Dish detection and segmentation for dietary assessment on smartphones

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9281, Issue , 2015, Pages 433-440

  Dehais, Joachim ^a,b   Anthimopoulos, Marios ^a,c   Mougiakakou, Stavroula ^a,c  

^a UNIVERSITY OF BERN   (Switzerland)

^b UNIVERSITY OF BERN   (Switzerland)

^c UNIVERSITY OF BERN   (Switzerland)

Author keywords

Computer vision; Diabetes; Diet assessment; Image segmentation; Obesity; Smartphone

Indexed keywords

COMPUTER VISION; IMAGE ANALYSIS; IMAGE PROCESSING; MEDICAL PROBLEMS; SMARTPHONES;

CHRONIC DISEASE; COMPUTER VISION SYSTEM; DIETARY ASSESSMENTS; OBESITY; PERSONAL INVOLVEMENT; SEGMENTATION METHODS; SEMI-AUTOMATICS; USER INTERACTION;

IMAGE SEGMENTATION;

EID: 84944706178     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-23222-5_53     Document Type: Conference Paper

References (27)

1. Shroff, G, Smailagic, A., Siewiorek, D.P.: Wearable context-aware food recognition for calorie monitoring. In: 12th IEEE ISWC, pp. 119-120 (2008)
2. He, Y., Khanna, N., Boushey, C.J., Delp, E.J.: Image segmentation for image-based dietary assessment: a comparative study. In: IEEE ISSCS 2013
3. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321-331 (1998)
4. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Tran. Pattern Anal. Mach. Intell. 22(8), 888-905 (2000)
5. Felzenszwalb, P.F., Huttenlocher, D.P.: Image segmentation using local variation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 98-104 (1998)
6. Zhu, F., Bosch, M., Khanna, N., Boushey, C.J., Delp, E.J.: Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment. IEEE J. Biomed. Health Inform. 19(1), 377-388 (2015)
7. Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: IEEE ICME, 2012, pp. 25-30 (2012)
8. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627-1645 (2010)
9. Duda, R.O., Hart, P.E.: Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM 15, 11-15 (1972)
10. Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800-810
11. Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G.D., Essa, I.A.: Leveraging Context to Support Automated Food Recognition in Restaurants. In: WACV 2015, pp. 580-587
12. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898-916 (2011)
13. Puri, M., Zhu, Z., Yu, Q., Divakaran, A., Sawhney, H.: Recognition and volume estimation of food intake using a mobile device. In: IEEE WACV, pp. 1-8 (2009)
14. Cai, W., Yu, Q., Wang, H., Zheng, J.: A fast contour-based approach to circle and ellipse detection. In: 5th IEEE WCICA (2004)
15. Anthimopoulos, M., Dehais, J., Diem, P., Mougiakakou, S.: Segmentation and recognition of multi-food meal images for carbohydrate counting. In: IEEE BIBE (2013)
16. Kawano, Y., Yanai, K.: FoodCam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications, pp. 1-25 (2014)
17. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309-314 (2004)
18. Morikawa, C., Sugiyama, H., Aizawa, K.: Food region segmentation in meal images using touch points. In: ACM Workshop on Multimedia for Cooking And Eating Activities (2012)
19. Oliveira, L., Costa, V., Neves, G., Oliveira, T., Jorge, E., Lizarraga, M.: A mobile, lightweight, poll-based food identification system. Pattern Recognition 47, 1941-1952 (2014)
20. Canny, J.: A Computational Approach to Edge Detetion. IEEE Trans. Pattern Analysis and Machine Intelligence 8(6), 679-698 (1986)
21. Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24(6), 381-395 (1981)
22. Ni, K., Jin, H., Dellaert, F.: Groupsac: efficient consensus in the presence of groupings. In: IEEE 12th International Conference on Computer Vision, pp. 2193-2200 (2009)
23. Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236-243. Springer, Heidelberg (2003)
24. Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641-647 (1994)
25. McDonald, R., Smith, K.J.: CIE94a new color difference formula. Journal of the Society of Dyers and Colourists 111(12), 376-379 (1995)
26. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Analysis and Machine Intelligence 26(11), 1452-1458 (2004)
27. Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. International Conference on Image Processing 3, 53-56 (1995)