For the automatic recognition of pointer instrument, the method for the automatic recognition of pointer instrument based on improved Hough Transform was proposed in this paper. The automatic recognition of pointer instrument is applied to all kinds of lighting conditions, but the accuracy of it binaryzation will be influenced when the light is too strong or too dark. Therefore, the improved Ostu method was suggested to realize recognition for adaptive thresholding of pointer instrument under all kinds of lighting conditions. On the basis of dial image characteristics, Otsu method is used to get the value of maximum between-cluster variance and initial threshold than analyze its maximum between-cluster variance value to determine the light and shade of the image. When the images are too bright or too dark, the smaller pixels should be given up and then calculate the initial threshold by Otsu method again and again until the best binaryzation image was obtained. Hence, transform the pointer straight line of the binaryzation image to Hough parameter space through improved Hough Transform to determine the position of the pointer straight line by searching the maximum value of arrays of the same angle. Finally, according to angle method, the pointer reading was obtained by the linear relationship for the initial scale and angle of the pointer instrument. Results show that the improved Otsu method make pointer instrument possible to obtained the accuracy binaryzation image even though the light is too bright or too dark , which improves the adaptability of pointer instrument to automatic recognize the light under different conditions. For the pressure gauges with range of 60MPa, the relative error identification reached to 0.005 when use the improved Hough Transform Algorithm.
The motion blur is one of the common factors leading to blurred images, the parameters of the point spread function (PSF) estimation is the key and prerequisite of motion blurred image restoration. Based on motion blur image characteristics of spectrum and cepstrum analysis, a automatic detection algorithm based on frequency domain and cepstrum domain algorithms is proposed in the paper, which can automatically detect the blur length and blur angle, then we can restorate the motion blur image. Experiments show that when the blur length is 15 ~ 80 pixels noiselessly, In addition to the individual blur length/angle (e.g. 30 pixels/300, 75 pixels/300), blur length estimation error is 0 ~ 0.2 pixels and blur angle estimation error is almost 0. The detection range is greater than some other methods, and the quality of image restoration is good.