ОЦІНКА ЕФЕКТИВНОСТІ ВИЯВЛЕННЯ НЕОДНОРІДНОСТЕЙ НА ЗОБРАЖЕННІ ЗА ДОПОМОГОЮ МАКСИМУМА БІАМПЛІТУДИ

The subject of the article is to analyze the effectiveness of a new method for detecting heterogeneities in a digital image by estimating the bimagnitude maximum of the pixel intensities. The aim is to evaluate the effectiveness of the new method of detecting heterogeneities in the image using the maximum of the bimagnitude compared to the known method based on the estimation of the local root mean square deviation (LRMSD) of pixel intensity values. The objectives of the paper are the following: to formalize the procedure for computing the bimagnitude maximum of the pixels in the local segment; create a test image with different contrast values on the borders; to develop a mathematical model for calculating in the Matlab system the efficiency of detecting heterogeneities in the image in the presence of additive Gaussian noise with different values of noise RMS; provide for analysis and comparison of the graphs the receiver operating characteristic (ROC) contained the number of correctly classified non-homogeneous areas versus the number of incorrectly classified areas. The used methods are the following: bispectral data analysis method; methods of probability theory and mathematical statistics; methods of digital image processing. The following results were obtained. A boundary map for the test image without distortion and the presence of additive Gaussian noise with a variance equal to 0.2 is constructed for two types of detectors: the first one is based on the maximum amplitude and the second one is based on the estimation of the local RMS. The results of computer simulations show that both detectors fine-tune the boundary for the images in the absence of noise. But in the presence of additive noise, the detector based on the biamplitude maximum provides a significant advantage. Graphs of the dependence of the number of correctly classified inhomogeneous sections on the number of incorrectly classified areas for the proposed and known reference detection methods are represented. The area under the curve (AUC) values that characterize the efficiency of detecting heterogeneities in the image are calculated. The scientific novelty of the obtained results is the following: a new approach of detecting inhomogeneities in the image is proposed with the help of a new informative feature estimated in the form of the local biamplitude maximum. To analyze the effectiveness of the proposed method, a test image was formed with different border contrast values. Using the proposed technique and the known method, boundary maps were constructed for the test image without distortion and in the presence of additive Gaussian noise. To evaluate the effectiveness of two methods, the graphs were plotted against the number of correctly classified inhomogeneous sites by the number of incorrectly classified (ROC) for both proposed and known detection methods. A detector based on the local RMS value is more effective at small Gaussian noise variance values, but as the noise variance increases, detector based on the biamplitude maximum estimation is more effective. The calculated AUC values for studied methods based on local RMS estimation and maximum biamplitude estimation are equal to 0. 678 and 0. 8468, respectively. Even though the proposed method loses efficiency, the bispectrum-based method is more effective at large values of noise variance, in particular, when the noise RMS is 0.6, AUC = 0.8748.

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Fast Ellipse Detection Method Based on Coordinates Transformation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.3038 ◽

2014 ◽

Vol 926-930 ◽

pp. 3038-3041

Author(s):

Cheng Wang

Keyword(s):

Digital Image ◽

Detection Method ◽

Closed Curve ◽

New Method ◽

Detection Methods ◽

Circle Detection ◽

Detection Problem ◽

Ellipse Detection ◽

Coordinates Transformation ◽

Detection Speed

In this paper, we introduce a new method for ellipse detection. For any object has closed curve in a digital image, it is easy to calculate the centroid of the object. We assume the object is an ellipse, and then by rotating, scaling this object, it can be transformed to a circle. So, ellipse detection problem becomes circle detection problem. Compared with other detection methods, our method only need process border points of the object, hence has higher detection speed.

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Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0211 ◽

2020 ◽

Vol 30 (1) ◽

pp. 240-257

Author(s):

Akula Suneetha ◽

E. Srinivasa Reddy

Keyword(s):

Image Quality ◽

Mean Square Error ◽

Fuzzy Set ◽

Digital Image ◽

Gaussian Noise ◽

Digital Images ◽

Structural Similarity ◽

Fuzzy Rules ◽

Mean Square ◽

Image Edges

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

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Statistical Assessment of Discrimination Capabilities of a Fractional Calculus Based Image Watermarking System for Gaussian Watermarks

Entropy ◽

10.3390/e23020255 ◽

2021 ◽

Vol 23 (2) ◽

pp. 255

Author(s):

Mario Gonzalez-Lee ◽

Hector Vazquez-Leal ◽

Luis J. Morales-Mendoza ◽

Mariko Nakano-Miyatake ◽

Hector Perez-Meana ◽

...

Keyword(s):

Fractional Calculus ◽

Roc Curve ◽

Gaussian Noise ◽

False Positive ◽

Operating Characteristic ◽

Image Watermarking ◽

Area Under The Curve ◽

Statistical Assessment ◽

Watermarking Scheme ◽

Fractional Equation

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

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A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142055006x ◽

2019 ◽

Vol 34 (02) ◽

pp. 2055006

Author(s):

Jiajia Liu ◽

Jianying Yuan ◽

Yongfang Jia

Keyword(s):

Computer Vision ◽

Sparse Representation ◽

Efficient Method ◽

Important Task ◽

New Method ◽

Automatic Inspection ◽

Test Image ◽

Recognition Result ◽

The Status ◽

Railway Operation

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.

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Study of Segmentation Threshold Based on Wavelet Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3855 ◽

2013 ◽

Vol 756-759 ◽

pp. 3855-3859

Author(s):

Jian Yi Li ◽

Hui Juan Wang

Keyword(s):

Wavelet Transform ◽

Digital Image ◽

Region Growing ◽

Detection Methods ◽

Mathematical Tool ◽

Matlab Simulation ◽

Segmentation Method ◽

Transform Method ◽

Threshold Segmentation ◽

Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

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Digital Imaging as a Possible Approach in Evaluation of Islet Yield

Cell Transplantation ◽

10.3727/000000003108746713 ◽

2003 ◽

Vol 12 (2) ◽

pp. 129-133 ◽

Cited By ~ 15

Author(s):

Peter Girman ◽

Jan Kříž ◽

Jozef Friedmanský ◽

FrantišEk Saudek

Keyword(s):

Image Analysis ◽

Digital Image ◽

Digital Imaging ◽

Digital Image Analysis ◽

Three Dimensional ◽

Two Dimensions ◽

New Method ◽

Important Method ◽

Three Dimensional Objects ◽

Significant Difference

Digital image analysis (DIA) is a new method in assessment of islet amount, which is expected to provide reliable and consistent results. We compared this method with conventional counting of small numbers of rat islets. Islets were isolated from 8 pancreases and counted in 24 samples in duplicate, first routinely by sizing according to estimated diameters under a calibrated reticule and then by processing of islets pictures taken by camera. As presumed, no significant difference was found in absolute numbers of islets per sample between DIA and conventional assessment. Volumes of islets per sample measured by DIA were on average more than 10% higher than amounts evaluated conventionally, which was statistically significant. DIA has been shown to be an important method to remove operator bias and provide consistent results. Evaluation of only two dimensions of three-dimensional objects still represents a certain limitation of this technique. With lowering of computer prices the system could become easily available for islet laboratories.

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A new method for characterizing nonlinearity in scanning probe microscopes using digital image correlation

Nanotechnology ◽

10.1088/0957-4484/16/9/072 ◽

2005 ◽

Vol 16 (9) ◽

pp. 1849-1855 ◽

Cited By ~ 29

Author(s):

Helena Jin ◽

Hugh A Bruck

Keyword(s):

Digital Image Correlation ◽

Digital Image ◽

Image Correlation ◽

New Method ◽

Scanning Probe

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A Study on Different Edge Detection Techniques in Digital Image Processing

Advances in Multimedia and Interactive Technologies - Feature Detectors and Motion Detection in Video Processing ◽

10.4018/978-1-5225-1025-3.ch005 ◽

2017 ◽

pp. 100-122 ◽

Cited By ~ 8

Author(s):

Shouvik Chakraborty ◽

Mousomi Roy ◽

Sirshendu Hore

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Edge Detection ◽

Digital Image Processing ◽

Digital Image ◽

Basic Feature ◽

Detection Methods ◽

Natural Image ◽

Detection Techniques ◽

Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

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