Pixel Gray Value Research Articles

An Expression Feature Extraction Method Based on Improved LBP with Wavelet Decomposition

Facial expression feature extracted isn’t exquisite enough with the aid of original LBP (Local Binary Pattern) operator, merely focusing on differences between the center pixel gray value and the neighborhood pixel gray value. MLBP (Multiple Local Binary Patterns) is proposed in this paper, making some improvements to LBP algorithm. A novel approach to facial expression recognition based on MLBP operator with wavelet decomposition is put forward, effectively reducing the dimensionality of facial expression feature vector. Experimental results show that the method presented can efficaciously improve recognition rate.

A Global-Local Contrast based Image Enhancement Technique based on Local Standard Deviation

Contrast Enhancement plays an important role in digital image processing. It enables the human and automated perceiver to find out the less obvious information in the original image. Global Contrast Enhancement techniques are used in a wide range of applications to enhance the quality of an image. Local contrast enhancement also plays an important role for a detailed visibility of an image. In this paper we tried to enhance the contrast of an image using Global enhancement technique complemented with local image contrast enhancement methods. This paper proposes a method to obtain good contrast image based on its local and global information and by changing the gray values of pixels according to the objective gray scaled image. General Terms Image Processing.

A New Modeling Method for Gray Feature

The Mean-Shift algorithm has very good tracking effect when the background is in a simple; but for a complex environment, tracking effect is not very ideal. Therefore, a new gray feature modeling method is proposed in this paper. Firstly, target in the tracking window is uniformly divided into even pieces. Then the pixel gray value of each block is calculated with subtraction of certain rules. Finally, the gray value of gray difference and the whole object value fusion are fused and established the object model. The object model that established not only contains the whole gray value information, but also contains the gray value differences between regions, has a more accurate description of the target, and then distinguish target from background better. The experiment results show that: the target model using the method in this paper to track based on the Mean-Shift algorithm, has good adaptability when the target is partially occluded and has better robustness for complex background.

Micron and Nanometer Measuring Methods Based on Metrological Scanning Electron Microscope

Micron and nanometer dimensional metrology is an important part of integrated circuits manufacturing system and nanofabrication research work. Semiconductor device feature size has shrunk from the micron to dozen of nanometers. How to watch the micro structure clearly, identify its edge, and measure its dimension accurately are the major issues that need to be research. In this paper, a metrological scanning electron microscope (M-SEM) system with precision stage and laser interferometer is presented. Furthermore, this paper focuses on demonstrating a metrological SEM image edge detection algorithm which is the essential part of the metrological SEM system to realize the measurement of line width. The procedure of the algorithm is that SEM image is first analyzed by noise type according to the histogram characteristics. Then the image noise is reduced by specific filters. Furthermore, a specific interesting object image region is selected and pixel gray level values of the region are obtained by line scan. SEM image edges are determined by the characteristic of the pixel information in two possible cases. This algorithm preserves the edge details of the original image and detects the image edge automatically in an easy and fast way.

A Secure and Efficient Image Encryption Scheme Based on Tent Map and Permutation-substitution Architecture

A secure image encryption scheme based on 2D skew tent map is proposed for the encryption of color images. The proposed encryption scheme is composed of one permutation process and one substitution process. The 3D color plain-image matrix is converted to 2D image matrix first, then 2D skew tent map is utilized to generate chaotic sequences, which are used for both permutation process and substitution process. The chaotic sequence for permutation process is dependent on plain- image and cipher keys, resulting in good key sensitivity and plaintext sensitivity. The substitution process is first initiated with the initial vectors generated by the cipher keys and 2D skew tent map, then the gray values of row and column pixels of 2D image matrix are mixed with the pseudorandom number sequences via bitxoring operation. Both permutation process and substitution process are executed row-by-row and column-by-column instead of pixel-by-pixel to improve the speed of encryption. The security and performance of the proposed image encryption have been analyzed, including histograms, correlation coefficients, information entropy, key sensitivity analysis, key space analysis, differential analysis, encryption/decryption rate analysis etc. All the experimental results suggest that the proposed image encryption scheme is robust and secure and can be used for secure image and video communication applications. Index Terms—Chaotic system, skew tent map, image encryption, permutation-substitution architecture

An Image Encryption Scheme Using 2D Generalized Sawtooth Maps

In this paper, a new image encryption scheme based on 2D generalized sawtooth map is proposed. Utilizing the chaoticnature of 2D generalized sawtooth maps, image pixel positions are scrambled and image pixels gray values are changedto encrypt the plain-images. Experimental results have been carried out with detailed analysis to demonstrate that theproposed image encryption scheme possesses large key space to resist brute-force attack and possesses good statisticalproperties to frustrate statistical analysis attacks.

The analysis of in-air reverberation patterns from medical ultrasound transducers.

National guidelines for routine ultrasound quality assurance include the measurement of transducer sensitivity using the in-air reverberation pattern generated from a transducer operating in air. The in-air reverberation method has been compared with other well-known measures of transducer sensitivity, such as the Sonora FirstCall probe tester (Sonora Medical Systems Inc, Longmont, CO, USA) and phantom-based images. There is good agreement between the in-air reverberation images and the Sonora FirstCall probe data for individual crystal sensitivity. However, the in-air reverberation approach is cheaper and easier to implement for linear and curvilinear transducers. The methods used for measuring the in-air reverberation pattern and the influence of scanner parameters such as gain and output power have been investigated. In general, reliance on a visual assessment of reverberation depth can lead to inconsistencies in the measurement of reverberation depth, when taken over a period of several months. The value of scanner parameters, in particular scanner gain, is also important when trying to measure changes in reverberation depth due to changes in transducer performance. A more accurate assessment of changes in transducer sensitivity, using the in-air reverberation method, is found by measuring the peak pixel grey scale values within a chosen reverberation band in the image. This quantitative approach can be taken a step further by assessing changes within the whole in-air reverberation pattern, by applying a two-dimensional cross correlation between two reverberation images to assess changes in transducer performance.

A Median Filtering Algorithm Based on Extremum Detection

In this paper a improved algorithm of median filtering based on extremum detection is introduced. According to the deficiencies of the extreme median filtering method in the two stages of noise detection and noise filtering, Adds a false detection noise pixel gray value correction recovery process. Experimental results show that the algorithm in this paper are superior than traditional median filtering algorithm and some improved image filtering algorithm in noise removal and edge retention.

A Novel Image Encryption Scheme Based on Generalized Multi-sawtooth Maps

In this paper, a generalized multi-sawtooth map based image encryption scheme with an efficient permutation-diffusion mechanism is proposed. In the permutation process, a generalized multi-sawtooth map is utilized to generate one chaotic orbit used to get one index order sequence for the permutation of image pixel positions, while in the diffusion process, two generalized multi-sawtooth maps are employed to yield two pseudo-random grey value sequences for a two-way diffusion of pixel grey values. The yielded grey value sequences are not only sensitive to the control parameters and initial conditions of the considered chaotic maps, but also strongly depend on the plain-image processed, therefore the proposed scheme can effectively resist statistical attack, differential attack, known-plaintext as well as chosen-plaintext attack. Experimental results show that the new image encryption scheme has satisfactory security thanks to its large key space and robust permutation-diffusion mechanism, which makes it a potential candidate for designing image encryption schemes.

Pore-scale dynamics of salt transport and distribution in drying porous media

Understanding the physics of water evaporation from saline porous media is important in many natural and engineering applications such as durability of building materials and preservation of monuments, water quality, and mineral-fluid interactions. We applied synchrotron x-ray micro-tomography to investigate the pore-scale dynamics of dissolved salt distribution in a three dimensional drying saline porous media using a cylindrical plastic column (15 mm in height and 8 mm in diameter) packed with sand particles saturated with CaI2 solution (5% concentration by mass) with a spatial and temporal resolution of 12 μm and 30 min, respectively. Every time the drying sand column was set to be imaged, two different images were recorded using distinct synchrotron x-rays energies immediately above and below the K-edge value of Iodine. Taking the difference between pixel gray values enabled us to delineate the spatial and temporal distribution of CaI2 concentration at pore scale. Results indicate that during early stages of evaporation, air preferentially invades large pores at the surface while finer pores remain saturated and connected to the wet zone at bottom via capillary-induced liquid flow acting as evaporating spots. Consequently, the salt concentration increases preferentially in finer pores where evaporation occurs. Higher salt concentration was observed close to the evaporating surface indicating a convection-driven process. The obtained salt profiles were used to evaluate the numerical solution of the convection-diffusion equation (CDE). Results show that the macro-scale CDE could capture the overall trend of the measured salt profiles but fail to produce the exact slope of the profiles. Our results shed new insight on the physics of salt transport and its complex dynamics in drying porous media and establish synchrotron x-ray tomography as an effective tool to investigate the dynamics of salt transport in porous media at high spatial and temporal resolution.

Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning

Simple nearest neighbor classification fails to exploit the additional information in image sets. We propose self-regularized nonnegative coding to define between set distance for robust face recognition. Set distance is measured between the nearest set points (samples) that can be approximated from their orthogonal basis vectors as well as from the set samples under the respective constraints of self-regularization and nonnegativity. Self-regularization constrains the orthogonal basis vectors to be similar to the approximated nearest point. The nonnegativity constraint ensures that each nearest point is approximated from a positive linear combination of the set samples. Both constraints are formulated as a single convex optimization problem and the accelerated proximal gradient method with linear-time Euclidean projection is adapted to efficiently find the optimal nearest points between two image sets. Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition. The proposed algorithm works independently of the chosen features and has been tested on gray pixel values and local binary patterns. Experiments on three standard data sets show that the proposed method consistently outperforms existing state-of-the-art methods.

Local enhancement method and its applications to UAV image matching

The quality of UAV image is closely related to light,imaging angle and the feature of the ground object. There are some shortcomings in many images,such as poor visual contrast,large distortion and insufficient resolution,which cause many difficulties in subsequent image matching and orthorectification. To obtain better results in UAV image matching,the authors first calculated the degree of dispersion between gray value of pixel and image gray average to divide the image into different areas according to the discrete gray information of original images. Then,the distance weighted interpolation method was used to calculate the transformation function and enhanced the various regions in different degrees. After that,the histogram of enhanced image was corrected. Finally,the epipolar constraint method was used in the image matching experiment. The results show that,because of the enhanced image gray value gradient difference,there exists some extent of increased matching success rate and uniformity in the woodland area which has smaller gray change,thus favoring the smooth progress of the work of subsequent image pro- cessing.

Research and Development of Non Multi-Scale to Pixel-Level Image Fusion

Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

A Moving Object Detection Algorithm Based On Multiple Judgments

In the field of moving object detection, the traditional background subtraction method is used broadly, which seems more sensitive to light and shows poor anti-interference performance. Background modeling is the key step of background subtraction method. The Local Binary Pattern (LBP) algorithm is considered to put texture information into the background model, combining color and texture information and an improved background subtraction method proposed. In addition, a new method is proposed combining the inter-frame difference method with improved background subtraction method in this paper. It can overcome traditional methods only using the pixel gray value changes for moving targets detection. The method makes use of dual-threshold to detect moving targets and makes multiple judgments. It not only uses the change of pixel gray value to detect moving targets, but also takes advantage of the number of changed pixels to detect moving targets which we are interested in. The experiments show that the algorithm proposed is adopted to detect the moving target accurately and can resist interferences brought about by the slow slight movements in the scene with better robustness. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3338

Quantitative pixel grey measurement of the “high-risk” sign, darkening of third molar roots: a pilot study

Our aim was to examine the panoramic darkening of the root, which is a "high-risk" sign, using quantitative measurements of pixel grey values to determine different aetiological backgrounds, namely inferior alveolar nerve (IAN) exposure with or without groove formation of the third molar roots or thinning/fenestration of the lingual cortex (LCTF). 38 impacted third molars that had been surgically removed and had darkened roots on panoramic radiographs were included in this retrospective case-control study. 15 IAN exposure cases were selected for the case group, and 23 cases with proven lingual cortical thinning or fenestration were chosen for the control group. The mean pixel grey values of selected areas in the dark band (D) and control areas within the same roots (R) were determined with the ImageTool (University of Texas Health Science Center, San Antonio, TX) software. The differences in pixel values (R-D) of the IAN and LCTF groups were analysed using the Mann-Whitney U-test and Pearson's χ(2) test. The medians of the R-D pixel values were 45.7 in the IAN group and 34.3 in the LCTF group, whereas the interquartile ranges were 12.0 (IAN) and 18.3 (LCTF) (p<0.001). The R-D critical value at which the outcomes differed significantly was 38. If the differences in pixel grey values (R-D) were higher than 38, the chance of IAN exposure was approximately 32 times higher than the chance of LCTF (χ(2) test, p < 0.001; odds ratio, 32.0; 95% confidence interval, 3.5-293.1). The pre-operative prediction of IAN exposure or lingual cortical thinning in cases with "darkening" is possible based on pixel grey measurements of digital panoramic radiographs.

A Semi-Automated Statistical Algorithm for Object Separation

We explicate a semi-automated statistical algorithm for object identification and segregation in both gray scale and color images. The algorithm makes optimal use of the observation that definite objects in an image are typically represented by pixel values having narrow Gaussian distributions about characteristic mean values. Furthermore, for visually distinct objects, the corresponding Gaussian distributions have negligible overlap with each other and hence the Mahalanobis distance between these distributions are large. These statistical facts enable one to sub-divide images into multiple thresholds of variable sizes, each segregating similar objects. The procedure incorporates the sensitivity of human eye to the gray pixel values into the variable threshold size, while mapping the Gaussian distributions into localized \delta-functions, for object separation. The effectiveness of this recursive statistical algorithm is demonstrated using a wide variety of images.

A Maximum-Minimum Filtering Algorithm Base on Threshold

In allusion to the contradiction between salt-and-pepper noise attenuation and image detail-preserving, a maximum-minimum filtering algorithm base on threshold is proposed in this paper. The gray values of the pixels in the filtering windows are given firstly. If the test pixel value is the extreme value of the filtering windows, this pixel is a suspicious noise point. And then sort the absolute value of the difference between the suspicious noise point value and pixels gray value in its neighborhood. Judge the pixel is a real noise point according to the average value of the middle two values in the difference sequence. The original gray value of noise point is determined by the maximum-minimum relationship between the pixel values of the neighborhood. If the pixel is not a noise point, its value is kept. The simulation results show that the proposed algorithm has greatly impact on signal to noise ratio and mean square error which retains more image details while making sure to remove noise.

An Image Hiding Scheme Using 3D Sawtooth Map and Discrete Wavelet Transform

An image encryption scheme based on the 3D sawtooth map is proposed in this paper. The 3D sawtooth map is utilized to generate chaotic orbits to permute the pixel positions and to generate pseudo-random gray value sequences to change the pixel gray values. The image encryption scheme is then applied to encrypt the secret image which will be imbedded in one host image. The encrypted secret image and the host image are transformed by the wavelet transform and then are merged in the frequency domain. Experimental results show that the stego-image looks visually identical to the original host one and the secret image can be effectively extracted upon image processing attacks, which demonstrates strong robustness against a variety of attacks.

Anew method for speeding up FractalCompression using different value of affine transformation and highest gray value of pixel

In this paper new algorithm for speeding up fractal image compression  is presented. A new adapted method based on computing the highest value of the pixel of the image to reduce the computational complexity in the encoder stage and which are led to decreasing the encoding time while the reconstructed image from the work as good as we want. For increasing the effectiveness of search stage we used another type of partitioning method that led to increase the flexibility of range partition, this method is HV-partition. We applied this method on images and also present a comparison of this method against other method which used to speed the fractal compression.

A Framework to Protect Patient Digital Medical Imagery for Secure Telediagnosis

Telediagnosis involves sharing, distribution and transmission of patient's digital medical images over public/shared networks for real-time diagnosis with the help of medical experts at distant locations. It raises security concerns to protect digital MRI, CT scan, X-ray angiography, etc. images from unauthorized access, alteration and usage. To circumvent the security and privacy problems of these tele-radiological images for avoiding any misdiagnosis, a security framework is proposed. Proposed framework provides visual protection in a way to withstand the statistical attacks pertinent to digital medical imagery and harnesses the features of tent map/4D hyper-chaotic Chen system to fetch high security. In which, all the pixels of image are shuffled through permutation indices vectors generated from tent maps and then the pixels gray values are masked through diffusion process done using hyper-chaotic Chen system for improving overall security. The exact original content of images can be restored without any loss of information using correct secret key. The simulation analyses validate the efficacy and practicability of the proposed framework for medical images protection