Recognition of Partially Occluded Objects Based on Segmentation by Median Filtering of Extended Direction Code

The image contour is segmented into lines, arcs and smooth curves by median filtering of extended direction code. Based on this segmentation, a set of new local invariant features are proposed to recognize partially occluded objects, which is more reasonable compared with conventional corner features. The matching results of some typical examples shows that these features are robust ,effective in recognition.

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.

Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be processed in a holistic manner in the proposed scheme,which makes full use of the correlation between RGB channels.And due to the use of convolutional neural network,it can effectively avoid the one-sidedness of artificial features.Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.

Median Filtering Detection Based on Quaternion Convolutional Neural Network

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics.Therefore,more attention has been paid to the forensics research of median filtering.In this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is proposed.The median filtering residuals(MFR)are used to preprocess the images.Then the output of MFR is expanded to four channels and used as the input of QCNN.In QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional methods.The quaternion pooling layer is designed to evaluate the result of quaternion convolution.QCNN is proposed to features well combine the three-channel information of color image and fully extract forensics features.Experiments show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.

Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified median filter for medical image coupling denoising is proposed.The method is composed of four modules:image acquisition,image storage,image processing and image reconstruction.Image acquisition gets the medical image that contains Gaussian noise and impulse noise.Image storage includes the preservation of data and parameters of the original image and processed image.In the third module,the medical image is decomposed as four sub bands(LL,HL,LH,HH)by wavelet decomposition,where LL is low frequency,LH,HL,HH are respective for horizontal,vertical and in the diagonal line high frequency component.Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients,the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing.The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction.The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better,not a simple combination of the two previous methods.With DWT and improved median filter coefficients coupling denoising,it is highly practical for high-precision medical images containing complex noises.The experimental results of proposed algorithm are compared with the results of median filter,wavelet transform,contourlet and DT-CWT,etc.According to visual evaluation index PSNR and SNR and Canny edge detection,in low noise images,PSNR and SNR increase by 10%–15%;in high noise images,PSNR and SNR increase by 2%–6%.The experimental results of the proposed algorithm achieved better acceptable results compared with other methods,which provides an important method for the diagnosis of medical condition.

An Adaptive Median Filter Algorithm Based on B-spline Function

According to the B-spline convolution mask, first, the contrast sensitiveness （CS） is computed and then is viewed as a noise sensitiveness coeficient （NSC） to adaptively determine a noise-recognized threshold value. Based on the noise density function （NDF） in a 3×3 window, the filtering window size is adaptively adjusted, and then a median filter is used to eliminate the noise-marked pixels. The experiment results show that the proposed algorithm can preserve image detail information well and effectively remove the noises, particularly the impulse noises that is also called salt-and-pepper noises superimposed on the computed tomography （CT） and magnetic resonance imaging （MRI） medical images.

Digital Filter Algorithm Based on Morphological Lifting Scheme and Median Filter

Abstract--When the circuits in which electronic products are fitted are disturbed by various interrupting signals, wave distortions occur to the normal voltage signals of these circuits. These wave distortions influence the normal operation and life cycle of electronic products. To eliminate the harmful effects of interrupting signals on electronic products, in this paper, a digital filter algorithm based on morphological lifting scheme and median filter （MLS-MF）, which will be used to filter various interrupting signals existing in the circuits in which electronic products are fitted, is proposed. A variety of interrupting sig- nals have been included in simulation studies, and simulation results have demonstrated the effectiveness and feasibility of the proposed digital filter algorithm in high frequency continuous interference, random background noise and damped oscillatory transient interference filter. Index Terms--Digital filter, lifting scheme, median filter, mor- phology.

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.

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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.

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.

Some Problems of 2D Morphological and Median Filters

In this paper we mainly discussed some problems of 2D morpnological and median filters .The differences between 1D and 2D morphological and median filters arc also described. It can be seen that many propcrties of ID finers arc invalid for 2D filters. Som cxamples and cxpcriments are gived to show these problems.

Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer(BCa)is a leading cause of death in the female population across the globe.Approximately 2.3 million new BCa cases are recorded globally in females,overtaking lung cancer as the most prevalent form of cancer to be diagnosed.However,the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries.Early diagnosis is the only option to minimize the risks of BCa.Deep learning(DL)-based models have performed well in image processing in recent years,particularly convolutional neural network(CNN).Hence,this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images.The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection.This proposed model is a multi-view-based computer-aided diagnosis(CAD)model,which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique(MLO)and cranio-caudal(CC).The digital mammogram images are collected from the digital database for screening mammography(DDSM)dataset.In preprocessing,median filter and contrast limited adaptive histogram equalization(CLAHE)techniques are utilized for image enhancement.After preprocessing,the segmentation is performed using the region growing(RG)algorithm.The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients(PHOG)and the AlextNet model.Finally,the classification is performed using the weighted k-nearest neighbor(WkNN)optimized with sequential minimal optimization(SMO).The classified images are evaluated based on accuracy,recall,precision,specificity,f1-score,and mathews correlation coefficient(MCC).Additionally,the false positive and error rates are evaluated.The proposed model obtained 98.57%accuracy,98.61%recall,99.25%specificity,98.63%precision,97.93%f1-score,96.26%MCC,0.0143 error rate,and 0.0075 false positive rate(FPR).Compared to the existing models,the research model has obtained better performances and outperformed the other models.

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

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.

Wavelet domain denoising method based on multistage median filtering

There are two main problems in the threshold denoising method based on wavelet transform. One is the difficulty of threshold selection, and the other is the inconsistence of the dip and curved events in the low signal-to-noise ratio （SNR） seismic data after denoising. In image denoising, multistage median filtering can preserve the details of the signal. So we proposed a denoising algorithm in wavelet transform domain based on multistage median filtering. Using this method the flat region and the edge region are differentiated by the difference between the maximum mid-value and the minimum mid-value, which preserves the details, thus improves the denoising effect. The simulation data and the real data processing results reveal that this method has stronger ability in separating signal from noise than that of the threshold denoising method.

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.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

DENOISING METHODS IN INTERFEROMETRIC SAR IMAGES PROCESSING

Thermal and “Speckle” noise is an obstacle to generate the digital elevation model(DEM) from interferogram by 2 D SAR images. According to the theory of interferometry, the main sources of noise in interferogram are discussed. On the basis of the character of noise in the interferogram, the low pass filter, median filter and wavelet transform are investigated. Wavelet transform is forwarded as the most effective method to eliminate the InSAR noise because it can keep the resolution of the images during eliminating the noise. The raw data verify the validity and effectiveness of wavelet transform.

Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

The enlarged veins in the pampiniform venous plexus,known as varicocele disease,are typically identified using ultrasound scans.Themedical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient.The proposed system is designed to determine whether a patient is affected.Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions.Therefore,the experimental results of this study focused on images taken in the supine position of the left testicles of patients.There are two possible vein structures in each image:a cross-section(circular)and a tube(non-circular)structure.This proposed system identifies dilated(varicocele)veins of these structures in ultrasound images in three stages:preprocessing,processing,and detection and measurement.These three stages are applied in three different color modes:Grayscale,Red-Green-Blue(RGB),and Hue,Saturation,and Value(HSV).In the preprocessing stage,the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation.Then,the processing stage employs different filters to perform image denoising.Finally,edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist.Overall implementation results showed the proposed system is faster andmore effective than the previous work.