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Mizanur M. Rahman, Edmund A. Quincy, Raymond G. Jacquot, and Michael J. Magee


New feature extraction techniques are developed for two-dimensional binary images of ice particles and raindrops. These features are employed in the statistical classification of these patterns into one of seven basic hydrometeor shapes. These images have been recorded by an airborne two-dimensional probe in order to provide information leading to an understanding of important physical processes in clouds. Minimum average probability of classification error is employed as the performance criterion with informal minimization of computing time.

A synthetic image set was generated to develop feature extraction techniques. Moment normalization and rotation normalization are employed to convert all images to a common size and orientation.

Ten time domain features are explored including a new development (circular deficiency) and a new application (cross correlation). Three frequency domain features are investigated including a new Fourier descriptor (centroid distance). An original method of reducing aliasing due to binary quantization is tested. Final features are selected on the basis of classification performance when applied to the synthetic image set. Bayes classification with four selected time domain features (independent of size, position and angular orientation) was twenty times faster than frequency domain classification. The average recognition time was 2032 images per minute for time domain classification.

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Mizanur M. Rahman, Raymond G. Jacquot, Edmund A. Quincy, and Ronald E. Stewart


The investigation reported here involves the automatic classification of binary (black and white) images of hydrometeors (ice particles and raindrops) taken from cloud samples. The goal is to classify such images (both complete and fractional) into the seven most common classes of hydrometeors by statistical pattern recognition techniques. Detailed investigation about the data acquisition system and preprocessing is made. Four moment invariants which yield good class separation were used as features for the classification process. A Bayes decision function which minimizes the probability of misclassification is used for classification.

Bayes theorem is employed to update mean vectors and covariance matrices involved in the decision function. A discrete Kalman filtering algorithm is developed for the on-line estimation of the probability of occurrence of each class. For such estimation a discrete adaptive Kalman filtering algorithm is also developed which adjusts the filter gain matrix such as to whiten the innovations sequence. These techniques were shown to work well but the adaptive algorithm was found to converge to the correct probability more rapidly.

The classification algorithm was modified to classify incomplete or fractional images and two metrics were successfully developed to detect the unclassifiable images. The adaptive Kalman filter with the Bayes decision function was employed to classify about 2000 images per minute of CPU time with about 10% error.

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