Cervical cancer arises when the anomalous cells on the cervix mature
unmanageable obviously in the renovation sector. The most probably used
methods to detect abnormal cervical cells are the routine and there is no
difference between the abnormal and normal nuclei. So that the abnormal
nuclei found are brown in color while normal nuclei are blue in color. The
spread or cells are examined and the image denoising is performed based on
the Iterative Decision Based Algorithm. Image Segmentation is the method of
paneling a digital image into compound sections. The major utilize of
segmentation is to abridge or modify the demonstration of an image. The
images are segmented by applying anisotropic diffusion on the Denoised image.
Image can be enhanced using dark stretching to increase the quality of the
image. It separates the cells into all nuclei region and abnormal nuclei
region. The abnormal nuclei regions are further classified into touching and
non-touching regions and touching regions undergoes feature selection
process. The existing Support Vector Machines (SVM) is classified few nuclei
regions but the time to taken for execution is high. The abnormality detected
from the image is calculated as 45% from the total abnormal nuclei. Thus the
proposed method of Fast Particle Swarm Optimization with Extreme Learning
Machines (Fast PSO-ELM) to classify all nuclei regions further into touching
region and separated region. The iterative method for to training the ELM and
make it more efficient than the SVM method. In experimental result, the
proposed method of Fast PSO-ELM may shows the accuracy as above 90% and
execution time is calculated based on the abnormality (ratio of abnormal
nuclei regions to all nuclei regions) image. Therefore, Fast PSO-ELM helps to
detect the cervical cancer cells with maximum accuracy.
In this paper, we propose a particle-filter-based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non-Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal-tracking approach on a Taylor series expanded state model and a polar-to-Cartesian-conversion-based measurement model. Computation of sample weights through non-Gaussian likelihood forms the major contribution of the proposed particle-filter-based approach compared to the existing unscented-Kalman-filter-based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state-of-the-art especially at very low signal-to-noise ratios (i.e., especially in the range of -5 to 20 dB). The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation.
Phase information recovered through interferometric techniques is mathematically wrapped in the interval (−π, π]. Obtaining the original unwrapped phase is very important in numerous number of applications. This paper discusses a Fourier transform based phase unwrapping method. Kalman filter is proposed for denoising in post processing step to restore the unwrapped phase without any noise. The proposed method is highly robust to noise and performs better even at lower SNR values (5-10dB) with a very less value of RMS error. Also, the time taken for execution is very less compared to the many available methods in the literature.
Robotic Soccer has been an intriguing field of research in Artificial Intelligence. In this paper we present a novel planning strategy to build a hierarchical intelligence model for two scalable robot teams. This prototype enables the robots to analyze the game states, decide their moves and to take decisions. The System employs the Bayesian-SVM Classifier to decide upon subsequent actions and the movement of ball. We have scaled up the robot moves by incorporating the Case Based Reasoning (CBR) for optimized behavior. Simulation results illustrate that SVMRobosoc significantly improves the performance.
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