@conference {17175,
	title = {From slacktivism to activism: participatory culture in the age of social media},
	booktitle = {Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {819 - 822},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Social networking sites (e.g. Facebook), microblogging services (e.g. Twitter), and content-sharing sites (e.g. YouTube and Flickr) have introduced the opportunity for wide-scale, online social participation. Visibility of national and international priorities such as public health, political unrest, disaster relief, and climate change has increased, yet we know little about the benefits - and possible costs - of engaging in social activism via social media. These powerful social issues introduce a need for scientific research into technology mediated social participation. What are the actual, tangible benefits of "greening" Twitter profile pictures in support of the Iranian elections? Does cartooning a Facebook profile picture really raise awareness of child abuse? Are there unintended negative effects through low-risk, low-cost technology-mediated participation? And, is there a difference - in both outcome and engagement level - between different types of online social activism? This SIG will investigate technology mediated social participation through a critical lens, discussing both the potential positive and negative outcomes of such participation. Approaches to designing for increased participation, evaluating effects of participation, and next steps in scientific research directions will be discussed.},
	keywords = {activism, change, design, participation, slacktivism, social media},
	isbn = {978-1-4503-0268-5},
	doi = {10.1145/1979742.1979543},
	url = {http://doi.acm.org/10.1145/1979742.1979543},
	author = {Rotman,Dana and Vieweg,Sarah and Yardi,Sarita and Chi,Ed and Preece,Jenny and Shneiderman, Ben and Pirolli,Peter and Glaisyer,Tom}
}
@conference {13144,
	title = {Human Appearance Change Detection},
	booktitle = {Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on},
	year = {2007},
	month = {2007/09//},
	pages = {536 - 541},
	abstract = {We present a machine learning approach to detect changes in human appearance between instances of the same person that may be taken with different cameras, but over short periods of time. For each video sequence of the person, we approximately align each frame in the sequence and then generate a set of features that captures the differences between the two sequences. The features are the occupancy difference map, the codeword frequency difference map (based on a vector quantization of the set of colors and frequencies) at each aligned pixel and the histogram intersection map. A boosting technique is then applied to learn the most discriminative set of features, and these features are then used to train a support vector machine classifier to recognize significant appearance changes. We apply our approach to the problem of left package detection. We train the classifiers on a laboratory database of videos in which people are seen with and without common articles that people carry - backpacks and suitcases. We test the approach on some real airport video sequences. Moving to the real world videos requires addressing additional problems, including the view selection problem and the frame selection problem.},
	keywords = {(artificial, appearance, approach;occupancy, change, changes, classification;support, classifier;vector, detection;image, detection;machine, difference, Frequency, intelligence);pattern, intersection, learning, machine, machines;vector, map;histogram, map;human, map;support, package, quantisation;video, quantization;video, recognition;boosting, recognition;image, sequence;left, sequences;image, sequences;learning, surveillance;, technique;codeword, vector},
	doi = {10.1109/ICIAP.2007.4362833},
	author = {Ghanem,N.M. and Davis, Larry S.}
}
@conference {12761,
	title = {Quasi-invariants for human action representation and recognition},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {307 - 310 vol.1 - 307 - 310 vol.1},
	abstract = {Although human action recognition has been the subject of much research in the past, the issue of viewpoint invariance has received scarce attention. In this paper, we present an approach to detect human action with a high tolerance to viewpoint change. Canonical body poses are modeled in a view invariant manner to enable detection from a general viewpoint. While there exist no invariants for 3D to 2D projection, there exists a wealth of techniques in 2D invariance that can be used to advantage in 3D to 2D projection. We employ 2D invariants to recognize canonical poses of the human body leading to an effective way to represent and recognize human action which we evaluate theoretically and experimentally on 2D projections of publicly available human motion capture data.},
	keywords = {2D, action, analysis;, body, canonical, change, human, image, invariance;, MOTION, poses;, quasi-invariants;, recognition;, representation;, tolerance;, viewpoint},
	doi = {10.1109/ICPR.2002.1044699},
	author = {Parameswaran, V. and Chellapa, Rama}
}
