Abstract: In quality control of freeze-dried durian, crispiness is
a key quality index of the product. Generally, crispy testing has to be
done by a destructive method. A nondestructive testing of the
crispiness is required because the samples can be reused for other
kinds of testing. This paper proposed a crispiness classification
method of freeze-dried durians using fuzzy logic for decision
making. The physical changes of a freeze-dried durian include the
pores appearing in the images. Three physical features including (1)
the diameters of pores, (2) the ratio of the pore area and the
remaining area, and (3) the distribution of the pores are considered to
contribute to the crispiness. The fuzzy logic is applied for making the
decision. The experimental results comparing with food expert
opinion showed that the accuracy of the proposed classification
method is 83.33 percent.
Abstract: A human verification system is presented in this
paper. The system consists of several steps: background subtraction,
thresholding, line connection, region growing, morphlogy, star
skelatonization, feature extraction, feature matching, and decision
making. The proposed system combines an advantage of star
skeletonization and simple statistic features. A correlation matching
and probability voting have been used for verification, followed by a
logical operation in a decision making stage. The proposed system
uses small number of features and the system reliability is
convincing.