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Levi and Weiss [17] add a new perspective on the training features proposed by Viola and Jones [27] They also detect upright, forward-facing faces Among other contributions in their work [17], their most striking revelation was adding an edge orientation feature that the machine can be trained on They also experimented with mean intensity features, which means taking the average pixel intensity in a rectangular area These features did not produce good results in their experiments and were not used in their system In addition to the features used by Viola and Jones [27], which considered sums of pixel intensities, Levi and Weiss [17] create features based on the most prevalent orientation of edges in rectangular areas There are obviously many orientations available for each pixel but they are reduced to eight possible rotations for ease of comparison and generalization For any rectangle, many possible features are extracted.

One set of features is the ratio of any two pairs of the eight EOHs [17] There are therefore 8 choose 2 = 28 possibilities for such features Another feature that is calculated is the ratio of the most dominant EOH in a rectangle to the sum of all other EOHs Levi and Weiss [17] claim that using EOHs, they are able to achieve higher detection rates at all training database sizes Their goal was to achieve similar or better performance of the system to Viola and Jones work while substantially reducing training time They primarily achieve this because EOH gives good results with a much smaller training set Using these orientation features, symmetry features are created and used Every time a WC was added to their machine, its vertically symmetric version was added to a parallel yet independent cascade.

Singh (2003) FAST Kernel: Background Theory and Experimental Results , presented at the First International Workshop on Protocols for Fast Long-Distance Networks, February 3 4, 2003, CERN, Geneva, Switzerland [6] S Floyd and T Henderson (1999) The NewReno Modification to TCP s Fast Recovery Algorithm , RFC 2582, April 1999 [7] S Floyd (2003) HighSpeed TCP for Large Congestion Windows, RFC 3649, Experimental, December 2003 [8] T Kelly (2003) Scalable TCP: Improving Performance in HighSpeed Wide Area Networks , First International Workshop on Protocols for Fast Long-Distance Networks, Geneva, February 2003 [9] M Allman, V Paxson, and W Stevens (1999) TCP Congestion Control, RFC 2581, April 1999 [10] S Floyd and K Fall (1997) Router Mechanisms to Support End-to-End congestion control, LBL Technical Report, February 1997 [11] L Brakmo and L.

Using this parallel machine architecture, the authors were able to increase the accuracy of their system by 2 percent when both machines were run simultaneously on the test data The authors also mention detecting pro le faces Their results are comparable to those of other proposed systems but their system works in real-time and uses a much smaller training set..

Peterson (1995) TCP Vegas: End to End Congestion Avoidance on a Global Internet , IEEE Journal on Selected Areas in Communication, 13, 1465 1480 [12] S Floyd (2002) HighSpeed TCP for Large Congestion Windows and Quick-Start for TCP and IP, Yokohama IETF, tsvwg, July 18, 2002 [13] RN Shorten and DJ Leith (2004) H-TCP: TCP for High-Speed and Long-Distance Networks Proceedings of PFLDnet, Argonne, 2004 [14] M Gerla, MY Sanadidi, R Wang, A Zanella, C Casetti, and S Mascolo (2001) TCP Westwood: Congestion Window Control Using Bandwidth Estimation , In Proceedings of IEEE Globecom 2001, San Antonio, Texas, USA, November 25 29, Vol 3, pp 1698 1702 [15] B Wydrowski and M Zukerman (2002) MaxNet: A Congestion Control Architecture for Maxmin Fairness , IEEE Communications Letters, 6, 512 514 [16] BP Wydrowski, LLH Andrew, and IMY.

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