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Machine vision systems with effective image processing methods are used in quality grading of agricultural products. A pattern recognition technique was developed to detect and classify surface defects such as pitting, splitting and stem-end rot found in images of mandarin fruits. The developed technique employs fuzzy thresholding for image segmentation, binary wavelet transform (BWT) for feature extraction and a rule based linear classifier model for detection and classification of the defects. The moment invariants computed from the detail subimage of BWT were taken as feature values. This paper in detail describes about the pattern recognition algorithm and its implementation. The detection and classification results obtained from the algorithm are reported and discussed. [ABSTRACT FROM PUBLISHER]

Text compression techniques like bzip2 lack the possibility to delete the nth word or to insert text be-fore the nth word of compressed texts without prior decompression of the compressed texts. We present a text compression technique that supports fast insertion into and deletion from compressed texts without full decompression of the compressed text. Our approach combines Indexed Reversible Transformation (IRT) [1], Run-Length-Encoding (RLE), and the Wavelet Tree (WT). For a reasona-ble size of inserted or deleted texts (more details are given in [2]), our approach is faster than modifying uncompressed text preceded by a decompression step and followed by a compression step. [ABSTRACT FROM PUBLISHER]