@conference {12696,
	title = {Role of shape and kinematics in human movement analysis},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-730 - I-737 Vol.1 - I-730 - I-737 Vol.1},
	abstract = {Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper we analyze the role of two of the most important cues in human motion-shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall{\textquoteright}s definition of shape. Kinematics is represented using a Linear Dynamical system We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in current automated gait based human identification algorithms. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.},
	keywords = {activity, algorithm;, algorithms;, analysis;, autoregressive, average, based, classification;, community;, Computer, definition;, dynamical, extraction;, feature, Gait, hidden, human, identification, image, Kendall, linear, manifold;, Markov, modeling;, models;, MOTION, Movement, moving, processes;, recognition, sequences;, SHAPE, spherical, system;, VISION, vision;},
	doi = {10.1109/CVPR.2004.1315104},
	author = {Veeraraghavan,A. and Chowdhury, A.R. and Chellapa, Rama}
}
@article {12121,
	title = {An operational process for goal-driven definition of measures},
	journal = {Software Engineering, IEEE Transactions on},
	volume = {28},
	year = {2002},
	month = {2002/12//},
	pages = {1106 - 1125},
	abstract = {We propose an approach (GOM/MEDEA) for defining measures of product attributes in software engineering. The approach is driven by the experimental goals of measurement, expressed via the GQM paradigm, and a set of empirical hypotheses. To make the empirical hypotheses quantitatively verifiable, GQM/MEDEA supports the definition of theoretically valid measures for the attributes of interest based on their expected mathematical properties. The empirical hypotheses are subject to experimental verification. This approach integrates several research contributions from the literature into a consistent, practical, and rigorous approach.},
	keywords = {attributes;, definition;, development, empirical, engineering;, expected, experimental, goal-driven, GQM, GQM/MEDEA;, hypotheses;, management;, mathematical, measure, measurement;, metrics;, operational, paradigm;, process;, Product, properties;, quality;, software, verification;},
	isbn = {0098-5589},
	doi = {10.1109/TSE.2002.1158285},
	author = {Briand,L.C. and Morasca,S. and Basili, Victor R.}
}
@conference {18373,
	title = {An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity},
	booktitle = {Software Reliability Engineering, 1992. Proceedings., Third International Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {164 - 172},
	abstract = {Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their fair share of problems. A decision tree model has previously been used to identify such modules. In this paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement has been tested using data from the NASA Software Engineering Laboratory},
	keywords = {(mathematics);, analysis;, axiomatic, classification, classification;, complexity;, computational, cost, decision, definition;, high, metrics;, model;, modules;, overall, program, reliability;, software, subroutines;, system, tree, TREES},
	doi = {10.1109/ISSRE.1992.285848},
	author = {Tian,Jianhui and Porter, Adam and Zelkowitz, Marvin V}
}
