Enhancement of low illumination images is of great importance in poor imaging conditions. A new image enhancement model was proposed in this paper. The model divided an image into blocks and used the local standard deviation to design the center/surround filter and utilized amplitude compensation factor to compensate the shortage of logarithmic function in compressing the near-zero data’s amplitude. In addition, the amplitude compensation factor can suppress noise. At the same time, the normalized brightness can maintain the normal brightness region of the image while the brightness of the image is increased. In order to verify the performance of the proposed model, the proposed model and existing models are applied to image enhancement. To evaluate its performance in image enhancement, results are compared from the subjective and objective aspects. The experimental results show that the proposed model preserved the image details better and avoided the excessive enhancement of the normal brightness region.
New grayscale morphological operators on hypergraph are proposed to avoid the loss of details caused by fixed structure element effectively. Hypergraph, the most general structure in discrete mathematics, is also a subset of a finite set. Being a structured representation of information, the ordinary image can be transformed into a hypergraph model, which can integrate hypergraph theory with mathematical morphology theory. Because hypergraphs have good performance in structuring information, first of all, this paper designs a reasonable method of turning grayscale images into hypergraph space. Then based on hypergraph theory, new grayscale morphological operators on hypergraph are defined. Experiments show that using the new operators can avoid the loss of image detail information, and improve the precision of image processing.
The image-based visual servoing would lead to image singularities that might cause control instabilities, and there exit
other constraints such as the object should remain in the camera field of view and avoid obstacles. This problem can be
solved by coupling path planning and image-based control. The trajectory is planned directly in the image space in our
strategy to avoid the 3D estimation of the object, which is required in the motion space based path planning method. In
the presented method, the initial path is given using the artificial potential field method without considering the
constraints and then genetic algorithm based method is used to check and modify the initial path. This method can
achieve satisfactory task while decrease the computation. The proposed method is used to align the micro peg and hole,
and the simulation results show that the object can reach its desired position accurately without violation these constrains.
Proportional control based visual controller is the main method used in the visual serving, but small proportional gain results in the slowly response and large proportional gain will result in large overshoot or make the system instable. A PD visual controller for microassembly system is presented to acquire better dynamic response. The fuzzy logic is applied to tuning the controller gains which is a model free method. Thus, the difficulty in obtaining precise and detailed system model is avoided and we can get satisfactory performance which is robust to modeling error and external disturbances. Furthermore, image moments are selected as visual features to avoid image singularities and the Jacobian matrix is full rank and upper triangular, thus it has the maximal decoupled structure and simplified the controller. A series of simulations are performed on peg and hole assembly to investigate the feasibility and effectiveness of this method.
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