@article {18431,
	title = {Software process improvement in small organizations: a case study},
	journal = {Software, IEEE},
	volume = {22},
	year = {2005},
	month = {2005/12//nov},
	pages = {68 - 75},
	abstract = {The Capability Maturity Model for software has become a major force in software development process improvement. We looked at the role of process improvement in the context of a small organization. Although the Capability Maturity Model integration is replacing the CMM, we focused on the older CMM. We had to look at more than which CMM key process areas apply to small businesses. Our overall goal was to institute good software development practices, we used the language of the CMM to gain the management{\textquoteright}s cooperation. Applying these practices is essential to managing growth, yet undertaking this effort without prior experience could impede a small company{\textquoteright}s innovative nature. This case study{\textquoteright}s purpose was to investigate the success factors of a software process improvement effort for a small software development organization.},
	keywords = {area;, aspects;, capability, CMM, development, improvement;, key, management;, Maturity, model;, organisational, organization;, process, small, software},
	isbn = {0740-7459},
	doi = {10.1109/MS.2005.162},
	author = {Dangle,K.C. and Larsen,P. and Shaw,M. and Zelkowitz, Marvin V}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
