Gaussian Blur Filter Research Articles

Biometric Data Security Using Watermarking Based on Vector Quantization

In this paper, we present a new watermarking method applied to biometric data for the purpose of securing their transmission over a computer network. To perform this task, we combined the fingerprint modality with that of the iris. We proceeded by merging the two biometric signatures using the watermarking based on vector quantization. The proposed technique extracts the main characteristics from the fingerprint image to mask the binary iris code image. This iris code is obtained from the eye image after several transformations. This choice is found to be the obvious combination solution for the type of the manipulated standard images. The vector quantization method was implemented based on Voronoi diagram using a codebook generated from a new chaotic system. The robustness of this technique was extensively tested using several simulation scenarios handling adequate database images. The results show that the proposed method is robust enough against JPEG compression. Other tests covered also different simulated computer attacks of the watermarked image using several types of median, mean and Gaussian blur filters. In this case, the filter sizes are taken large enough i.e., (10 × 10). The overall obtained results are satisfactory and very encouraging.

SU‐C‐207B‐05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose:Develop an automated Random Forest algorithm for tissue segmentation of CT examinations.Methods:Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient's (male, 20 yr, 72 kg) chest‐abdomen‐pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra‐observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images.Results:The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients.Conclusion:The TWS Random Forest auto‐segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

Accurate Facial Landmark Extraction

Facial landmark extraction system is crucial in various applications, including face recognition, expression analysis, face tracking, and face animation. This letter aims to improve the performance of an existing landmark extraction method proposed by Ren et al. in terms of error rate. Specifically, the Gaussian blur filter is applied on the input image to reduce noise interference and the theta-based split rule is deployed to strengthen the performance of the random forests. Then, global linear regression is applied instead of treating each landmark independently. Experimental results demonstrate that the proposed modified facial landmark extraction algorithm outperforms the conventional methods for both the LFPW and Helen databases.

Evaluation of Multi-Target Human Sperm Tracking Algorithms in Synthesized Dataset

Infertility is an important issue for many couples and male infertility is highly related to semen and spermatozoa which can be surveyed by means of semen/sperm analysis. Many fertility assessment laboratories are now equipped with automatic systems called Computer Aided Semen/Sperm Analysis (CASA) for doing this task. Evaluation of such systems is very important. In this research a web-based simulator is developed which facilitates evaluation of CASA systems. The developed software has many useful parameters such as blurring images or adding noise and it also gives full control of sperm counts and types. To illustrate performance of the developed simulator, many parameters such as spermatozoa population, standard deviation of Gaussian blur filter and noise intensity have been swept and the results of two well-known multi-target tracking systems (Linear Kalman Filter and Particle Filter) were compared and discussed.

Improve Captcha's Security Using Gaussian Blur Filter

Providing security for webservers against unwanted and automated registrations has become a big concern. To prevent these kinds of false registrations many websites use CAPTCHAs. Among all kinds of CAPTCHAs OCR-Based or visual CAPTCHAs are very common. Actually visual CAPTCHA is an image containing a sequence of characters. So far most of visual CAPTCHAs, in order to resist against OCR programs, use some common implementations such as wrapping the characters, random placement and rotations of characters, etc. In this paper we applied Gaussian Blur filter, which is an image transformation, to visual CAPTCHAs to reduce their readability by OCR programs. We concluded that this technique made CAPTCHAs almost unreadable for OCR programs but, their readability by human users still remained high.

3D reconstruction of corneal endothelial cell shape: new insight in structure‐function relationships

AbstractPurpose Corneal endothelial cells (CECs) support the pump‐leak theory explaining the maintenance of corneal transparency. CEC are highly polarized with a pole facing aqueous humor having an hexagonal shape bordered by apical tight‐junctions that control permeability. The interdigitated shape of basolateral membranes, described in the late 70’s, are much less known although they bear most of the pumping sites. Aim: to revisit the shape of CEC with modern microscopy and 3D reconstruction.Methods Optimized methods of immunolocalization (fixation and antigen retrieval) were applied on flat mounted whole cornea (fresh or after organ culture). The subcellular structures were distinguished by visualizing apical pole with actin, myosin and ZO‐1; lateral membranes with NCAM (CD56), connexion 43, vimentin and tubulin, and the basal pole with integrin. Stacks of high magnification images were acquired with a confocal microscope with a 150nm pitch. 3D reconstruction was performed with imageJ using convolve and Gaussian blur filters, and a model was defined.Results A 3D map of all markers was obtained. In particular, CD56 perfectly outlined the basolateral membranes of CEC. The 3D model highlighted the dramatic increase in membrane surface due to basolateral expansions.Conclusion CD56 staining pattern and especially the presence of numerous interdigitations should be considered as a hallmark for functional CEC and their study should therefore be integrated in quality controls of CEC production for regenerative medicine processes. Modifications of this 3D structure should also be studied in endothelial diseases. Grant: IFRESIS, St‐ETIENNE METROPOLE

The Performance Analysis Based on Heterogeneous Parallel Processors for Anisotropic Diffusion Filters

A noise in digital image degrades the performance of image processing. These images are most often used in medical field for diagnosis and treatment. Thus, there is a huge demand for high quality images from the medical field. The current algorithms to process useable images are derived using Gaussian blur filter. However, using such isotropic diffusion technique, the filter cuts away pertinent information within the image. The high noise level in an image will blur organs in medical images such as in MRI. In order to accommodate the loss of information while maintaining the quality of the image, an anisotropic diffusion filter has been developed. The anisotropic diffusion filter uses heat equation and the only downfall of the technique is lengthy process time. The purpose of this study is to decrease the processing time using parallel processors through the optimization of GPU.

Cross-Calibration for Data Fusion of EO-1/Hyperion and Terra/ASTER

The data fusion of low spatial-resolution hyperspectral and high spatial-resolution multispectral images enables the production of high spatial-resolution hyperspectral data with small spectral distortion. EO-1/Hyperion is the world's first hyperspectral sensor. It was launched in 2001 and has a similar orbit to Terra/ASTER. In this work, we apply hyperspectral and multispectral data fusion to EO-1/Hyperion and Terra/ASTER datasets by the preprocessing of datasets and the onboard cross-calibration of sensor characteristics. The relationship of the spectral response function is determined by convex optimization by comparing hyperspectral and multispectral images over the same spectral range. After accurate image registration, the relationship of the point spread function is obtained by estimating a matrix that acts as Gaussian blur filter between two images. Two pansharpening-based methods and one unmixing-based method are adopted for hyperspectral and multispectral data fusion and their properties are investigated.

Rapid extraction of central vacancy by image-analysis of Fry plots

The Fry method, based on the relative movement of different material points, typically grain centers, with reference to each other graphically yields a point distribution that displays the finite strain ellipse as a central vacancy. The diffused nature of the central vacancy induces subjectivity in strain estimation, particularly, if the point population when undeformed lacked an isotropic anticlustered distribution. Most existing methods use analytical and/or iterative approaches for improving the sharpness of the central vacancy and positioning the best-fit strain ellipse in a Fry plot. We provide an image-analysis method that is independent of any iteration or analytical solution. It is also an efficient technique for extraction of the central vacancy without any subjectivity. The method is more direct, simple and easy-to-use than most existing techniques.The image-analysis method uses Gaussian blur filter for distinction between the areas of largest and smallest pixel intensities in a Fry plot image. It then applies the optimal threshold value and an inversion filter for extraction of the sharp central vacancy. The method also searches for the best-fit strain ellipse through the extracted central vacancy and displays axial ratio and orientation of the ellipse in a separate window. The validity of the method is tested using several computer-simulated and natural examples.