From where and how to what we see

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 625-632

  Karthikeyan, S ^a,b   Jagadeesh, Vignesh ^a,b   Shenoy, Renuka ^a,b   Ecksteinz, Miguel ^c   Manjunath, B S ^a,b  

^a Center for Bioimage Informatics   (United States)

^b Department of Electrical and Computer Engineering ^*  (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Dog and Cat Detection; Eye Tracking; Text Detection

Indexed keywords

DETECTORS; MARKOV PROCESSES; OBJECT RECOGNITION; SEMANTICS;

DETECTION PERFORMANCE; EYE-TRACKING; IMAGE INFORMATION; MARKOV RANDOM FIELDS; OBJECT DETECTION ALGORITHMS; OBJECT DETECTORS; REGIONS OF INTEREST; TEXT DETECTION;

EYE MOVEMENTS;

EID: 84898794039     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.83     Document Type: Conference Paper

References (31)

1. B. Alexe et al. Searching for objects driven by context. Advances in Neural Information Processing Systems 2012.
2. Y. Boykov et al. Fast approximate energy minimization via graph cuts. Pattern Analysis and Machine Intelligence 2001.
3. A. Bulling et al. Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011.
4. A. Bulling et al. Wearable EOG goggles: eye-based interaction in everyday environments. ACM, 2009.
5. A. Bulling and H. Gellersen. Toward mobile eye-based humancomputer interaction. Pervasive Computing, IEEE, 2010.
6. M. Cerf et al. Decoding what people see from where they look: Predicting visual stimuli from scanpaths. Attention in Cognitive Systems 2009.
7. M. Cerf et al. Faces and text attract gaze independent of the task: Experimental data and computer model. Journal of vision 2009.
8. H. Chen et al. Robust text detection in natural images with edgeenhanced maximally stable extremal regions. IEEE International Conference on Image Processing (ICIP) 2011.
9. X. Chen and A. L. Yuille. Detecting and reading text in natural scenes. CVPR 2004.
10. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002.
11. C. Desai et al. Discriminative models for multi-class object layout. Computer Vision-ICCV 2009.
12. S. K. Divvala et al. An empirical study of context in object detection. CVPR, 2009.
13. B. Epshtein et al. Detecting text in natural scenes with stroke width transform. CVPR 2010.
14. P. Felzenszwalb et al. A discriminatively trained, multiscale, deformable part model. IEEE CVPR, 2008.
15. S. Goferman et al. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012.
16. V. Hedau et al. Thinking inside the box: Using appearance models and context based on room geometry. Computer Vision-ECCV 2010.
17. L. Itti et al. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998.
18. T. Judd et al. Learning to predict where humans look. IEEE International Conference on Computer Vision 2009.
19. S. Karthikeyan et al. Learning bottom-up text attention maps for text detection using stroke width transform. ICIP, IEEE, 2013.
20. S. Karthikeyan et al. Learning top-down scene context for visual attention modeling in natural images. ICIP, IEEE, 2013.
21. S. M. Lucas et al. Icdar 2003 robust reading competitions. ICDAR.
22. A. Mishra et al. Active segmentation with fixation. IEEE International Conference on Computer Vision, 2009.
23. O. M. Parkhi et al. Cats and dogs. Computer Vision and Pattern Recognition (CVPR) 2012.
24. O. M. Parkhi et al. The truth about cats and dogs. IEEE International Conference on Computer Vision (ICCV), 2011.
25. C. Rother et al. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics (TOG) 2004.
26. P. Shivakumara et al. A laplacian approach to multi-oriented text detection in video. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011.
27. R. Subramanian et al. Can computers learn from humans to see better?: inferring scene semantics from viewers' eye movements. ACM International Conference on Multimedia 2011.
28. A. Torralba. Contextual priming for object detection. International Journal of Computer Vision 2003.
29. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR 2001.
30. K. Wang and S. Belongie. Word spotting in the wild. Computer Vision-ECCV 2010.
31. Y. Zhong et al. Automatic caption localization in compressed video. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000.