Parikh et al. 1997

Parikh, J.A., J.S. DaPonte, J.N. Vitale, and G. Tselioudis, 1997: Pattern Recogn. Lett. vol. 18, pp. 1347-1351, doi:10.1016/S0167-8655(97)00115-3.

Cloud analyses provide information which is vital to the detection, understanding and prediction of meteorological trends and environmental changes. This paper compares statistical, neural network and genetic algorithm methods for recognition and tracking of midlatitude storm clouds in sequences of low-resolution cloud-top pressure data sets. Regions of interest are identified and tracked from one image frame to the next consecutive frame in an eight-frame sequence. Classification techniques are used to determine the relationships between regions of interest in consecutive time frames. A genetic algorithm procedure is then used to revise classifier outputs to ensure that consistency constraints are not violated.

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Parikh, J.A., DaPonte, J.S., Vitale, J.N., and Tselioudis, G.: Pattern Recogn. Lett., in: vol. 18, 1347-1351, doi:10.1016/S0167-8655(97)00115-3, 1997.

Parikh, J.A., J.S. DaPonte, J.N. Vitale, and G. Tselioudis (1997), Pattern Recogn. Lett., in, vol. 18, pp. 1347-1351, doi:10.1016/S0167-8655(97)00115-3.

Parikh, J.A., J.S. DaPonte, J.N. Vitale, and G. Tselioudis, 1997: Pattern Recogn. Lett. vol. 18, pp. 1347-1351, doi:10.1016/S0167-8655(97)00115-3.

Parikh, J.A., DaPonte, J.S., Vitale, J.N., & Tselioudis, G. 1997, in, vol. 18, 1347, doi:10.1016/S0167-8655(97)00115-3.

Parikh JA, DaPonte JS, Vitale JN, Tselioudis G. Pattern Recogn. Lett. In: vol. 18, pp. 1347-1351. 1997.

J.A. Parikh, J.S. DaPonte, J.N. Vitale, G. Tselioudis, In (vol.18, (1997), pp. 1347-1351.