References (31)
References
Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: Speeded up robust features. In European Conference on Computer Vision, 404–417.Google Scholar
Brône, G., Oben, B., a Goedemé, T. (2011). Towards a more effective method for analyzing mobile eye-tracking data: integrating gaze data with object recognition algorithms. In Proceedings of the 1st PETMEI Workshop in Pervasive Eye-Tracking and Mobile Eye-Based Interaction, 53–56.DOI logoGoogle Scholar
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). Brief: binary robust independent elementary features. In European Conference on Computer Vision, 778–792.Google Scholar
Dalal, N. & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 886–893.Google Scholar
De Beugher, S., Brône, G., & Goedemé, T. (2012). Automatic analysis of eye-tracking data using object detection algorithms. Proceedings of the 2nd PETMEI Workshop in Pervasive Eye-Tracking and Mobile Eye-Based Interaction.DOI logoGoogle Scholar
(2014). Automatic analysis of in-the-wild mobile eye-tracking experiments using object, face and person detection. In Computer Vision Theory and Applications, 625–633.Google Scholar
Dollár, P., Tu, Z., Perona, P., & Belongie, S. (2009). Integral channel features. In Proceedings of the British Machine Vision Conference, 1–11.Google Scholar
Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. Transactions on Pattern Analysis and Machine Intelligence, 34(4), 743–761.DOI logoGoogle Scholar
Evans, K. M., Jacobs, R. A., Tarduno, J. A., & Pelz, J. B. (2012). Collecting and analyzing eye-tracking data in outdoor environments. Journal of Eye Movement Research, 5(2) 1–19.Google Scholar
Felzenszwalb, P. F., Girshick, R. B., & McAllester, D. (2010). Cascade object detection with deformable part models. In Computer Vision and Pattern Recognition, 2241–2248.Google Scholar
Fischler, M. A. & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commununications of the ACM, 24(6), 381–395.DOI logoGoogle Scholar
Gall, J. & Lempitsky, V. (2009). Class-specific hough forests for object detection. In Computer Vision and Pattern Recognition, 1022–1029.Google Scholar
Hayes A. F. & Krippendorff K. (2007). Answering the Call for a Standard Reliability Measure for Coding Data. Communication Methods and Measures, 1(1), 77–89.DOI logoGoogle Scholar
Henderson, J. M. (2003). Human gaze control during real-world scene perception. Trends in Cognitive Sciences, 7(11), 498–504.DOI logoGoogle Scholar
Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In International Conference on Computer Vision, 2106–2113.Google Scholar
Kalman, R. (1960) A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME Journal of Basic Engineering, 82, 35–45.DOI logoGoogle Scholar
Kassner, M. and Patera, W. & Bulling, A. (2014) Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction. In CoRR.DOI logoGoogle Scholar
Leutenegger, S., Chli, M., & Siegwart, R. (2011). Brisk: Binary robust invariant scalable keypoints. In International Conference on Computer Vision, 2548–2555.Google Scholar
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.DOI logoGoogle Scholar
Mathias, M., Benenson, R., Timofte, R., & Van Gool, L. (2013). Handling occlusions with franken-classifiers. In International Conference on Computer Vision, 1505–1512.Google Scholar
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Van Gool, L. (2005). A comparison of affine region detectors. International Conference on Computer Vision, 65(1–2), 43–72.DOI logoGoogle Scholar
Miksik, O. & Mikolajczyk, K. (2012). Evaluation of local detectors and descriptors for fast feature matching. In International Conference on Pattern Recognition, 2681–2684.Google Scholar
Rosten, E. & Drummond, T. (2005). Fusing points and lines for high performance tracking. In International Conference on Computer Vision, 1508–1515.Google Scholar
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). Orb: An efficient alternative to sift or surf. In International Conference on Computer Vision 2564–2571.Google Scholar
Toyama, T., Kieninger, T., Shafait, F., & Dengel, A. (2012). Gaze guided object recognition using a head-mounted eye tracker. In Proceedings of the ETRA Conference, 91–98.Google Scholar
Tuytelaars, T. & Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 3(3), 177–280.DOI logoGoogle Scholar
Van Gompel, R. (2007). Eye Movements: A Window on Mind and Brain. Elsevier Science.Google Scholar
Vandemoortele, S., De Beugher, S., Brône, G., Feyaerts, K., Goedemé, T., De Baets, T., & Vervliet, S. (2015). Into the wild: Musical communication in ensemble playing. Discerning mutual and solitary gaze events in musical duos using mobile eye-tracking. In Proceedings of the SAGA Workshop.Google Scholar
Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 511–518.Google Scholar
Vandemoortele, S., De Beugher, S., Brône, G., Feyaerts, K., Goedemé, T., De Baets, T., & Vervliet, S. (2016). In Into the Wild. Muzikale Interactie in Ensembles: een Multimodale Studie met Eye-Trackers.Leuven: Acco.Google Scholar
Yun, K., Peng, Y., Samaras, D., Zelinsky, G. J., & Berg, T. L. (2013). Studying relationships between human gaze, description, and computer vision. In Computer Vision and Pattern Recognition, 739–746.Google Scholar