Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Color Image Steganalysis Based on Residuals of Channel Differences

This study proposes a color image steganalysis algorithm that extracts highdimensional rich model features from the residuals of channel differences.First,the advantages of features extracted from channel differences are analyzed,and it shown that features extracted in this manner should be able to detect color stego images more effectively.A steganalysis feature extraction method based on channel differences is then proposed,and used to improve two types of typical color image steganalysis features.The improved features are combined with existing color image steganalysis features,and the ensemble classifiers are trained to detect color stego images.The experimental results indicate that,for WOW and S-UNIWARD steganography,the improved features clearly decreased the average test errors of the existing features,and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms.Specifically,when the payload is smaller than 0.2 bpc,the average test error decreases achieve 4%and 3%.

The Differences between Chinese and Western Cultures based on the Animal Images in Idioms

Chinese idioms,like English proverbs,are a language form,with a certain philosophical motive and ideological basis,given meaning through people's lifelong experiences.Throughout the ages,a wide range of beliefs,customs and natural scenery have been condensed into text forms in both Chinese idioms and English proverbs that display certain aspects of the essence of the two cultures.This paper focuses on the study of animal images in idioms,and elaborates on the different interpretations of specific images caused by the differences in history,regions,customs,and values of the two cultures.The purpose of this research is to provide a breakthrough more relevant to students who devote themselves to the study and research of Chinese and English languages and culture from the perspective of cultural contrast and analysis,so as to make a more thorough understanding of differences between Chinese and Western customs,culture and ideology.Therefore,language translations and cultural communications can be improved through a more accurate understanding of the Chinese and Western cultures.

A Moving IR Point Target Detection Algorithm Based on Reverse Phase Feature of Neighborhood in Difference Between Neighbor Frame Images

An algorithm for detecting moving IR point target in complex background is proposed, which is based on the Reverse Phase Feature of Neighborhood （RPFN） of target in difference between neighbor frame images that two positions of the target in the difference image are near and the gray values of them are close to in absolute value but with inverse sign. Firstly, pairs of points with RPFN are detected in the difference image between neighbor frame images, with which a virtual vector graph is made, and then the moving point target can be detected by the vectors＇ sequence cumulated in vector graphs. In addition, a theorem for the convergence of detection of target contrail by this algorithm is given and proved so as to afford a solid guarantee for practical applications of the algorithm proposed in this paper. Finally, some simulation results with 1000 frames from 10 typical images in complex background show that moving point targets with SNR not lower than 1.5 can be detected effectively.

A Block Difference Compression Algorithm for Digital Image Supervision

A block difference compression algorithm based on block PSNR, Which is one of the parameters of image quality is presented for image sequence processing. This algorithm adopts classical JPEG method in intra-frame coding, and processes 8×8 blocks of inter-frame with different methods depending on the results from the current PSNR compared with a threshold. The calculation of threshold and the situation of error accumulation based on different thresholds as well as the structure of code stream are also presented. The advantage of this algorithm is the reduction of the large operation volume during inter-frame processing.

Mapping the Dyke Swarms Emplaced within the Different Archean Cratons of the Indian Shield Using Google^(TM) Earth Images and Arc GIS^(TM) Techniques

The Indian shield comprises a number of Archean–Paleoproterozoic cratonic blocks and predominantly Meso–Neoproterozoic mobile belts with Archean protoliths.All these ancient cratons were thought to be integral parts of

Loss of Image in Translating Chinese Poem into English

The process of translating Chinese poem into English is actually the process of the transference of image.Image is the soul of poetry.In translating the poem there often exists a phenomenon that is loss of image.So there are some questions to be discussed,such as:the definition of image,the reasons of loss of image,and the way to avoid losing image.This thesis will discuss the questions from three aspects:language difference,culture difference and factors of translator.

An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

Colorimetric characterization of imaging device by total color difference minimization

Colorimetric characterization is to transform the device-dependent responses to device-independent colorimetric values, and is usually conducted in CIEXYZ space. However, the optimal solution in CIEXYZ space does not mean the mini-mization of perceptual error. A novel method for colorimetric characterization of imaging device based on the minimization of total color difference is proposed. The method builds the transform between RGB space and CIELAB space directly using the downhill simplex algorithm. Experimental results showed that the proposed method performs better than traditional least-square (LS) and total-least-square (TLS) methods, especially for colors with low luminance values.

Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources

Borehole-to-surface electrical imaging （BSEI） uses a line source and a point source to generate a stable electric field in the ground. In order to study the surface potential of anomalies, three-dimensional forward modeling of point and line sources was conducted by using the finite-difference method and the incomplete Cholesky conjugate gradient （ICCG） method. Then, the damping least square method was used in the 3D inversion of the formation resistivity data. Several geological models were considered in the forward modeling and inversion. The forward modeling results suggest that the potentials generated by the two sources have different surface signatures. The inversion data suggest that the low- resistivity anomaly is outlined better than the high-resistivity anomaly. Moreover, when the point source is under the anomaly, the resistivity anomaly boundaries are better outlined than when using a line source.

Real-time digital image stabilization based on regional field image gray projection

Digital image stabilization technique plays important roles in video surveillance and object acquisition.Many useful electronic image stabilization algorithms have been studied.A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions,respectively.For the odd field image channel,motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves.Then motion compensations can be obtained after using the Kalman filter method.Finally,the odd field image is adjusted according to the compensations.In the mean time,motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame.By minimizing absolute difference between odd and even field image gray projection curves of the current frame,the inter-field motion parameters can be estimated.Therefore,the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations.Finally,the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images.Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance.In addition,image blurring can be improved.

Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting

The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens.As a result,it is difficult to use the obtained image information.To make use of the effective information in the image,these distorted imagesmust first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities.To solve this problem,this study presents an adaptive classification fitting method for fish-eye image correction.The degree of distortion in the image is represented by the difference value of the distances fromthe distorted point and undistorted point to the center of the image.The target points selected in the image are classified by the difference value.In the areas classified by different distortion differences,different parameter curves were used for fitting and correction.The algorithm was verified through experiments.The results showed that this method has a substantial correction effect on fish-eye images taken by different fish-eye lenses.

A New Quality Improving Scheme for VQ Decompressed Images Based on DWT

The better compression rate can be achieved by the traditional vector quantization （VQ） method, and the quality of the recovered image can also be accepted. But the decompressed image quality can not be promoted efficiently, so how to balance the image compression rate and image recovering quality is an important issue, in this paper, an image is transformed by discrete wavelet transform （DWT） to generate its DWT transformed image which can be compressed by the VQ method further. Besides, we compute the values between the DWT transformed image and decompressed DWT transformed image as the difference matrix which is the adjustable basis of the decompressed image quality. By controlling the deviation of the difference matrix, there can be nearly Iossless compression for the VQ method. Experimental results show that when the number of compressed bits by our method is equal to the number of those bits compressed by the VQ method, the quality of our recovered image is better. Moreover, the proposed method has more compression capability comparing with the VQ scheme.

Color Image Quality Assessment Based on Structural Similarity

It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.

Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state functional MRI

Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.

A Cryptograph Domain Image Retrieval Method Based on Paillier Homomorphic Block Encryption

With the rapid development of information network,the computing resources and storage capacity of ordinary users cannot meet their needs of data processing.The emergence of cloud computing solves this problem but brings data security problems.How to manage and retrieve ciphertext data effectively becomes a challenging problem.To these problems,a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper.This can be described as follows:According to the Paillier encryption technology,the image owner encrypts the original image in blocks,obtains the image in ciphertext domain,then passes it to the third party server.The server calculates the difference histogram of the image in ciphertext domain according to the public key and establishes the index database.The user passes the retrieved image to the server.The server computes the differential histogram of the retrieved image by public key.Then,compares the similarity of it with the histogram in index database and selects larger similarity images in ciphertext and send them to the user.The user obtains the target image with the private key.The experimental results show that the method is feasible and simple.

Susceptibility-weighted imaging is suitable for evaluating signal strength in different brain regions of a rabbit model of acute hemorrhagic anemia

Acute hemorrhagic anemia can decrease blood flow and oxygen supply to brain, and affect its physiological function. While detecting changes in brain function in patients with acute hemorrhagic anemia is helpful for preventing neurological complications and evaluating therapeutic effects, clinical changes in the nervous systems of these patients have not received much attention. In part, this is because current techniques can only indirectly detect changes in brain function following onset of anemia, which leads to lags between real changes in brain function and their detection.

IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model, gray level l , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.