In the field of harvesting robot control with vision feedback, hand-eye system calibration is very important which directly affects the precision of the task. Aiming at the feature that the vision of harvesting robot is fixed, a hand-eye system calibration method was developed. In the proposed method, by making use of the stereo vision to obtain coordinates of the target point in the robot coordinate system, and combining robot motion parameters, the calibration equations can be established first, and the eye-to-hand systems can be further calibrated based on improved least square method. This calibration process is simple, and does not need complex auxiliary devices. Related experiments have been done based on the suggested method to obtain the transformation matrix between hand-eye systems calibration, which has been shown to be of precision and efficiency.
Super-resolution image reconstruction (SRIR) can improve the fuzzy image’s resolution; solve the shortage of the spatial resolution, excessive noise, and low-quality problem of the image. Firstly, we introduce the image degradation model to reveal the essence of super-resolution reconstruction process is an ill-posed inverse problem in mathematics. Secondly, analysis the blurring reason of optical imaging process - light diffraction and small angle scattering is the main reason for the fuzzy; propose an image point spread function estimation method and an improved projection onto convex sets (POCS) algorithm which indicate effectiveness by analyzing the changes between the time domain and frequency domain algorithm in the reconstruction process, pointed out that the improved POCS algorithms based on prior knowledge have the effect to restore and approach the high frequency of original image scene. Finally, we apply the algorithm to reconstruct synchrotron radiation computer tomography (SRCT) image, and then use these images to reconstruct the three-dimensional slice images. Comparing the differences between the original method and super-resolution algorithm, it is obvious that the improved POCS algorithm can restrain the noise and enhance the image resolution, so it is indicated that the algorithm is effective. This study and exploration to super-resolution image reconstruction by improved POCS algorithm is proved to be an effective method. It has important significance and broad application prospects – for example, CT medical image processing and SRCT ceramic sintering analyze of microstructure evolution mechanism.
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