In the process of zero-crossing trigger measurement of differential confocal microscope, the sample surface features or tilt will cause the edges can't be triggered. Meanwhile, environment vibration can also cause false triggering. In order to restore the invalid information of sample, and realize high-precision surface topography measurement, Total Variation (TV) inpainting model is applied to restore the scanning images. Emulation analysis and experimental verification of this method are investigated. The image inpainting algorithm based on TV model solves the minimization of the energy equation by calculus of variations, and it can effectively restore the non-textured image with noises. Using this algorithm, the simulation confocal laser intensity curve and height curve of standard step sample are restored. After inpainting the intensity curve below the threshold is repaired, the maximum deviation from ideal situation is 0.0042, the corresponding edge contour of height curve is restored, the maximum deviation is 0.1920, which proves the algorithm is effective. Experiment of grating inpainting indicates that the TV algorithm can restore the lost information caused by failed triggering and eliminate the noise caused by false triggering in zero-crossing trigger measurement of differential confocal microscope. The restored image is consistent with the scanning result of OLYMPUS confocal microscope, which can satisfy the request of follow-up measurement analysis.
An adaptive approach to small object segmentation based on Genetic Algorithms is proposed. A new parameter scale of the subject area's percentage is introduced in this method, which can overcome the P-tile method's defect of requiring the exact percentage of an object area, and meanwhile makes effective use of the small object's character. Genetic Algorithm forms the skeleton of the new approach, which can dynamically locate the optical threshold in the search space. The proposed algorithm can be extended to segment those images with object of arbitrary size by simply changing the set of the new parameter. Experiment results indicate that the proposed algorithm performs better segmentation quality and takes less computational time than conventional Otsu method.
Genetic Algorithm (GA) is derived from the mechanics of genetic adaptation in biological systems, which can search the global space of certain application effectively. The proposed algorithm introduces three parameters, fitmax, fitmin, and fitave to measure how close the individuals are, so as to improve the Adaptive Genetic Algorithm (AGA) proposed by M. Sriniras. At the same time, the elitist strategy is employed to protect the best individual of each generation, and Remainder Stochastic Sampling with Replacement (RSSR) is employed in the proposed Improved Adaptive Genetic Algorithm (IAGA) to improve the basic reproduction operator. The proposed IAGA is applied to image segmentation. The experimental results exhibit satisfactory segmentation and demonstrate the learning capabilities of it. By determining pc and pm of the whole generation adaptively, it strikes a balance between the two incompatible goals: sustain the global convergence capacity and converge rapidly to global optimum.
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