Single multimode fiber (MMF) digital scanning imaging system is a development tendency of modern endoscope. We concentrate on the calibration method of the imaging system. Calibration method comprises two processes, forming scanning focused spots and calibrating the couple factors varied with positions. Adaptive parallel coordinate algorithm (APC) is adopted to form the focused spots at the multimode fiber (MMF) output. Compare with other algorithm, APC contains many merits, i.e. rapid speed, small amount calculations and no iterations. The ratio of the optics power captured by MMF to the intensity of the focused spots is called couple factor. We setup the calibration experimental system to form the scanning focused spots and calculate the couple factors for different object positions. The experimental result the couple factor is higher in the center than the edge.
In machine vision measurement, the edge is a key point for fitting geometric parameter. There are two problems in the edge detection process. First, there is redundant information for the object with complex shape in the field of the view. Second, the surface of the object is full of texture which is misidentified as the edge. The texture processes similar feature to the edge and cannot be removed by filter. To solve the above problems, vision sight is proposed to get an interesting region and remove redundant information. A new algorithm based on fuzzy entropy is used to auto-estimate the edge detection direction from the pure region to mixed region in order to avoid the textures which misidentified as the edge. Comparing the algorithm with Canny, the former gets less texture points than the latter. A mask film is used as a standard to weight the validity of the algorithm. The experimental result shows that the algorithm proposed by this paper is robust and accuracy in detecting edge.
According to radiation temperature measurement theory, the key of temperature measurement is to choose the appropriate temperature model, which depends on the type of measured material. So how to identify the material type is significant to measure its surface temperature. Different materials have different spectral characters at the same temperature. In this paper, a method based on spectrum analysis is proposed to identify material. The spectrum of four kinds of materials is measured using Fourier transform infrared spectrometer (FTIR) at the same temperature 873K. The peak values extracted from each spectrum are used to train the identification algorithm. Then one material is chosen from the measured materials to verify the identification algorithm if the type of material can be identified. The experimental results suggest that the new method based on spectrum analyses can accurately identify the type of material.