Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.

Adaptive median threshold algorithm used in FDIS of DSSS receivers

For direct sequence spread spectrum （DSSS） receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression （FDIS）. The key issue of this process is how to determine a threshold to eliminate interference in the frequency domain, which has been extensively studied. However, these previous methods are tedious or very complex. A simple and ef- ficient algorithm based on medians is proposed. The elimination threshold is only related to the median by a scale factor, which can be obtained by the numerical analysis. Simulation results show that the algorithm provides excellent narrow-band interfer- ence suppression while only slightly degrading the signal-to-noise ratio （SNR）. A one-pass algorithm using logarithmic segmentation is further derived to estimate medians with low computational complexity. Finally, the FDIS is implemented in a field programmable gate array （FPGA） of Xilinx. Experiments are carried out by connecting the FDIS FPGA to a DSSS receiver, and the results show that the receiver has an effective countermeasure for a 60 dB interference-to-signal ratio （ISR）.

Iterative Adaptive Median Filter for Impulse Noise Cancellation

Based on the characteristics of impulse noises, the authors establish a new filter, Iterative Adaptive Median Filter （IAMF）. Acccording to the characteristics of images polluted by impulse noises, they establish weight function combined with iterative algorithm to eliminate noises. In IAMF filter process, because the noise sixes do not participate in the computation, they do not influence the normal points in the image, therefore IAMF can retain the detail well, maintain the good clarity after processing image, and simultaneously reduce the computation. Experiments showed that IAMF have ideal denoising effect for the images polluted by the impulse noises; especially when the noise rates are more than 0.5, IAMF is mote prominent, even when the noise rotes are more than 0.9, IAMF can achieve a satisfactory results.

Adaptive Threshold Median Filter for Multiple-Impulse Noise

Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter （ATMF） has been developed by combining the adaptive median filter （AMF） and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed filter.

Adaptive Filter in SAR Interferometry Derived DEM

In this paper, the performance of median filter, elevation dependent adaptive sigma median filter, and directionally dependent adaptive sigma median filter are tested on both InSAR Tandem DEM and simulated high-level noisy DEM. Through the comparison, the directionally dependent adaptive sigma median filter is proved to be the most effective one not only in the noise removing but also in the boundary preserve.

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Pest detection in agricultural cropfields is the most challenging task,so an effective pest detection technique is required to detect insects automatically.Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection,improved crop man-agement and productivity.On the other hand,developing the automatic pest mon-itoring system dramatically reduces the workforce and errors.Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy.Therefore,a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitor-ing and detection.The four-step image processing technique begins with image pre-processing,removing the insect image’s noise and sunlight illumination by utilizing an adaptive medianfilter.The insects’size and shape are identified using the Expectation Maximization Algorithm(EMA)based clustering technique,which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image.Speeded up robust feature(SURF)method is employed to select the best possible image features.Eventually,the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm,which combines the benefits of Firefly(FF),Grey Wolf Optimization(GWO)and Convolutional Neural Network(CNN)classification algorithm for enhancing the classification accuracy.The entire work is executed in MATLAB simulation software.The test result reveals that the suggested technique has deliv-ered optimal performance with high accuracy of 97.5%,precision of 94%,recall of 92%and F-score value of 92%.

Efficient fundamental frequency transformation for voice conversion

In order to improve the performance of voice conversion, the fundamental frequency （F0） transformation methods are investigated, and an efficient F0 transformation algorithm is proposed. First, unlike the traditional linear transformation methods, the relationships between F0s and spectral parameters are explored. In each component of the Gaussian mixture model （GMM）, the F0s are predicted from the converted spectral parameters using the support vector regression （SVR） method. Then, in order to reduce the over- smoothing caused by the statistical average of the GMM, a mixed transformation method combining SVR with the traditional mean-variance linear （MVL） conversion is presented. Meanwhile, the adaptive median filter, prevalent in image processing, is adopted to solve the discontinuity problem caused by the frame-wise transformation. Objective and subjective experiments are carried out to evaluate the performance of the proposed method. The results demonstrate that the proposed method outperforms the traditional F0 transformation methods in terms of the similarity and the quality.

Filtering algorithm of line structured light for long-distance obstacle detection

Since unmanned ground vehicles often encounter concave and convex obstacles in wild ground, a filtering algorithm using line structured light to detect these long distance obstacles is proposed. For the line structured light image, a ranked-order based adaptively extremum median （RAEM） filter algorithm on salt and pepper noise is presented. In the algorithm, firstly effective points and noise points in a filtering window are differentiated; then the gray values of noise points are replaced by the medium of gray values of the effective pixels, with the efficient points＇ gray values unchanged; in the end this algorithm is proved to be efficient by experiments. Experimental resuits demonstrate that the image blur, resulting into proposed algorithm can remove noise points effectively and minimize the protecting the edge information as much as possible.

Smoother and Bayesian filter based semi-codeless tracking of dual-frequency GPS signals

To precisely determine the integrated orbit of the Chinese manned spacecraft mission, a smoother and Bayesian filter based technique for optimum semi-codeless tracking of the P（Y） code on dual-frequency GPS signals has been advanced. This signal processing technique has been proven effective and robust for affording access to dual-frequency GPS signals. This paper introduces the signal dynamics and measurement models, describes the W o D bit estimation method, and corrects the mistakes of direct estimation of W bit in current semi-codeless tracking. Median filter is chosen as a smoother to find the best measurements at the current time among the history and current information. The Bayesian filter is used to track the L2 P（Y） code phase and L2 carrier phase recursively.