An Image Representation Method Based on Retina Mechanism for the Promotion of SIFT and Segmentation

1. Elder, J.H.: Are edges incomplete? International Journal of Computer Vision 34(2), 97–122 (1999)

   Article  MathSciNet  Google Scholar 

2. Shou, T., Wang, W., Yu, H.: Orientation biased extended surround of the receptive field of cat retinal ganglion cells. Neuroscience 98, 207–212 (2000)

   Article  Google Scholar 

3. Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: IEEE International Conference on Computer Vision, vol. 651, pp. 654–661. IEEE Press (2005)

   Google Scholar 

4. Saxena, A., Chung, S.H., Ng, A.Y.: 3-d depth reconstruction from a single still image. International Journal of Computer Vision 76(1), 53–69 (2008)

   Article  Google Scholar 

5. Fauqueur, J., Boujemaa, N.: Region-based image retrieval: Fast coarse segmentation and fine color description. Journal of Visual Languages & Computing 15(1), 69–95 (2004)

   Article  Google Scholar 

6. Deng, Y., Manjunath, B., Kenney, C., Moore, M.S., Shin, H.: An efficient color representation for image retrieval. IEEE Transactions on Image Processing 10(1), 140–147 (2001)

   Article  MATH  Google Scholar 

7. Qiu, F.T., Chao-Yi, L.: Mathematic simulation of disinhibitory properties of concentric receptive field. Acta Biophysica Sinica 11, 214–220 (1995)

   Google Scholar 

8. Ghosh, K., Sarkar, S., Bhaumik, K.: A possible mechanism of zero-crossing detection using the concept of the extended classical receptive field of retinal ganglion cells. Biological Cybernetics 93, 1–5 (2005)

   Article  MATH  Google Scholar 

9. Chao-Yi, L., Wu, L.: Extensive integration field beyond the classical receptive field of cat’s striate cortical neurons–classification and tuning properties. Vision Research 34(18), 2337–2355 (1994)

   Article  Google Scholar 

10. Chao-Yi, L.: Integration field beyond the classical receptive field: organization and functional properties. News Physiol. Sci. 11, 181–186 (1996)

    Google Scholar 

11. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, vol. 1152, pp. 1150–1157. IEEE Press (1999)

    Google Scholar 

12. Fernandes, B.J.T., Cavalcanti, G.D.C., Ren, T.I.: Nonclassical receptive field inhibitonapplied to image segmentation. Neural Network World 19, 21 (2010)

    Google Scholar 

13. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1 (2011)

    Article  Google Scholar 

14. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceeding Eighth IEEE International Conference Computer Vision, vol. 412, pp. 416–423. IEEE Press (2001)

    Google Scholar 

15. Maire, M., Arbeláez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press (2008)

    Google Scholar 

16. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

17. Arbelaez, P.: Boundary extraction in natural images using ultrametric contour maps. In: IEEE Vision and Pattern Recogniton Workshop, pp. 182–182. IEEE Press (2006)

    Google Scholar 

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