Image feature extraction using gradient local auto-correlations

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

Volumn 5302 LNCS, Issue PART 1, 2008, Pages 346-358

  Kobayashi, Takumi ^a   Otsu, Nobuyuki ^a  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

DISCRIMINATIVE POWER; HISTOGRAM-BASED METHOD; IMAGE FEATURE EXTRACTIONS; IMAGE GRADIENTS; LOCAL AUTO-CORRELATION; LOCAL GRADIENTS; ORDER STATISTICS; PEDESTRIAN DETECTION;

COMPUTER VISION;

EID: 56749157856     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88682-2_27     Document Type: Conference Paper

References (20)

1. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511-518 (2001)
2. Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 878-885 (2005)
3. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27, 1615-1630 (2005)
4. Lin, Y.Y., Liu, T.L., Fuh, C.S.: Local ensemble kernel learning for object category recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8 (2007)
5. Smeulders, A.W., Worring, M., Sntini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence 22, 1349-1380 (2000)
6. Boiman, O., Irani, M.: Detecting irregularities in images and in video. In: International Conference on Computer Vision, pp. 462-469 (2005)
7. Belongie, S., Malik, J., Puzicha, J.: Matching shapes. In: International Conference on Computer Vision, pp. 454-461 (2001)
8. Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511-518 (2007)
9. Laptev, I., Lindeberg, T.: Space-time interest points. In: International Conference on Computer Vision, pp. 432-439 (2003)
10. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International journal of computer vision 73, 213-238 (2007)
11. Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: International Conference on Computer Vision, pp. 1-8 (2007)
12. Lowe, D.: Distinctive image features from scale invariant features. International Journal of Compuater Vision 60, 91-110 (2004)
13. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 20-25 (2005)
14. Rautkorpi, R., Iivarinen, J.: A novel shape feature for image classification and retrieval. In: International Conference on Image Analysis and Recognition, pp. 753-760 (2004)
15. Otsu, N., Kurita, T.: A new scheme for practical flexible and intelligent vision systems. In: IAPR Workshop on Computer Vision (1988)
16. Russ, J. (ed.): The Image Processing Handbook. CRC Press, Boca Raton (1995)
17. Winder, S., Brown, M.: Learning local image descriptors. In: Computer Vision and Pattern Recognition, pp. 1-8 (2007)
18. Vapnik, V. (ed.): Statistical Learning Theory. Wiley, Chichester (1998)
19. Freeman, W., Adelson, E.: The design and use of steerable filters. Pattern Analysis and Machine Intelligence 13, 891-906 (1991)
20. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. Pattern Analysis and Machine Intelligence 23, 349-361 (2001)