identification in dietary assessment,” IEEE Conference on Image Processing, pp. 1789-1792, 2011.
[24]F. Zhu, M. Bosch, N. Khanna, C.J. Boushey, and E.J. Delp, “Multilevel segmentation for food classification in dietary assessment,” International Symposium on Image and Signal Processing and Analysis, pp. 337-342, 2011.
[25]F. Zhu, “Multilevel image segmentation with application in dietary assessment and evaluation,” Ph. D. dissertation, Purdue University, 2011.
[26]J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, 2000.
[27]D.G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[28]A. Bosch, A. Zisserman, and X. Muoz, “Image classification using random forests and ferns,” International Conference on Computer Vision, pp. 1-8, 2007.
[29]S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169-2178, 2006.
[30]H. Zhang, A.C. Berg, M. Maire, and J. Malik, “Svm-knn: Discriminative nearest neighbor classification for visual category recognition,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2126-2136, 2006.
[31]J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: A comprehensive study,” International Journal of Computer Vision, vol. 73, pp. 213-238, 2007.
[32]C. Yang, L. Zhang, H. Lu, X. Ruan, and M.H. Yang, “Saliency detection via graph-based manifold ranking,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166-3173, 2013.
[33]J.J. Ding, C.J. Kuao, and W.C. Hong, “An efficient image segmentation technique by fast scanning and adaptive merging,” Computer Vision, Graphics, and Image Processing, Taiwan, 2009.
[34]R.O. Duda and P.E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” ACM Communications, vol. 15, no.1, pp.11-15, 1972.
[35]R. Achanta, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, 2012.
[36]S. Ray and R.H. Turi, “Determination of number of clusters in k-means clustering and application in colour image segmentation,” International Conference on Advances in Pattern Recognition and Digital Techniques, 1999.
[37]D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Patter Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002.
[38]Y. Boykovand and G. Funka-Lea. “Graph cuts and efficient ND image segmentation,” International Journal of Computer Vision, vol. 70, no. 2 pp. 109-131, 2006.
[39]C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: Interactive foreground extraction using iterated graph cuts,” ACM Transactions on Graphics, vol. 23, no. 3, pp. 309-314, 2004.
[40]G. Economou, V. Pothos, and A. Ifantis, “Geodesic distance and MST based image segmentation,” European Signal Processing Conference, pp. 941-944, 2004.
[41]C. Tomasi, Carlo, and R. Manduchi, “Bilateral filtering for gray and color images,” International Conference on Computer Vision, pp. 839-846, 1998.
[42]H. Bay, A. Ess, L. Tuytelaars, and T. Van Cool, “Surf: Speeded up robust features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-349, 2008.
[43]T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.
[44]J.L. Shih and L.H. Chen, “Colour image retrieval based on primitives of colour moments,” IEE Proceedings-Vision, Image and Signal Processing, vol. 149, no. 6, pp. 370-376, 2002.
[45]C.C. Chang and C.J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1-27:27, 2011.
[46]Central Bank of the Republic of China, http://www.currency.cbc.gov.tw/a1/T04.htm#1

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