To see the other types of publications on this topic, follow the link: Fractal Image Processing.
Journal articles on the topic 'Fractal Image Processing'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Fractal Image Processing.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Kovalchuk, A. M. "THE USE OF FRACTAL CONVERTINGS AND THEIR SYSTEMS IN THE ENCRYPTION – DECRYPHATION OF MONOCHROME IMAGES." Ukrainian Journal of Information Technology 6, no. 2 (2024): 98–104. https://doi.org/10.23939/ujit2024.02.098.
Full textAbstract:
Fractals occupy a rather important and defining place in computer graphics. This is the construction of landscapes, trees, plants, even animals and the generation of fractal textures, as well as fractal image compression. Modern physics and mechanics are just beginning to study the behavior of fractal objects. And of course, fractals are used directly in mathematics itself, as well as in cryptography when protecting images. The article describes the use of elements of the RSA algorithm in fractal quadratic transformations and systems of fractal transformations for encryption / decryption of monochrome images. Images are one of the most used types of information. Because of this, protecting images from unauthorized use and access is an urgent task. The main condition for creating image protection is the assumption that the image is a stochastic signal. This allows us to transfer classical signal encryption methods to the case of images. But the image is such a signal that, in addition to the typical informativeness of data, also has visual informativeness, which brings new challenges to the protection problems. In fact, creating an attack on an encrypted image is possible in two cases: through traditional hacking of encryption methods, or through methods of visual image processing (methods of extracting contours, filtering, etc.). The latter do not provide a complete reproduction of the input image, but provide an opportunity to obtain some information from the image. In this regard, another requirement is put forward to encryption methods in the case of their use in relation to images – complete noise of the encrypted image. This is necessary in order to prevent the use of visual image processing methods. Therefore, the urgent task is to develop such a use of the RSA algorithm in order to: preserve the resistance to decryption and ensure full noise of the image in order to make it impossible to use the methods of further visual image processing. One of the ways to solve this problem is to use elements of the RSA algorithm in fractal algorithmic transformations and their systems. One of the ways to solve this problem is to use elements of the RSA algorithm in mathematical transformations, in particular, in fractal algorithmic transformations. Fractal transformations can be both linear and quadratic. And also systems of such fractal transformations. Encryption – decryption can be performed both with additional noise and without additional noise.
2
Khudaiberdiev, Mizaakbar, Igor Khan, Bobomurod Tojiboyev, and Bahodir Achilov. "Fractal representations in image processing of remote sensing of the earth." E3S Web of Conferences 541 (2024): 04010. http://dx.doi.org/10.1051/e3sconf/202454104010.
Full textAbstract:
This article explores the application of fractal representations in the image processing of remote sensing data for Earth observation. Fractals, with their self-similar properties and complex patterns, offer a powerful mathematical framework for analyzing the intricate structures found in natural landscapes. The study highlights the advantages of using fractal-based methods over traditional image processing techniques, particularly in capturing the multifaceted textures and irregularities of Earth’s surface features. By leveraging fractal geometry, enhanced accuracy in the classification and interpretation of remote sensing images is achieved. This approach facilitates better monitoring and understanding of environmental changes, land use patterns, and natural disasters. The findings underscore the potential of fractal representations to significantly improve the quality and efficacy of remote sensing image analysis, providing a robust tool for Earth science research and practical applications in environmental management.
3
Song, Juan, Bangfu Wang, Qingyang Jiang, and Xiaohong Hao. "Exploring the Role of Fractal Geometry in Engineering Image Processing Based on Similarity and Symmetry: A Review." Symmetry 16, no. 12 (2024): 1658. https://doi.org/10.3390/sym16121658.
Full textAbstract:
Fractal geometry theory has been widely used in engineering image processing. In this work, the basic principles and features of fractal geometry are first introduced and its importance in image processing is explained. The features of the symmetry and asymmetry of images are represented in fractal geometry and symmetry scaling is utilized to deal with image processing problems in engineering applications. Subsequently, specific applications of fractal geometry in engineering image processing are discussed in detail in terms of image compression, edge detection, texture analysis, and image reconstruction and restoration. The exploration of these applications reveals the advantages and usefulness of fractal geometry theory in image processing, and it is found that the image has certain symmetry and self-similarity, which is conducive to the establishment of mathematical models for the statistics of graphic contours and shapes. Finally, the unique value of fractal geometry in engineering image processing is further emphasized by comparing the innovations of fractal geometry with traditional image processing methods, which prompts the in-depth consideration of its potential value in this field. This paper provides new insights and directions for the research of engineering image processing, which is of positive significance for future research.
4
KUKLINSKI, WALTER S. "UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING." Fractals 02, no. 03 (1994): 363–69. http://dx.doi.org/10.1142/s0218348x94000454.
Full textAbstract:
One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.
5
Xiao, Lin. "A Study on Detailed Image Processing of Fractal Characteristics of Irregular Cloud Edges." Modern Management Science & Engineering 6, no. 3 (2024): p21. http://dx.doi.org/10.22158/mmse.v6n3p21.
Full textAbstract:
With the recent development of computer graphics and image processing technology, this paper describes the progress of cloud image generation and processing techniques. Clouds in nature usually have complex and changeable shapes, so accurate and detailed processing of their images has always been an important research topic in computer graphics. First, this paper summarizes the application value of fractal theory in cloud image processing. Next, it presents the fractal generation and edge thinning algorithm as crucial technical components in forming the framework of cloud image detail processing. The fractal generation algorithm uses the fractal geometry principle to simulate the basic shape of the cloud. In contrast, the edge thinning algorithm enhances the realism of the cloud edge by adding small-scale features on the boundary.
6
Mr., Vaibhav Vijay Bulkunde, Nilesh P. Bodne Mr., and Sunil Kumar Dr. "Implementation of Fractal Image Compression on Medical Images by Different Approach." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 398–400. https://doi.org/10.31142/ijtsrd23768.
Full textAbstract:
FIC Fractal Image Processing is actually a JPG image which needs to be perform large scale encoding to improve and increase the compression ratio. In this paper we are going to analyze different constraints and algorithms of image processing in MATLAB so that there will be very low loss in image quality. It has been seen that whenever we contain any HD picture that to in medical application we need to sharpen the image that is we need to perform image encryption, image de noising and there should be no loss in image quality. In this paper, we actualized both consecutive and parallel adaptation of fractal picture pressure calculations utilizing programming show for parallelizing the program in Graphics Processing Unit for medicinal pictures, as they are very comparable inside the picture itself. Whenever we consider an image into fractal image, it has great importance and application in image processing field. In this paper compression scheme is used to sharpen and smoothen of image by using various image processing algorithm. There are a few enhancements in the usage of the calculation too. Fractal picture pressure is based on the self closeness of a picture, which means a picture having closeness in dominant part of the locales. We accept this open door to execute the pressure calculation and screen the impact of it utilizing both parallel and successive execution. Fractal pressure has the property of high pressure rate and the dimensionless plan. Pressure plot for fractal picture is of two kind, one is encoding and another is deciphering. Encoding is especially computational costly. Then again interpreting is less computational. The use of fractal pressure to restorative pictures would permit acquiring a lot higher pressure proportions. While the fractal amplification an indivisible element of the fractal pressure would be helpful in showing the recreated picture in an exceedingly meaningful structure. Be that as it may, similar to all irreversible strategies, the fractal pressure is associated with the issue of data misfortune, which is particularly troublesome in the therapeutic imaging. A very tedious encoding process, which can last even a few hours, is another troublesome downside of the fractal pressure. Mr. Vaibhav Vijay Bulkunde | Mr. Nilesh P. Bodne | Dr. Sunil Kumar "Implementation of Fractal Image Compression on Medical Images by Different Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23768.pdf
7
CHARLES, P. D., and C. A. BROWN. "THE PATCHWORK METHOD AND IMAGE PROCESSING." Fractals 02, no. 03 (1994): 441–43. http://dx.doi.org/10.1142/s0218348x94000612.
Full textAbstract:
The patchwork method uses triangular patches to estimate surface area as a function of patch size. The patch area can be interpreted as a scale of observation. Using the patchwork method, one can identify crossover scales, or thresholds, which distinguish scales where a surface is best described by Euclidean or fractal geometries. A method is proposed for application of the patchwork method to video images of luminance intensity data. By recursively subdividing an image and applying the patchwork method, or by using a moving average and applying the patchwork method to a set of small subimages, the patchwork method can be applied for each pixel in the source image to obtain a new set of pixel intensities. Depending which characteristic is used, crossover values or level of detail at varying scales of observation, the resulting intensities highlight regions of fractal complexity or boundaries between image segments best described by Euclidean or fractal geometries. Often, the resulting image displays interesting properties and is useful in feature recognition, image segmentation, and image enhancement.
8
Dai, Guang Zhi, Guo Qiang Han, and Chao Yi Dong. "Super-Resolution Analysis on Molten Pool Image of Metal Active-Gas Welding Based on Wavelet with Fractal." Applied Mechanics and Materials 103 (September 2011): 152–57. http://dx.doi.org/10.4028/www.scientific.net/amm.103.152.
Full textAbstract:
According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively raises MAG welding image resolution, but also PSNR of reconstruction images are enhanced 21.1049 dB.
9
AHAMMER, H., and M. MAYRHOFER-REINHARTSHUBER. "IMAGE PYRAMIDS FOR CALCULATION OF THE BOX COUNTING DIMENSION." Fractals 20, no. 03n04 (2012): 281–93. http://dx.doi.org/10.1142/s0218348x12500260.
Full textAbstract:
The fractal dimensions of real world objects are commonly investigated using digital images. Unfortunately, these images are unable to represent an infinitesimal range of scales. In addition, a proper evaluation of the applied methods that encompass the image processing techniques is often missing. Several mathematical well-defined fractals with theoretically known fractal dimensions, represented by digital images, were investigated in this work. The very popular Box counting method was compared to a new image pyramid approach as well as to the Minkowski dilation method. Effects from noise and altered aspect ratios were also considered. The new Pyramid method is quite identical to the Box counting method, but it is easier to implement. Additionally, the calculation times are much shorter and memory requirements are almost comparable.
10
Chen, Xiang, and Jian Min Wang. "Image Segmentation Approaches of Based on Fractals." Advanced Materials Research 341-342 (September 2011): 773–75. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.773.
Full textAbstract:
Image segmentation is the key steps of image processing and analysis. In image processing process, first, we need make the image into a number of significant areas and the interested objects; this need use image segmentation technology. This paper summarizes the methods of image segmentation. Mainly the image segmentation approach of based on fractal is analyzed, and this method has received good result of image segmentation, it proved that fractals-based method of image segmentation is an effective way.
11
A.O., Zhurba. "ДОСЛІДЖЕННЯ МЕТОДУ ФРАКТАЛЬНОГО СТИСНЕННЯ ЗОБРАЖЕНЬ З МЕТОЮ ПОКРАЩЕННЯ ЯКОСТІ СТИСНЕННЯ". System technologies 4, № 153 (2024): 24–33. http://dx.doi.org/10.34185/1562-9945-4-153-2024-03.
Full textAbstract:
The development of the Internet, along with the availability of increasingly powerful computers and other digital devices, cameras, scanners and printers, has led to the wide-spread use of digital images. In this regard, interest in improving data compression algo-rithms, such as images, is growing. Data compression is important for both transfer speed and storage efficiency. In addition to many commercial uses, compression technologies are also of interest in the military industry, such as applications for processing telemetry data from missile inter-ceptors or for archiving terrain image data for defense simulations. Solving the problem of image compression, or, more generally, image coding, has used advances and stimulated the development of many fields of engineering and math-ematics. The article examines fractal image compression — a data compression method based on the use of self-similar patterns in an image. This method allows you to achieve a high degree of compression while preserving image details. Fractal image compression is a unique and efficient approach to data compression based on the mathematical theory of fractals. Nowadays, it has important applications and advantages that make it a valuable tool in image processing. The main advantages include: 1. Preservation of details during compression. One of the key advantages of fractal compression is its ability to preserve a high degree of image detail in a relatively small amount of storage. This is especially important in situations where image quality must be preserved with limited storage and data transfer resources. 2. Efficiency of transmission through the network. Fractal compression allows for compact images, making it suitable for image transmission over a low-bandwidth network. This is especially true for mobile devices, the Internet of Things, and other scenarios where high bandwidth is not always available. 3. Adaptive compression for different resolutions. Fractal compression allows you to adapt the level of compression depending on the resolution and details of the image. This means that it can be used to compress various image sizes without significant loss of quality. 4. Data archiving and storage. Fractal compression can be useful for archiving and long-term storage of images, as it allows you to effectively reduce the amount of data without losing important information. This is especially important for libraries, archives, research databases and other data repositories. Fractal image compression remains a relevant and valuable tool in today's envi-ronment, thanks to its ability to efficiently compress, preserve details, and adapt to dif-ferent usage scenarios. Therefore, the study of its efficiency, the optimization of the soft-ware code to obtain a faster and better compression result, is an urgent task.
12
La Torre, Davide, and Edward R. Vrscay. "GENERALIZED FRACTAL TRANSFORMS AND SELF-SIMILARITY: RECENT RESULTS AND APPLICATIONS." Image Analysis & Stereology 30, no. 2 (2011): 63. http://dx.doi.org/10.5566/ias.v30.p63-76.
Full textAbstract:
Most practical as well as theoretical works in image processing and mathematical imaging consider images as real-valued functions, u : X → ℝg, where X denotes the base space or pixel space over which the images are defined and ℝg ⊂ ℝ is a suitable greyscale space. A variety of function spaces ℱ(X) may be considered depending on the application. Fractal image coding seeks to approximate an image function as a union of spatially-contracted and greyscale-modified copies of subsets of itself, i.e., u ≈ Tu, where T is the so-called Generalized Fractal Transform (GFT) operator. The aim of this paper is to show some recent developments of the theory of generalized fractal transforms and how they can be used for the purpose of image analysis (compression, denoising). This includes the formulation of fractal transforms over various spaces of multifunctions, i.e., set-valued and measure-valued functions. The latter may be useful in nonlocal image processing.
13
Bereziuk, Volodymyr, and Yaroslav Sokolovskyy. "ENHANCEMENT OF MEDICAL MRI IMAGES BASED ON THE ATANGAN-BALEANU FRACTAL OPERATOR." Computer Design Systems. Theory and Practice 6, no. 3 (2024): 65–78. https://doi.org/10.23939/cds2024.03.065.
Full textAbstract:
This article describes the use of the Atangana-Baleanu fractal operator for the task of enhancing textures in medical MRI images. It provides a detailed explanation of the mathematical framework of the Atangana-Baleanu fractal differential. A numerical approach for calculating the fractal differential using the finite difference method is considered. Based on the approximated solution, approximation coefficients are determined. These coefficients are used to create eight differently oriented masks, which serve as filters for spatial image processing in various directions. A corresponding algorithm for applying fractal masks is developed and described. The obtained results of the algorithm’s performance on medical image processing are compared. The impact of the image enhancement algorithm on image parameters is also investigated. Furthermore, a comparison with other texture enhancement algorithms is conducted.
14
Xu, Tong, and Lei Huang. "A Novel Algorithm of Fractal-Wavelet Image Denosing." Advanced Materials Research 532-533 (June 2012): 1440–44. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1440.
Full textAbstract:
Image denosing is the first preprocessing step in dealing with image processing where the overall system quality should be improved. So it is a key issue in all image processing researches. Over the past years, fractal-wavelet transforms were introduced in an effort to reduce the blockiness and computational complexity that are inherent in fractal image compression. The essence of fractal image denosing is to predict fractal code of a noiseless image from its noisy observation. From the predicted fractal code, we can generate an estimate of the original image. In the paper, we show how well fractal-wavelet denosing predicts parent wavelet subtrees of the noiseless image. The performance of various fractal-wavelet denosing schemes is compared to that of some standard wavelet thresholding methods. From the several of experimental results, these fractal-based image denosing methods are quite competitive with standard wavelet thresholding methods for image denosing.
15
Wang, Xuan, Wei Liu, Hui Cao, and Dong Ping Ma. "Analysis and Study of the Steel Plate Surface Defects Image Processing and the Fractal Dimension Characteristics." Advanced Materials Research 482-484 (February 2012): 1773–76. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.1773.
Full textAbstract:
Steel surface defect detection is the key point of this research. The paper mainly focuses on the image processing and image feature extraction of the steel plate surface. The paper also focuses on the calculating procedure and results of the fractal dimension in different defects images. It can be concluded from the results of the study, fractal dimension of the defect images becomes an important feature of the steel plate surface image pattern recognition.
16
Kadhim, Maha Abdulameer. "Medical image processing using fractal functions." Periodicals of Engineering and Natural Sciences (PEN) 9, no. 2 (2021): 691. http://dx.doi.org/10.21533/pen.v9i2.1870.
Full text
17
Pesquet-Popescu, B., and J. L. Vehel. "Stochastic fractal models for image processing." IEEE Signal Processing Magazine 19, no. 5 (2002): 48–62. http://dx.doi.org/10.1109/msp.2002.1028352.
Full text
18
UENO, YOSHITO. "WAVELETS AND FRACTAL IMAGE COMPRESSION BASED ON THEIR SELF-SIMILARITY OF THE SPACE-FREQUENCY PLANE OF IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (2003): 393–405. http://dx.doi.org/10.1142/s0219691303000256.
Full textAbstract:
This paper presents a fusion scheme for wavelets and fractal image compression based on the self-similarity of the space-frequency plane of sub-bands after wavelet transformation of images. Various kinds of wavelet transform are examined for the characteristics of their self-similarity and evaluated for the adoption of fractal encoder. The aim of this paper is to reduce the information of the two sets of blocks involved in the fractal image compression by using the self-similarity of images. And also, the new video encoder using the fusion method of wavelets and fractal adopts the similar manner as the motion compensation technique of MPEG encoder. Experimental results show almost the same PSNR and bits rate as conventional fractal image encoder by depending on the sampled images through computer simulations.
19
Abdul-Adheem, Wameedh, and Sanaa Ahmed. "Image Processing Techniques for COVID-19 Detection in Chest CT." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (March 31, 2023): 218–26. http://dx.doi.org/10.55562/jrucs.v52i2.554.
Full textAbstract:
COVID-19 virus is a disease that has spread around the world recently. Early diagnosis of the disease leads us to the opportunity to treat patients faster to reduce its spread in the community. The CT scan image is one of the routines used to diagnose the COVID-19 diseases in an efficient manner and in a faster time. Fractal Dimension which mean (fragmented or irregular) used in wide range of image processing and analysis applications to get the self- similarity of images. To classify textures, combine images, segment and compress images and to generate incredibly complex and good-looking images, the Fractal Dimension method is used. Moreover, Euler method uses features to explain the structural property caused by noise in binary images. It also describes the topological features and analyze the texture of images. In this paper, a method is proposed to obtain the features of CT scan images for COVID-19 by using a hybrid technique called Fractal Dimension Euler (FDE), which merges the two methods of image processing (Fractal Dimension method and Euler Number Method). The two algorithms aim to segment the CT-scan images for the chest to distinguish between the affected and uninfected area of the chest to detect the COVID-19. The results of the proposed approach were very useful in comparison with another approach, the FD method was applied to CT scan images for COVID- 19 using a method called box counting. After that, the Euler method was used to distinguish between foreground and background by using a threshold value. The best threshold value was (255) which achieved the finest result.
20
P.Tharaniya. "Border Detection of Skin Cancer Cells over Fractal Dimension Analysis and Image Processing Techniques." Communications on Applied Nonlinear Analysis 32, no. 3s (2024): 45–59. https://doi.org/10.52783/cana.v32.2533.
Full textAbstract:
Fractal dimension analysis is a novel technique that uses the self-similarity qualities of fractals to identify irregular forms, such as those prevalent in diseased tissues, in order to detect the borders of skin cancer cells. Acquire detailed pictures of skin tissue samples that have cancer cells in them. A variety of imaging methods, including microscopy and medical imaging tools like MRIs and CT scans, can be used for this. Determine the image's fractal dimension by applying suitable methods, like the fractal signature method or box-counting. A geometric shape's complexity is measured by its fractal dimension, and because malignant cells have uneven edges, they typically show higher complexity. To increase the border recognition process' accuracy, clean the photos to get rid of noise and boost contrast. Here, methods such as morphological procedures, histogram equalization, and median filtering can be used. To increase the border detection system's accuracy and resilience, fine-tune the parameters and algorithms in light of the validation results. A reliable approach for identifying the borders of skin cancer cells can be created by fusing fractal dimension analysis with image processing methods. This will help with early detection and therapy planning. Based on the fractal dimension, choose an appropriate threshold value to divide the image into zones of interest. This stage aids in the malignant cells' separation from the surrounding tissue.
21
Manandhar, Reena, and Sanjeeb Prashad Pandey. "Echocardiography image denoising using fractal wavelet transform." Journal of Science and Engineering 5 (August 31, 2018): 23–33. http://dx.doi.org/10.3126/jsce.v5i0.22369.
Full textAbstract:
One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.
22
Nappi, Michele, and Daniel Riccio. "Combining Fractal Coding and Orthogonal Linear Transforms." ISRN Signal Processing 2011 (April 26, 2011): 1–9. http://dx.doi.org/10.5402/2011/359592.
Full textAbstract:
Many desirable properties make fractals a powerful mathematic model applied in several image processing and pattern recognition tasks: image coding, segmentation, feature extraction, and indexing, just to cite some of them. Unfortunately, they are based on a strong asymmetric scheme, consequently suffering from very high coding times. On the other side, linear transforms are quite time balanced, allowing them to be usefully exploited in realtime applications, but they do not provide comparable performances with respect to the image quality for high bit rates. In this paper, we investigate different levels of embedding orthogonal linear transforms in the fractal coding scheme. Experimental results show a clear improved quality for compression ratios up to 15 : 1.
23
ZOU, YURU, HUAXUAN HU, JIAN LU, XIAOXIA LIU, QINGTANG JIANG, and GUOHUI SONG. "A NONLOCAL LOW-RANK REGULARIZATION METHOD FOR FRACTAL IMAGE CODING." Fractals 29, no. 05 (2021): 2150125. http://dx.doi.org/10.1142/s0218348x21501255.
Full textAbstract:
Fractal coding has been widely used as an image compression technique in many image processing problems in the past few decades. On the other hand side, most of the natural images have the characteristic of nonlocal self-similarity that motivates low-rank representations of them. We would employ both the fractal image coding and the nonlocal self-similarity priors to achieve image compression in image denoising problems. Specifically, we propose a new image denoising model consisting of three terms: a patch-based nonlocal low-rank prior, a data-fidelity term describing the closeness of the underlying image to the given noisy image, and a quadratic term measuring the closeness of the underlying image to a fractal image. Numerical results demonstrate the superior performance of the proposed model in terms of peak-signal-to-noise ratio, structural similarity index and mean absolute error.
24
Bereziuk, Volodymyr, and Yaroslav Sokolovskyi. "ENHANCEMENT OF MEDICAL MRI IMAGES BASED ON FRACTAL OPERATORS." Computer Design Systems. Theory and Practice 6, no. 2 (2024): 130–45. http://dx.doi.org/10.23939/cds2024.02.130.
Full textAbstract:
This article presents the research of texture enhancement algorithms on medical images. Medical MRI brain scans contain large areas with low level grey colors that carry useful information for doctors. Texture improvement allow to highlight large grey areas on images for future detailed recognition. Based on the study of existing texture enhancement methods, it was determined that fractal operators are effective for processing medical images. The mathematical framework of fractal operators is presented based on the approximation equation of the Grünwald-Letnikov fractional derivatives. The creation of fractal differential masks and the algorithm of masks usage for image enhancement are described based on this equation. The approximation error of the Grunwald-Letnikov derivative is calculated in comparison with the analytical value of the Grunwald-Letnikov derivative. The algorithm based on the fractal derivative shows improvements in image parameters such as contrast, correlation, energy, and homogeneity compared to the original image parameters. A comparison of the results of the algorithm based on the fractal differential with other algorithms for improving the texture of images is also given. It is concluded that the fractal differential algorithm is well-suited for MRI image enhancement tasks, unlike other algorithms, both in visual comparisons and numerical metrics, and thus can be applied to solve real-world problems.
25
Song, Peng Ran, and Chang Ming Wang. "Study for Quantitative Analysis of Loess Microstructure Influence." Advanced Materials Research 594-597 (November 2012): 522–26. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.522.
Full textAbstract:
Microstructure is a important index of soil physical, mechanical and engineering properties, SEM images and computer image processing technology make the soil microstructure research developing rapidly in recent years, but the researches on the influence factors and important degree are rare. Process the images form scanning electron microscopy test with the image processing toolbox of MATLAB. Fractal dimensions, porosities and pore size distributions are calculated in different analyzing windows, thresholds and magnifications. The results show that:1) As the results of the experiment influenced greatly by the smaller analyzing windows, in order to get the real calculation value, the medium section was processed; 2) Fractal dimension values are less influenced than porosities and pore size distributions by different thresholds; 3) Too big magnification can cause inaccurate fractal dimensions. Porosities and pore size distributions are inverse growing with increasing of magnification.
26
He, Deyi, and Chusheng Liu. "An Online Detection Method for Coal Dry Screening Based on Image Processing and Fractal Analysis." Applied Sciences 12, no. 13 (2022): 6463. http://dx.doi.org/10.3390/app12136463.
Full textAbstract:
In coal dry screening, online detection for screening efficiency is a significant challenge. Notwithstanding, the method of image processing is strenuous to implement in this field due to the complex surface texture of shattered coal. This method identifies the fractal phenomenon before and after coal screening is discovered for the indirect detection of screening efficiency. For better fractal dimension distribution, an image denoising and filter method for wiping off the coal image surface texture is applied. Additionally, an enhanced Kirsch edge-detection algorithm is employed to obtain coal particle edges. Furthermore, the relation between fractal dimension and screening efficiency is presented by using the box-counting method. In this research, we skilfully transform the tough problem of image detection for particle size distribution into the calculation of the fractal dimension of the coal-edge image, and closely associate the fractal dimension with screening efficiency. With this method, it will be easier to predict the screening efficiency in real-time.
27
LIAN, SHIGUO. "IMAGE AUTHENTICATION BASED ON FRACTAL FEATURES." Fractals 16, no. 04 (2008): 287–97. http://dx.doi.org/10.1142/s0218348x08004034.
Full textAbstract:
In this paper, the fractal features of natural images are used to construct an image authentication scheme, which can detect whether an image is maliciously tampered (cutting, wiping, modification, etc.) or not and can even locate the tampered regions. For the original image, the fractal transformation is applied to each of the image blocks, and some of the transformation parameters are quantized and used as the authentication code. The authentication code can be stored or transmitted secretly. To authenticate an image, the new authentication code is computed from the image with the similar method, and then compared with the stored or received code. A metric is proposed to decide whether an image block is tampered or not. Comparative experiments show that the authentication scheme can detect malicious tampering, is robust against such common signal processing as JPEG compression, fractal coding, adding noise or filtering, and thus, obtains competent performances compared with existing image authentication schemes.
28
Svynchuk, Olga, Oleg Barabash, Joanna Nikodem, Roman Kochan, and Oleksandr Laptiev. "Image Compression Using Fractal Functions." Fractal and Fractional 5, no. 2 (2021): 31. http://dx.doi.org/10.3390/fractalfract5020031.
Full textAbstract:
The rapid growth of geographic information technologies in the field of processing and analysis of spatial data has led to a significant increase in the role of geographic information systems in various fields of human activity. However, solving complex problems requires the use of large amounts of spatial data, efficient storage of data on on-board recording media and their transmission via communication channels. This leads to the need to create new effective methods of compression and data transmission of remote sensing of the Earth. The possibility of using fractal functions for image processing, which were transmitted via the satellite radio channel of a spacecraft, is considered. The information obtained by such a system is presented in the form of aerospace images that need to be processed and analyzed in order to obtain information about the objects that are displayed. An algorithm for constructing image encoding–decoding using a class of continuous functions that depend on a finite set of parameters and have fractal properties is investigated. The mathematical model used in fractal image compression is called a system of iterative functions. The encoding process is time consuming because it performs a large number of transformations and mathematical calculations. However, due to this, a high degree of image compression is achieved. This class of functions has an interesting property—knowing the initial sets of numbers, we can easily calculate the value of the function, but when the values of the function are known, it is very difficult to return the initial set of values, because there are a huge number of such combinations. Therefore, in order to de-encode the image, it is necessary to know fractal codes that will help to restore the raster image.
29
R, Usha, and Perumal K. "A modified fractal texture image analysis based on grayscale morphology for multi-model views in MR Brain." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 154. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp154-163.
Full textAbstract:
<span>This paper presents a modified fractal texture feature analysis with the use of grayscale image morphology for automatic image classification of different views in MR brain images into normal and abnormal. This main contribution of this approach is a reduction of the total number of a threshold value, and the number of image decomposition, in which only the number of extract threshold value two or three are enough for tumor region extraction - compared to four or more is required in the previous method of SFTA (segmentation based fractal texture analysis). This is achieved by pre-processing of hierarchical transformation technique (HTT), which make use of morphological image transformations with the desired structural element. From this decomposed images, mean, area, fractal dimension and selective shape features are extracted and fed into KNN and ensemble bagged tree classifiers. Finally, some of the post-processing is handled for tumor region extraction and tumor cells computation. It is found that this proposed approach has superior results in the segmentation of diseased tissue from normal tissue and the prediction of image classes in terms of accuracy with the less number of threshold extraction and image decomposition rather than the SFTA algorithm.</span>
30
Shahnazari, M. R., A. Saberi, and Ali J. Chamkha. "Simulation of Nonlinear Viscous Fingering in a Reactive Flow Displacement: A Multifractal Approach." Journal of Nanofluids 12, no. 1 (2023): 288–97. http://dx.doi.org/10.1166/jon.2023.2003.
Full textAbstract:
fractal analysis of viscous fingering of a reactive miscible flow displacement in homogeneous porous media is investigated and multifractal spectrum, and fractal dimension are introduced as two essential features to characterize the irregularity of finger patterns. The Reaction of the two reactant fluids generates a miscible chemical product C in the contact zone. Considering the similarity between chemical products and coastline, monofractal and multifractal analyzes are performed. In monofractal analysis, the box-counting method is implemented on binary images and in multifractal analysis, due to the image processing; the fractal characteristics of viscous fingering instability are analyzed by means of fractal quantities such as Holder exponent, multifractal spectrum, f (α)-image and fractal dimension dynamics. Fractal analysis shows that the fractal dimension increases with time. Also, by considering five different nonlinear simulations, the results show that in the case both sides of the chemical product C are unstable, the multifractal spectrum curve has the highest peak, which means the more complex finger patterns lead to more values of fractal dimension. In addition, a comparison between different values of Ar is conducted and the results show similar behavior. However, small value of aspect ratio leads to a broader width of the multifractal spectrum curve. Furthermore, f (α)-images of concentration contour were investigated for different precisions and some undetectable finger patterns were observed in these images. It can be concluded that the use of f (α)-image represents more detailed image than concentration contours.
31
Guo, Yuheng. "GAN Image Generation and Detection Technology Based on Fractal Dimension." Journal of Physics: Conference Series 2113, no. 1 (2021): 012063. http://dx.doi.org/10.1088/1742-6596/2113/1/012063.
Full textAbstract:
Abstract With the development of modern technology today, image analysis technology has become an important research direction in the computer field. Computer image processing technology is a cross and fusion involving multiple disciplines, which includes multiple research directions such as physics, chemistry, and computer science. Starting from the fractal dimension, this article studies the image generation and detection technology. The purpose of the research topic in this article is to have a deep understanding of image generation technology so as to master image processing methods proficiently. The methods used in this article include case analysis, data, comparison, and experimental methods, etc., and carry out related researches on image generation detection technology and fractal dimension. The experimental results show that the algorithm accuracy of GAN in the detection technology of computer-generated images is higher than other algorithms, and the improvement space is about 0.06.
32
Lu, Guojun. "Fractal image compression." Signal Processing: Image Communication 5, no. 4 (1993): 327–43. http://dx.doi.org/10.1016/0923-5965(93)90055-x.
Full text
33
Parkinson, Ian, and Nick Fazzalari. "FRACTAL ANALYSIS OF TRABECULAR BONE: A STANDARDISED METHODOLOGY." Image Analysis & Stereology 19, no. 1 (2011): 45. http://dx.doi.org/10.5566/ias.v19.p45-49.
Full textAbstract:
A standardised methodology for the fractal analysis of histological sections of trabecular bone has been established. A modified box counting method has been developed for use on a PC based image analyser (Quantimet 500MC, Leica Cambridge). The effect of image analyser settings, magnification, image orientation and threshold levels, was determined. Also, the range of scale over which trabecular bone is effectively fractal was determined and a method formulated to objectively calculate more than one fractal dimension from the modified Richardson plot. The results show that magnification, image orientation and threshold settings have little effect on the estimate of fractal dimension. Trabecular bone has a lower limit below which it is not fractal (λ<25 μm) and the upper limit is 4250 μm. There are three distinct fractal dimensions for trabecular bone (sectional fractals), with magnitudes greater than 1.0 and less than 2.0. It has been shown that trabecular bone is effectively fractal over a defined range of scale. Also, within this range, there is more than 1 fractal dimension, describing spatial structural entities. Fractal analysis is a model independent method for describing a complex multifaceted structure, which can be adapted for the study of other biological systems. This may be at the cell, tissue or organ level and compliments conventional histomorphometric and stereological techniques.
34
Akinshin, Nikolay, Oleg Esikov, Alexander Potapov, Ruslan Akinshin, and Alexander Kuleshov. "Application of Fractal Analysis Methods of Textures Earth Surface Images for Ecological Setting Assessment." EPJ Web of Conferences 224 (2019): 04008. http://dx.doi.org/10.1051/epjconf/201922404008.
Full textAbstract:
The use of fractal analysis methods for automatic textures earth surface image evaluation and ecological setting assessment has been proposed. Image processing techniques have been chosen for the following fractal analysis. The use of the Minkowski dimension to construct sheet dimension and histogram of fractal dimension has been suggested. Performance tests for the proposed methods and algorithms have been performed and analysed presented. Application of the fractal analysis methods is efficient in solving problems of monitoring objects clustering, assessing their conditions, identifying anomalies that have implications for the ecological setting, and evaluating the development of anomalous situations. The fractal analysis algorithms permit efficient processing of array information in the case, when they are implemented on modern computational hardware platforms.
35
SATO, TAKASHI, MAKOTO MATSUOKA, and HIDEKI TAKAYASU. "FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES." Fractals 04, no. 04 (1996): 463–68. http://dx.doi.org/10.1142/s0218348x96000571.
Full textAbstract:
We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.
36
WANG, XING-YUAN, and YAHUI LANG. "A FAST FRACTAL ENCODING METHOD BASED ON FRACTAL DIMENSION." Fractals 17, no. 04 (2009): 459–65. http://dx.doi.org/10.1142/s0218348x09004491.
Full textAbstract:
In this paper a fast fractal coding method based on fractal dimension is proposed. Image texture is an important content in image analysis and processing which can be used to describe the extent of irregular surface. The fractal dimension in fractal theory can be used to describe the image texture, and it is the same with the human visual system. The higher the fractal dimension, the rougher the surface of the corresponding graph, and vice versa. Therefore in this paper a fast fractal encoding method based on fractal dimension is proposed. During the encoding process, using the fractal dimension of the image, all blocks of the given image first are defined into three classes. Then each range block searches the best match in the corresponding class. The method is based on differential box counting which is chosen specifically for texture analysis. Since the searching space is reduced and the classification operation is simple and computationally efficient, the encoding speed is improved and the quality of the decoded image is preserved. Experiments show that compared with the full search method, the proposed method greatly reduced the encoding time, obtained a rather good retrieved image, and achieved the stable speedup ratio.
37
TANAKA, MANABU, ATSUSHI KAYAMA, RYUICHI KATO, and YOSHIAKI ITO. "ESTIMATION OF THE FRACTAL DIMENSION OF FRACTURE SURFACE PATTERNS BY BOX-COUNTING METHOD." Fractals 07, no. 03 (1999): 335–40. http://dx.doi.org/10.1142/s0218348x99000335.
Full textAbstract:
In the box-counting method, positioning of images do not significantly affect the estimation of the fractal dimension of river pattern on the brittle fracture surface, and that of dimple pattern on the ductile fracture surface of materials. A reasonable estimation of the fractal dimension can be made using the box-counting method by a single measurement on the fracture surface pattern. The fractal dimension of dimple pattern in pure Zn polycrystals (about 1.50) is larger than that of river pattern in soda-lime glass (about 1.30). Personal difference in image processing does not have a large influence on the estimation of the fractal dimension of grain-boundary fracture surface profile, compared with the effects of local variation in fracture pattern concerning image size.
38
SANKARAGOMATHI, B., L. GANESAN, and S. ARUMUGAM. "ENCODING VIDEO SEQUENCES IN FRACTAL-BASED COMPRESSION." Fractals 15, no. 04 (2007): 365–78. http://dx.doi.org/10.1142/s0218348x0700371x.
Full textAbstract:
With the rapid increase in the use of computers and the Internet, the demand for higher transmission and better storage is increasing as well. This paper describes the different techniques for data (image-video) compression in general and, in particular, the new compression technique called fractal image compression. Fractal image compression is based on self-similarity, where one part of an image is similar to the other part of the same image. Low bit rate color image sequence coding is very important for video transmission and storage applications. The most significant aspect of this work is the development of color images using fractal-based color image compression, since little work has been done previously in this area. The results obtained show that the fractal-based compression works for the color images works as well as for the gray-scale images. Nevertheless, the encoding of the color images takes more time than the gray-scale images. Color images are usually compressed in a luminance-chrominance coordinate space, with the compression performed independently for each coordinate by applying the monochrome image processing techniques. For image sequence compression, the design of an accurate and efficient algorithm for computing motion to exploit the temporal redundancy has been one of the most active research areas in computer vision and image compression. Pixel-based motion estimation algorithms address pixel correspondence directly by identifying a set of local features and computing a match between these features across the frames. These direct techniques share the common pitfall of high computation complexity resulting from the dense vector fields produced. For block matching motion estimation algorithms, the quad-tree data structure is frequently used in image coding to recursively decompose an image plane into four non-overlapping rectangular blocks.
39
Maugeri, Laura, Mauro DiNuzzo, Marta Moraschi, et al. "Fractal Dimension Analysis of High-Resolution X-Ray Phase Contrast Micro-Tomography Images at Different Threshold Levels in a Mouse Spinal Cord." Condensed Matter 3, no. 4 (2018): 48. http://dx.doi.org/10.3390/condmat3040048.
Full textAbstract:
Fractal analysis is a powerful method for the morphological study of complex systems that is increasingly applied to biomedical images. Spatial resolution and image segmentation are crucial for the discrimination of tissue structures at the multiscale level. In this work, we have applied fractal analysis to high-resolution X-ray phase contrast micro-tomography (XrPCμT) images in both uninjured and injured tissue of a mouse spinal cord. We estimated the fractal dimension (FD) using the box-counting method on tomographic slices segmented at different threshold levels. We observed an increased FD in the ipsilateral injured hemicord compared with the contralateral uninjured tissue, which was almost independent of the chosen threshold. Moreover, we found that images exhibited the highest fractality close to the global histogram threshold level. Finally, we showed that the FD estimate largely depends on the image histogram regardless of tissue appearance. Our results demonstrate that the pre-processing of XrPCμT images is critical to fractal analysis and the estimation of FD.
40
Berke, József. "Application Possibilities of Orthophoto Data Based on Spectral Fractal Structure Containing Boundary Conditions." Remote Sensing 17, no. 7 (2025): 1249. https://doi.org/10.3390/rs17071249.
Full textAbstract:
The self-similar structure-based analysis of digital images offers many new practical possibilities. The fractal dimension is one of the most frequently measured parameters if we want to use image data in measurable analyses in metric spaces. In practice, the fractal dimension can be measured well in simple files containing only image data. In the case of complex image data structures defined in different metric spaces, their measurement in metric space encounters many difficulties. In this work, we provide a practical solution for the measurement of ortho-photos—as complex image data structures—based on the spectral fractal structure based on boundary conditions (height, time, and temperature), presenting the further development of the related theoretical foundations. We will discuss the optimal flight altitude determination in detail through practical examples. For this, in addition to the structural measurements on the images, we also use the well-known image entropy in information theory. The data obtained in this way can facilitate the optimal UAS operation execution that best suits further image processing tasks (e.g., classification, segmentation, and index analysis).
41
SAPOVAL, B., and M. ROSSO. "GRADIENT PERCOLATION AND FRACTAL FRONTIERS IN IMAGE PROCESSING." Fractals 03, no. 01 (1995): 23–31. http://dx.doi.org/10.1142/s0218348x95000047.
Full textAbstract:
In contrast to standard percolation where criticality is reached only for a particular value pc of the driving parameter p, gradient percolation exists without the precise tuning of a percolation parameter. For this reason it may be a common physical situation. Very generally, gradient percolation will appear in a uniform system whenever there exists a local random response to an excitation which varies in space. We show that such a situation exists in the example of photographic imaging, due to the random aspect of the photographic process. In this case gradient percolation may be used as a filter for recovering fuzzy images. This filter has the advantage of self-adjusting and to be neutral in regard to the size of the objects. In particular it could be used to increase artificially the depth of focus on photographs that are partially fuzzy.
42
Zhang, Can. "Remote Sensing Image Processing Using Wavelet Fractal Interpolation." Journal of Computer Research and Development 42, no. 2 (2005): 247. http://dx.doi.org/10.1360/crad20050210.
Full text
43
Bourbakis, Nikolaos G., and Chris Alexopoulos. "A fractal-based image processing language: formal modeling." Pattern Recognition 32, no. 2 (1999): 317–38. http://dx.doi.org/10.1016/s0031-3203(98)00074-0.
Full text
44
Kaneko, Hiroshi, and Ken-ichi Arakawa. "Challange to Complexity; (7) Fractal for Image Processing." Journal of the Institute of Television Engineers of Japan 50, no. 7 (1996): 918–27. http://dx.doi.org/10.3169/itej1978.50.918.
Full text
45
Mu, Zhi Tao, Zuo Tao Zhu, Ding Hai Chen, and Fu Gao Zhang. "Research on Corrosion Fractal Character of LY12CZ Aluminum Alloy Based on Image Processing Technique." Advanced Materials Research 152-153 (October 2010): 1691–95. http://dx.doi.org/10.4028/www.scientific.net/amr.152-153.1691.
Full textAbstract:
The number of corrosion pits with different corrosion times were got through extracting the corrosion character of LY12CZ aluminum alloy based on binary image disposal technique, through statistical fractal theory we can see that the distribution of corrosion pits have fractal character; the fractal dimension and weight loss rate increased with the corrosion time and the change rule of fractal dimension with different corrosion time consistent with weight loss rate, both are accordant with power function, so the fractal dimension can be regarded as an important parameter to evaluate the corrosion degree.
46
Mahdi, Mohammed Sahib, and Aqeel Abboud Abdul Hassan. "Satellite Images Classification in Rural Areas Based on Fractal Dimension." Journal of Engineering 22, no. 4 (2016): 147–57. http://dx.doi.org/10.31026/j.eng.2016.04.10.
Full textAbstract:
Fractal geometry is receiving increase attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite images. In this paper, a satellite image is used which was taken by Quick Bird that contains different visible classes. After pre-processing, this image passes through two stages: segmentation and classification. The segmentation carried out by hybrid two methods used to produce effective results; the two methods are Quadtree method that operated inside Horizontal-Vertical method. The hybrid method is segmented the image into two rectangular blocks, either horizontally or vertically depending on spectral uniformity criterion; otherwise the block is segmented by the quadtree. Then, supervised classification is carried out by means the Fractal Dimension. For each block in the image, the Fractal Dimension was determined and used to classify the target part of image. The supervised classification process delivered five deferent classes were clearly appeared in the target part of image. The supervised classification produced about 97% classification score, which ensures that the adopted fractal feature was able to recognize different classes found in the image with high accuracy level.
47
Zaki, Rizki, Tedjo Darmanto, and Riki Ramdan. "SIMULASI TRANSISI METAMORFOSIS BENTUK CITRA MODEL FRAKTAL DENGAN LIMA KOMPONEN KOLASE ARAH RADIAL DAN VERTIKAL." EDUSAINTEK: Jurnal Pendidikan, Sains dan Teknologi 11, no. 1 (2023): 243–57. http://dx.doi.org/10.47668/edusaintek.v11i1.998.
Full textAbstract:
Metamorphic as a keyword has several implementations in digital era. The basic implementation is in image processing that can be extended to other fields. The metamorphic process in image processing as a series transition visualization of two source images can be simulated in fractal model. As long as a pair of fractal objects of the iterated function system model uses the affine coefficients as a result of transformations in initial state, so it can be transformed to other states easily just applying gradually manipulation on each set of coefficients based on the interpolation values between the source and destination objects. In this paper there are two kinds of metamorphic transition effects on two pairs of fractal objects with five collage components discussed. The first model exhibits the metamorphic transition effect in radial direction and the second one exhibits the metamorphic transition effect in vertical direction between the pair of similar objects.
48
Yu, Hai Zhu, Xiao Li Chai, and Hua Deng. "Image Interpolation Based on Wavelet Transform." Applied Mechanics and Materials 484-485 (January 2014): 853–55. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.853.
Full textAbstract:
Image interpolation is widely studied and used in digital image processing. In this paper, a method of image magnification according to the properties of fractal interpolation and wavelet transformation are presented. We focus the development of edge forming methods to be applied as a post process of standard image zooming methods for grayscale images, with the hope of retaining edges. Experiments make sure it valid.
49
Lin, Chia-Hung, Jian-Liung Chen, and Zwe-Lee Gaing. "Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition." Mathematical Problems in Engineering 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/328676.
Full textAbstract:
This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.
50
Ghaderi, Sadegh. "Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite." Journal of Nanomaterials 2023 (April 21, 2023): 1–11. http://dx.doi.org/10.1155/2023/8564161.
Full textAbstract:
One of the most fundamental subjects in nanoscience and nanotechnology is structural analysis. We employed a scanning electron microscope (SEM) image of the manufactured Gd3+/13X/DOX/FA nanocomposite in this study. The size, dimensions, and morphology of nanocomposite materials were studied to ensure the uniformity and homogeneity of SEM images. This is the first study to look at segmented SEM images for fractal dimension (FD) and other statistical criteria, including average, maximum, minimum, skewness, and range for magnetic resonance imaging (MRI) nanocomposite. The average of FD (FDavg), the standard deviation of FD (FDsd), and the lacunarity of FD (FDlac) fractal data analysis criteria were also employed. The findings show that particle sizes and shapes vary because the minimum-to-maximum range is not zero, and our data provide a reasonable range. This interpretation is further supported by an analysis of the nanocomposite’s SEM image. At first glance, the image seemed to be uniform. However, when the calculations were performed, it was discovered that the generated particles were not particularly uniform. The particles were uniformly dispersed throughout all surfaces, although their sizes, dimensions, and morphologies varied. In conclusion, the study was supported by fractal data analysis, emphasizing the importance of structural analysis for future research, particularly for medical applications like MRI.