Gray Gradient Research Articles

Alanyl‑glutamine prophylactically protects against lipopolysaccharide‑induced acute lung injury by enhancing the expression of HSP70.

At present, the timing of glutamine administration remains controversial. The aim of the present study was to confirm the early protective mechanisms of alanyl‑glutamine (Ala‑Gln) against lipopolysaccharide (LPS)‑induced lung injury, as well as to detect the best time for Ala‑Gln usage. A total of 60 adult Wistar rats were randomly divided into 6groups: The control group (C), the LPS‑induced shock group (LPS), the pre‑Ala‑Gln treated group (A1) and the pre‑Gln treated group (G1), which separately received 4.5% Dipeptiven and 3% glutamine just before LPS administration; the post‑Ala‑Gln treated group (A2) and the post‑Gln treated group (G2), which was respectively infused with 4.5% Dipeptiven and 3% glutamine at 1h following LPS. Survival rates were observed at 6h following the LPS injection. Blood samples were drawn for analysis of cytokine levels 1h prior to (T0) and 6h following (T1) LPS injection. All rats were killed at T1 and the pulmonary samples were collected. Plasma concentrations of tumor necrosis factor‑α, interleukin (IL)‑1β and IL‑8 at T0 and T1, apoptosis in lung epithelial cells and the expression of heat shock protein (HSP)70 were detected. The lung wet/dry weight ratio (W/D) and the content of protein in the bronchoalveolar lavage fluid (BALF) were also determined. Survival rates at 6h following (T1) LPS administration were both 100% in groups A1 and G2, but 70% in A2 and G2 groups. The W/D, the content of protein in BALF and cytokine levels were significantly lower in groups A1 and G1 than that in group LPS (P<0.05) at T1. The apoptosis index of both alveolar and bronchial epithelial cells was obviously lower in A1 and G1 groups than that in the LPS group (P<0.05). Gray gradients of HSP70 in the A1 and G1 groups were dramatically higher than those of group LPS (P<0.05). In conclusion, pre‑administration of Ala‑Gln just before LPS can effectively protect the lung by enhancing HSP70 expression, but delayed administration cannot protect LPS‑induced lung injury.

Precise locating approach of the beacon based on gray gradient segmentation interpolation in satellite optical communications.

Accurate location computation for a beacon is an important factor of the reliability of satellite optical communications. However, location precision is generally limited by the resolution of CCD. How to improve the location precision of a beacon is an important and urgent issue. In this paper, we present two precise centroid computation methods for locating a beacon in satellite optical communications. First, in terms of its characteristics, the beacon is divided into several parts according to the gray gradients. Afterward, different numbers of interpolation points and different interpolation methods are applied in the interpolation area; we calculate the centroid position after interpolation and choose the best strategy according to the algorithm. The method is called a "gradient segmentation interpolation approach," or simply, a GSI (gradient segmentation interpolation) algorithm. To take full advantage of the pixels of the beacon's central portion, we also present an improved segmentation square weighting (SSW) algorithm, whose effectiveness is verified by the simulation experiment. Finally, an experiment is established to verify GSI and SSW algorithms. The results indicate that GSI and SSW algorithms can improve locating accuracy over that calculated by a traditional gray centroid method. These approaches help to greatly improve the location precision for a beacon in satellite optical communications.

A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

Effective star tracking method based on optical flow analysis for star trackers.

Benefiting from rapid development of imaging sensor technology, modern optical technology, and a high-speed computing chip, the star tracker's accuracy, dynamic performance, and update rate have been greatly improved with low power consumption and miniature size. The star tracker is currently one of the most competitive attitude measurement sensors. However, due to restrictions of the optical imaging system, difficulties still exist in moving star spot detection and star tracking when in special motion conditions. An effective star tracking method based on optical flow analysis for star trackers is proposed in this paper. Spot-based optical flow, based on a gray gradient between two adjacent star images, is analyzed to distinguish the star spot region and obtain an accurate star spot position so that the star tracking can keep continuous under high dynamic conditions. The obtained star vectors and extended Kalman filter (EKF) are then combined to conduct an angular velocity estimation to ensure region prediction of the star spot; this can be combined with the optical flow analysis result. Experiment results show that the method proposed in this paper has advantages in conditions of large angular velocity and large angular acceleration, despite the presence of noise. Higher functional density and better performance can be achieved; thus, the star tracker can be more widely applied in small satellites, remote sensing, and other complex space missions.

Fingerprint Liveness Detection Using Difference Co-occurrence Matrix Based Texture Features

Fingerprint authentication systems have been widely deployed in both civilian and government applications, however, whether fingerprint authentication systems is security or not has been an important issue under fraudulent attempts through artificial spoof fingerprints. In this paper, inspired by popular feature descriptors such as gray level co-occurrence matrix (GLCM) and Gradient (difference matrix (DM)), we propose a novel software-based fingerprint liveness detection algorithm called difference co-occurrence matrix (DCM). In doing so, quantization operation is firstly conducted on the images. DMs are constructed by calculating difference matrices of horizontal and vertical pixel values of images; difference co-occurrence arrays are constructed from the difference matrices between adjacent pixels. To reduce the influence of abnormal pixel values, truncation is used for DMs. Then, we compute four parameters (Angular Second Moment, Entropy, Inverse Differential Moment and Correlation) used as feature vectors of fingerprint images. For the first time in the fingerprint liveness detection, we construct eight difference co-occurrence matrices and extract texture features from processed DCMs. Finally, SVM classifier is used to predict classification accuracy. The experimental results reveal that our proposed method can achieve more accurate classification compared with the best algorithms of 2013 Fingerprint Liveness Detection Competition, while being able to recognize spoofed fingerprints with a better degree of accuracy.

An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques

The conventional two dimensional (2-D) histogram based Otsu’s method gives unreliable results while considering multilevel thresholding of brain magnetic resonance (MR) images, because the edges of the brain regions are not preserved due to the local averaging process involved. Moreover, some of the useful pixels present inside the off-diagonal regions are ignored in the calculation. This article presents an evolutionary gray gradient algorithm (EGGA) for optimal multilevel thresholding of brain MR images. In this paper, more edge information is preserved by computing 2-D histogram based gray gradient. The key to our success is the use of the gray gradient information between the pixel values and the pixel average values to minimize the information loss. In addition, the speed improvement is achieved. Theoretical formulations are derived for computing the maximum between class variance from the 2-D histogram of the brain image. A first-hand fitness function is suggested for the EGGA. A novel adaptive swallow swarm optimization (ASSO) algorithm is introduced to optimize the fitness function. The performance of ASSO is validated using twenty three standard Benchmark test functions. The performance of ASSO is better than swallow swarm optimization (SSO). The optimum threshold value is obtained by maximizing the between class variance using ASSO. Our method is tested using the standard axial T2 − weighted brain MRI database of Harvard medical education using 100 slices. Performance of our method is compared to the Otsu’s method based on the one dimensional (1-D) and the 2-D histogram. The results are also compared among four different soft computing techniques. It is observed that results obtained using our method is better than the other methods, both qualitatively and quantitatively. Benefits of our method are – (i) the EGGA exhibits better objective function values; (ii) the EGGA provides us significantly improved results; and (iii) more computational speed is achieved.

Crack detection in magnetic tile images using nonsubsampled shearlet transform and envelope gray level gradient

A novel algorithm based on nonsubsampled shearlet transform (NSST) and envelope gray level gradient (EGLG) is proposed for detecting low-contrast cracks in variably illuminated magnetic tile images. The algorithm first adopted NSST to decompose the original images into multiple subbands at different directions and scales. Then a novel column filtering based on EGLG was employed to remove the uneven background in the approximation subband, and a shearlet coefficient variance discriminator (SCVD) was used to eliminate interferences of noise and textures in the detail subbands. Finally the cracks were extracted from the reconstructed images. To verify the validity of the proposed algorithm, extensive experiments were conducted in a novel machine vision based system and its comparison with traditional algorithms was given. Experimental results show that this method achieves an accuracy rate of 95.5% in detecting cracks longer than 0.9mm with an average runtime of 0.576s, and outperforms traditional methods in terms of accuracy and robustness.

Tree Image Segmentation Based on an Improved Two-Dimensional Otsu Algorithm

Influenced by light, houses, street lamps and other factors, tree images often contain noise and complex background information. The existing Otsu segmentation algorithms have deficiencies such as poor anti-noise capability, ignoring the class cohesion and so on. Based on the traditional gray value-neighborhood average gradient Otsu segmentation method, we propose an improved two-dimensional Otsu algorithm for tree image segmentation. The algorithm takes into account the between-class distance and within-class distance, which combined the average variance concept of two categories and proposed new threshold selection method, and reduce the interference of noise effectively. To achieve the best segmentation and reduce over-segmentation of background information, a method of removing small areas and morphological processing are used to optimize segmentation results. Experimental results show that the proposed algorithm has a good inhibition effect on noise and the effect of tree image segmentation is better than that of the traditional one.

Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition

Image retrieval is a fundamental issue in pattern recognition. In this work, lateral inhibition (LI) model is adopted as a pre-processing step, which widens the gray level gradients so as to facilitate the image retrieval scheme. In searching for a perfect match between a predefined template and a reference image, we adopt metaheuristic algorithms for good seach capability. Artificial bee colony (ABC) algorithm is a bio-inspired optimization technique, which imitates the foraging behavior of honey bee swarms. It is well known that the algorithm is good at exploration but poor at exploitation. We present balance-evolution artificial bee colony (BE-ABC) algorithm that aims to strike a balance between exploration and exploitation rather than just focusing on improving the latter. BE-ABC algorithm adaptively manipulates the search intensity at the exploration and exploitation stages during the iterations. Besides that, it incorporates an overall degradation procedure to prevent premature convergence. Simulation results confirm that BE-ABC algorithm is more capable than several state-of-the-art metaheuristic algorithms in this image retrieval scheme.

Photometric correction for an optical CCD-based system based on the sparsity of an eight-neighborhood gray gradient.

In an optical measurement and analysis system based on a CCD, due to the existence of optical vignetting and natural vignetting, photometric distortion, in which the intensity falls off away from the image center, affects the subsequent processing and measuring precision severely. To deal with this problem, an easy and straightforward method used for photometric distortion correction is presented in this paper. This method introduces a simple polynomial fitting model of the photometric distortion function and employs a particle swarm optimization algorithm to get these model parameters by means of a minimizing eight-neighborhood gray gradient. Compared with conventional calibration methods, this method can obtain the profile information of photometric distortion from only a single common image captured by the optical CCD-based system, with no need for a uniform luminance area source used as a standard reference source and relevant optical and geometric parameters in advance. To illustrate the applicability of this method, numerical simulations and photometric distortions with different lens parameters are evaluated using this method in this paper. Moreover, the application example of temperature field correction for casting billets also demonstrates the effectiveness of this method. The experimental results show that the proposed method is able to achieve the maximum absolute error for vignetting estimation of 0.0765 and the relative error for vignetting estimation from different background images of 3.86%.

Shape from shading and optical flow used for 3-dimensional reconstruction of endoscope image

Conclusion: The shape from shading and optical flow are implemented to three-dimensional (3D) reconstruction.Objective: Solve the problem of 3D reconstruction in endoscope imaging.Method: One of the fundamental approaches in the domain of computer vision is shape from shading. This algorithm was proposed to obtain the shape of an object from a single intensity image. Because of the severe conditions are required in shape from shading to reconstruct 3D surface. The photometric calibration is proposed from the view of image processing. The calibration is important for illumination-based visualization techniques such as SFS (shape-from-shading). However, the surface reconstruction from SFS is the relative variation in the grey gradient field. So the change from relative variation to absolute variation is necessary when the actual size of surroundings has to be known. Then the optical flow is introduced to solve this change in the paper. The optical tracker is also used in this system to capture the pose of endoscopy.Result: The stability of surface reconstruction is improved when the photometric calibration is used before shape from shading.

Object tracking from airborne video using particle filters algorithm on dynamic feature fusion

A particle filter object video tracking algorithm based on dynamic feature fusion is proposed in this paper. This algorithm uses the complementary features, which are gray histogram and gradient histogram, to represent the object model. In the process of tracking, the confidence for each feature is adjusted according to the discrimination between the object and the background, and the object model is dynamically established and updated online. The presented method can improve the accuracy of the object modeling and furthermore improve the accuracy of the particle filter tracking algorithm. Experimental results have demonstrated the effectiveness of our approach for airborne platform video.

Kernel sparse coding method for automatic target recognition in infrared imagery using covariance descriptor

Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets.

(Invited) Understanding Cathodes for Solid Acid Fuel Cells By Coupling FIB-SEM 3D Reconstruction with Mathematical Modeling

Fuel cells based on the crystalline proton-conducting electrolyte CsH2PO4 (CDP) typically operate between 230 °C and 260 °C1. In this intermediate temperature regime, so-called solid acid fuel cell (SAFC) anodes are tolerant to high concentrations of impurities2, are amenable to internal reforming of multiple fuels3, and may possibly omit platinum entirely4. These properties make SAFCs excellent candidates for stationary fuel cell systems operating on fuels such as methanol or natural gas. A longstanding barrier to the adoption of SAFC technology has been the low activity and high precious metal content of the cathode. In the state-of-the-art cathode architecture, a macroporous framework of the CDP electrolyte is decorated with a percolating network of platinum nanoparticles that acts as both the oxygen reduction catalyst and the sole electronic conductor in the electrode. Typical SAFC cathode Pt loadings are greater than 1.75 mg/cm2, an amount circumscribed by the minimum quantity needed to obtain sufficient electrical conductivity in the electrode5. Despite the success of the current SAFC cathode design, only limited work has been done to date to understand the structure-property relationships governing its performance. Here we address this problem using an approach coupling ex situmicroscopic characterization and mathematical modeling with experimental input from electrochemical cell testing. Porous SAFC cathodes were sectioned using FIB/SEM to render a 3D reconstruction of a representative electrode sub-volume. A machine-learning algorithm was used to automatically segment image stacks to address segmentation problems due to gray level gradients and overlapping gray level peaks in the image histogram. Numerical analysis was performed to extract electrode physical properties (i.e. porosity, tortuosity). Electrode physical parameters from the 3D electrode reconstruction were used as inputs to a 1D macrohomogenous model similar to those applied previously to PEM fuel cells6. The model was used to self-consistently fit SAFC polarization curves, yielding, among other quantities, an electrochemically active area for the electrode, which has been notoriously difficult to measure in the SAFC system. We comment on these and other findings, as well as the utility of the model in addressing the SAFC system given some of the categorical distinctions between it and the PEM system. Acknowledgments This work is supported by ARPA-E via cooperative agreement DE-AR0000499. References 1 S.M. Haile, C.R.I. Chisholm, Kenji Sasaki, D.A. Boysen, and T. Uda, Faraday Discuss. 134, 17 (2007). 2 C. R. I. Chisholm, Dane A. Boysen, A. B. Papandrew, Strahinja K Zecevic, Sukyal Cha, Kenji A Sasaki, Áron Varga, Konstantinos P. Giapis, and S.M. Haile, Electrochem. Soc. Interface 18, 53–59 (2009). 3 T. Uda, D.A. Boysen, C. R. I. Chisholm, and S.M. Haile, Electrochem. Solid-State Lett. 9, A261 (2006). 4 Alexander B. Papandrew, Robert W. Atkinson III, Raymond R. Unocic, and Thomas a. Zawodzinski, J. Mater. Chem. A 3, 3984–3987 (2015). 5 Alexander B. Papandrew, Calum R.I. Chisholm, Ramez A Elgammal, Mustafa M. Özer, and Strahinja K Zecevic, Chem. Mater. 23, 1659–1667 (2011). 6 T.E. Springer, T.A. Zawodzinski, and S Gottesfeld, J. Electrochem. Soc. 138, 2334 (1991).

Single camera volumetric shadowgraphy system for simultaneous droplet sizing and depth location, including empirical determination of the effective optical aperture

This work proposes an improvement of the shadowgraphy technique by allowing for 3-D droplet/particle location and sizing in a volumetric measurement with a single camera.The shadowgraphy technique implemented in this work uses a diffuse backward illuminated screen as background stage. Using this setup, the shadow image of droplets or particles is recorded on a camera. As particles get out of focus, apparent shape, size and visibility of the gray level profiles registered from the particle images change. Based on these effects, this work proposes an improved methodology to extract out-of-plane location information. Thus, the mentioned camera is enough for obtaining the 3D location of the shadow images of the particles/droplets in a control volume, and no additional hardware is needed.A simple geometrical optics model links the defocused particle image to the sizes of the circle of confusion and the particle itself. Based on this connection, a robust practical procedure is proposed to determine the circle of confusion size from the gray scale gradients of the particle image profile and from image contrast considerations. This knowledge of the circle of confusion size provides the particle depth location.The proposed approach is tested on real opaque images of a calibrated target. The proposal includes a practical procedure to double-check relevant optical parameters like the effective aperture of the lens setup. The empirical determination of the effective optical aperture is essential as it may significantly deviate from the nominal expected value, depending on the optical setup. Results obtained are satisfactory, allowing for an accurate volumetric measurement with displacements up to 30 particle diameters away in the out of focus direction (less than 20% error in particle sizing and 2mm in particle depth location). Extension of these results to the measurement of transparent water droplets generated by an injector is also proposed and the coherence of results is checked. In this case, the size of the background stage and its location are important issues. Following the indications detailed in the paper, satisfactory results are obtained for measurements of transparent water droplets with displacements up to 10 particle diameters away in the out of focus direction.

Spray Droplet Characterization from a Single Nozzle by High Speed Image Analysis Using an In-Focus Droplet Criterion.

Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed backlight image acquisition and analysis system. As only part of the drops of an agricultural spray can be in focus at any given moment, an in-focus criterion based on the gray level gradient was proposed to decide whether a given droplet is in focus or not. In a first experiment, differently sized droplets were generated with a piezoelectric generator and studied to establish the relationship between size and in-focus characteristics. In a second experiment, it was demonstrated that droplet sizes and velocities from a real sprayer could be measured reliably in a non-intrusive way using the newly developed image acquisition set-up and image processing. Measured droplet sizes ranged from 24 μm to 543 μm, depending on the nozzle type and size. Droplet velocities ranged from around 0.5 m/s to 12 m/s. The droplet size and velocity results were compared and related well with the results obtained with a Phase Doppler Particle Analyzer (PDPA).

The Fusion of the Visual and Thermal Images on the Basis of Determining the Image Fragments which Contain Essential Details

The aim of the following study was to develop a procedure which guarantees the data fusion of thermal and visual images. The first stage of the proposed algorithm consisted of images acquisition which guaranteed that the same parts of images represented the same parts of the observed terrain. The second stage depended on previous information about the searched object features. Two different situations were considered herein. In the case when we had the searched object’s feature vector for both representations of a searched object, we could conduct the pattern recognition for each image. It was conducted separately for visual and thermal images. In this way, we obtained the important parts of the images which should be represented in a fused image. The other case examined in the paper, considered the situation in which we did not have the formalised information about the object. In this case, it was necessary to analyse whole images in order to define the potential parts of the images where the object could be found. This analysis should be helpful for an operator to indicate the parts of the images where there are some artefacts which can be the elements of the searched object. Therefore, in this case, the second stage of the algorithm consisted in calculating the local features of the images. These features constituted grey scale gradient computed for the pixels inside the aperture. This study presented the examples of the fused images obtained by means of the developed method.

Joint source channel coding with unequal error protection and quality biased rate allocation for MPEG-2 video transmission

In recent years the importance of video communication has been increased many folds. In this paper, we propose an information significance biased bit allocation technique for rate distortion optimisation. First MPEG-2 compressed bitstreams are separated in different layers. Each layer effect in received quality due to channel constrains decides the priority of the layer and the required level of Unequal Error Protection (UEP). Results indicate that Joint Source Channel Coding (JSCC) with UEP for three-layer (I, B, P frames) defined as UEP3 can lead better acceptable quality of video in noisy environment compared to 1-layer Equal Error Protection (EEP) and 2-layer (Base layer and Enhancement Layer) UEP2 coding approach. Further PSNR of UEP3 method can be improved using layering of I frame Blocks. Blocks are classified into strong informative and marginal informative category by considering the local variance and grey scale gradient distribution and protection are assigned.

Using texture analyses of contrast enhanced CT to assess hepatic fibrosis

PurposeTo determine the ability of texture analyses of contrast-enhanced CT images for distinguishing between varying degrees of hepatic fibrosis in patients with chronic liver disease using histopathology as the reference standard. Materials and methodsFollowing IRB approval, 83 patients who underwent contrast enhanced 64-MDCT of the abdomen and pelvis in the portal venous phase between 12/2005 and 01/2013 and who had a liver biopsy within 6 months of the CT were included. An in-house developed, MATLAB-based texture analysis program was employed to extract 41 texture features from each of 5 axial segmented volumes of liver. Using the Ishak fibrosis staging scale, histopathologic grades of hepatic fibrosis were correlated with texture parameters after stratifying patients into three analysis groups, comparing Ishak scales 0–2 with 3–6, 0–3 with 4–6, and 0–4 with 5–6. To assess the utility of texture features, receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) was used to determine the performance of each feature in distinguishing between normal/low and higher grades of hepatic fibrosis. ResultsA total of 19 different texture features with 7 histogram features, one grey level co-occurrence matrix, 6 gray level run length, 1 Laws feature, and 4 gray level gradient matrix demonstrated statistically significant differences for discriminating between fibrosis groupings. The highest AUC values fell in the range of fair performance for distinguishing between different fibrosis groupings. ConclusionThese findings suggest that texture-based analyses of contrast-enhanced CT images offer a potential avenue toward the non-invasive assessment of liver fibrosis.

Fabrication and optimization of micro-scale speckle patterns for digital image correlation

Experimental investigations are performed on the fabrication and optimization of micro-scale speckle patterns formed by spinning an epoxy resin and powder for digital image correlation measurements. New factors influencing the fabrication process, including the ambient temperature, centrifugal velocity, and solidifying time, are carefully analyzed and are evaluated in terms of the average gray gradient and particle agglomeration, and the optimal micro-scale speckle pattern is obtained with the proposed parameters in the fabrication process. Additionally, the micro-scale speckle pattern is experimentally verified by performing prescribed rigid-body translation tests, and the relative errors are approximately 1.5%. Finally, the micro-scale speckle patterns are transferred to tensile specimens of aluminum and a polymer material with a V notch. The measurement results are consistent with the theoretical predictions, and this agreement demonstrates the feasibility and accuracy of the micro-scale speckle patterns.