The impact of stereo visual sensor parameters on the measurement accuracy of the sensor is studied based on
experimental design. Using a mathematical model of stereo vision, errors caused by stereo visual sensor parameters are
analyzed. Based on this, main parameters are identified and used in experimental design. In order to find the optimized
combination of the parameters, optimum regression design method is used. Eleven primary experiments with different
combinations of parameters are designed and conducted. According to the experiment results, a regressive equation is
established. By resolving it, the optimized combination of the parameters is got.
A non-destructive measuring and evaluating method for fruits is proposed based on color identification. The color
images of fruits are taken firstly. Then, images' RGB histograms are calculated and used as quality parameters for fruits.
A BP neural network with three layers is established. Its input and output are the RGB histograms and evaluating results,
respectively. After training, the qualities of fruits are identified by the BP network according to the histogram of RGB of
fruits' images. For verifying the proposed method, the qualities of bananas are measured and evaluated. Experiment
results show the reliability and feasibility of proposed method.
In order to measure the activities of <i>Nostoc flagelliforme</i> cells, a new method based on color identification was proposed
in this paper. <i>N. flagelliforme </i>cells were colored with fluoreseein diaeetate. Then, an image of colored N. flagelliforme
cells was taken, and changed from RGB model to HIS model. Its histogram of hue H was calculated, which was used as
the input of a designed BP network. The output of the BP network was the description of measured activity of <i>N.
flagelliforme</i> cells. After training, the activity of <i>N. flagelliforme </i>cells was identified by the BP network according to the histogram of H of their colored image. Experiments were conducted with satisfied results to show the feasibility and
usefulness of activity measurement of <i>N. flagelliforme </i>cells based on color identification.
In this paper, a new method for monitoring and controlling fermentation process of branched chain amino acid (BCAA)
was proposed based on color identification. The color image of fermentation broth of BCAA was firstly taken by a CCD
camera. Then, it was changed from RGB color model to HIS color model. Its histograms of hue H and saturation S were
calculated, which were used as the input of a designed BP network. The output of the BP network was the description of
the color of fermentation broth of BCAA. After training, the color of fermentation broth was identified by the BP
network according to the histograms of H and S of a fermentation broth image. Along with other parameters, the
fermentation process of BCAA was monitored and controlled to start the stationary phase of fermentation soon.
Experiments were conducted with satisfied results to show the feasibility and usefulness of color identification of
fermentation broth in fermentation process control of BCAA.