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Artículos de revistas sobre el tema "Image processing"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Shete, S. G., and Nagnath G. Ghadge. "Image Processing in MATLAB 9.3." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 925–29. https://doi.org/10.31142/ijtsrd9545.

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In this paper we introduce how to handle different kinds of image formats in MATLAB 9.3 by using Matlab Workspace and its Various Commands. Also we illustrated example of processing the images. Shete S. G. | Ghadge Nagnath G. "Image Processing in MATLAB 9.3" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: https://www.ijtsrd.com/papers/ijtsrd9545.pdf

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2

Mohamed Y Abdallah, Yousif, Mohamed MO Yousef, and Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 557–64. https://doi.org/10.21275/sr21709185141.

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3

Peng, Er Bao, and Guo Tong Zhang. "Image Processing Technology Research of On-Line Thread Processing." Advanced Materials Research 908 (March 2014): 555–58. http://dx.doi.org/10.4028/www.scientific.net/amr.908.555.

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The paper introduced image processing technology based on image segmentation about on-line threads images, and describes in detail image processing technology from image preprocessing, image gmentation,and threaded parameter test. Threaded images of on-line processing parts obtained are introduced as the key technology, Target edge extraction process from the segmented image are also recounted. At last, this article shows a comparison between actual machining parameters of screw thread and the standard parameter , provides the criterion for error compensation.

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4

Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab env

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5

Goto, Mitsunori. "8. Image Processing Using ImageJ." Japanese Journal of Radiological Technology 75, no. 7 (2019): 688–92. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.7.688.

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6

Arti Verma. "Digital Image Processing of SEM image of Polymer Nanocomposite Thin Film Using Java Based Program Image J." Power System Technology 43, no. 2 (2019): 44–46. https://doi.org/10.52783/pst.1135.

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Various images obtain from TEM; SEM can be analyzed with the help of computer image analysis software. Image processing is used to describe the size, shape, surface topography of micro or nano structure materials. In the present paper the characterization analysis is being reported qualitatively by using digital image processing of SEM/TEM image of some nanomaterials. This novel technique is an effective experimental tool for the detailed structural characterization. For image processing Java based software ImageJ is used in the present study.

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7

Patel, Bindiya, Dr Pankaj Kumar Mishra, and Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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8

x, Priyanka. "Review Paper on Identifying Fake Images by Digital Image Processing." International Journal of Scientific Engineering and Research 5, no. 5 (2017): 169–71. https://doi.org/10.70729/ijser151486.

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9

Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, et al. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.

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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months o

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10

Ulkar Huseynova, Anakhanim Mutallimova, Ulkar Huseynova, Anakhanim Mutallimova. "DIGITAL IMAGE PROCESSING." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 36, no. 01 (2024): 179–88. http://dx.doi.org/10.36962/pahtei36012024-179.

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Digital processing and subsequent picture identification are one of the scientific fields that is now experiencing rapid development. Currently, a lot of technology is focused on developing systems that use graphical images as information, including receiving, processing, storing, and transmitting information. Two primary areas of use for digital image processing methods are of interest: 1. Increasing image quality to enhance human visual perception. 2. Image processing for use in autonomous machine vision systems, including storage, transmission, and presentation. The fundamentals of digital

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11

Ottapura, Sayooj, Rahul Mistry, Jatin Keni, and Chaitanya Jage. "Underwater Image Processing using Graphics Processing Unit (GPU)." ITM Web of Conferences 32 (2020): 03041. http://dx.doi.org/10.1051/itmconf/20203203041.

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Image processing is a method used for enhancement of an image or to extract some useful information from the image. It is a type of signal processing in which input is an image and output may be an image or any characteristics/features associated with that image. In this paper we will be focusing on a specific type of Image Processing i.e. Underwater Image Processing. Underwater Image Processing has always faced the problem of imbalance in colour distribution and this problem can be tackled by the simplest algorithm for colour balancing. We will be proceeding with the assumption that the highe

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12

Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь, and Cheng Jun. "Guided filtering for solar image/video processing." Solar-Terrestrial Physics 3, no. 2 (2017): 9–15. http://dx.doi.org/10.12737/stp-3220172.

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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results pr

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13

Strauss, Lourens Jochemus, and William ID Rae. "Image quality dependence on image processing software in computed radiography." South African Journal of Radiology 16, no. 2 (2012): 44–48. http://dx.doi.org/10.4102/sajr.v16i2.305.

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Background. Image post-processing gives computed radiography (CR) a considerable advantage over film-screen systems. After digitisation of information from CR plates, data are routinely processed using manufacturer-specific software. Agfa CR readers use MUSICA software, and an upgrade with significantly different image appearance was recently released: MUSICA2. Aim. This study quantitatively compares the image quality of images acquired without post-processing (flatfield) with images processed using these two software packages. Methods. Four aspects of image quality were evaluated.

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14

Bardhan, Yash, Tejas A. Fulzele, and Prabhat Ranjan Shekhar Upadhyay Prof V. D. Bharate. "Emotion Recognition using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1523–26. http://dx.doi.org/10.31142/ijtsrd10995.

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15

G., Shete S., and Ghadge Nagnath G. "Image Processing in MATLAB 9.3." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 925–29. http://dx.doi.org/10.31142/ijtsrd9545.

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16

Singh, Mohit. "Image Processing - A Quick Survey." International Journal of Science and Research (IJSR) 10, no. 12 (2021): 853–54. https://doi.org/10.21275/sr211208231506.

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17

Gaikwad, Anil P., and Bhagyashri R. More. "Digital Watermarking for Image Processing." Paripex - Indian Journal Of Research 2, no. 1 (2012): 65–67. http://dx.doi.org/10.15373/22501991/jan2013/24.

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18

Ibrahim, Nur Aifiah Binti, and Shah Alam. "Currency Authenticator Using Image Processing." International Journal of Research Publication and Reviews 6, no. 4 (2025): 1709–16. https://doi.org/10.55248/gengpi.6.0425.1367.

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19

Alessandro, Massaro, Vitti Valeria, and Galiano Angelo. "Automatic Image Processing Engine Oriented on Quality Control of Electronic Boards." Signal & Image Processing : An International Journal (SIPIJ) 9, no. 2 (2019): 1–14. https://doi.org/10.5281/zenodo.3243307.

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ABSTRACT We propose in this work a study of an image processing engine able to detect automatically the features of electronic board weldings. The engine has been developed by using ImageJ and OpenCV libraries. Specifically the image processing segmentation has been improved by watershed approach. After a complete design of the automation processes, different test have been performed showing the engine efficiency in terms of features extraction, scale setting and thresholding calibration. The engine provides as outputs the storage of the cropped images of each single defects. The proposed engi

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20

Abdulhamid, Mohanad, and Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation." Radioelectronics. Nanosystems. Information Technologies 12, no. 3 (2020): 407–14. http://dx.doi.org/10.17725/rensit.2020.12.407.

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Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison o

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21

Malathi, M., and P. Sinthia. "An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing." Journal of Computational and Theoretical Nanoscience 18, no. 4 (2021): 1251–55. http://dx.doi.org/10.1166/jctn.2021.9388.

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The main objective of the research work is to recognize the rust of the substance with the help of Image Processing. The recognition of the rust portion of an image is carried out by quantizing of image in matrix form. The quantization process helps to perform the fundamental operation on image and also helps to identify the desired oxidation portion of an image. The corrosion portion was identified through the threshold operation, edge detection and segmentation. Threshold value assists to describe the types of the rust. Further the abrupt modification of colour in the images was captured by

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22

Maria Riasat. "Research on various image processing techniques." Open Access Research Journal of Chemistry and Pharmacy 1, no. 1 (2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-pro

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23

Krupa, R. Rathna. "An Overview of Image Hiding Techniques in Image Processing." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, no. 02 (2014): 01–05. http://dx.doi.org/10.9756/sijcsea/v2i2/0202090202.

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24

Weiss, Scott. "Image processing." ACM Inroads 13, no. 3 (2022): 56. http://dx.doi.org/10.1145/3555687.

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25

Cawkell, A. E. "Image processing." Information Services & Use 11, no. 5-6 (1991): 263–64. http://dx.doi.org/10.3233/isu-1991-115-601.

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26

McWhinnie, Harold J. "Image Processing." Leonardo. Supplemental Issue 1 (1988): 119. http://dx.doi.org/10.2307/1557925.

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27

Terrell, Trevor J. "Image Processing." IEE Review 37, no. 10 (1991): 355. http://dx.doi.org/10.1049/ir:19910160.

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28

Jackson, A. "Image processing." British Journal of Radiology 77, suppl_2 (2004): S107. http://dx.doi.org/10.1259/bjr/23442591.

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29

Prasad, S. S., and Neelam Bhalla. "Image Processing." Defence Science Journal 52, no. 3 (2002): 223–25. http://dx.doi.org/10.14429/dsj.52.2294.

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30

Dowman, I. J. "IMAGE PROCESSING." Photogrammetric Record 9, no. 51 (2006): 417–18. http://dx.doi.org/10.1111/j.1477-9730.1978.tb00434.x.

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31

Engel, A. "Image processing." Ultramicroscopy 28, no. 1-4 (1989): 290–91. http://dx.doi.org/10.1016/0304-3991(89)90310-0.

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32

A, Suguna, Dinesh B V, Nithin S C, and Adarsh N S. "Image Processing." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 79–83. http://dx.doi.org/10.26562/ijiris.2023.v0903.06.

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Image processing entails altering an image's composition to enhance its graphical content for human interpretation and autonomous machine perception. Digital image processing is a subset of the electronic domain in which an image is transformed into a collection of tin y numbers, or pixels, that reflect a physical property, like s cene radiance, are stored in a digital memory, and are then processed by a computer or other digital hardware. Two key application areas have sparked interest in digital i mage processing techniques: improving pictorial information for human interpretation and proces

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33

Toriwaki, Jun-ichiro. "Special Issue Image Processing. 1. Image Processing. Introduction." Journal of the Institute of Television Engineers of Japan 46, no. 11 (1992): 1386–92. http://dx.doi.org/10.3169/itej1978.46.1386.

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34

Mochizuki, Takashi. "Image processing apparatus and image processing method for use in the image processing apparatus." Journal of the Acoustical Society of America 104, no. 4 (1998): 1902. http://dx.doi.org/10.1121/1.424210.

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35

Mamatha, R., P. Sowmya, B. Suprathika, B. Abhigna, and Ch. Nissi. "Enhancing Underwater Image Quality through Image Processing." International Journal of Research 11, no. 5 (2024): 1–6. https://doi.org/10.5281/zenodo.11118384.

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<strong>Underwater images find application in various fields, like marine research, inspection of aquatic habitat, underwater surveillance, identification of minerals, and more. Longer wavelengths are absorbed by water at greater depths. Due to the greater wavelength, red is absorbed and the images seem primarily bluish-green. Images suffer greatly as a result of these phenomena, which causes low contrast, color distortion, and poor visibility. Underwater photos must therefore be improved in order to retain the important information they provide and make them suitable for a variety of uses. </

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36

Utkarsh, Gupta, Kumar Sudhanshu, Singhal Devansh, Tomar Parth, and Kumar Ajay. "IMAGE PROCESSING SYSTEM USING JAVA." International Journal of Innovative Research in Information Security VII, no. IV (2020): 36–40. https://doi.org/10.26562/ijiris.2020.v0704.002.

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The article is all about the Image Processing System that can be defined as, processing and altering an existing image in the desired manner. Image is one of the perceptible sources in applications of Image Processing including a large number of tools and techniques which help to extract complex features of an image. Probably the most powerful image processing system is the human brain together with the eye. The system receives, enhances, and stores images at enormous rates of speed. The objective of Image Processing is to visually enhance or statistically evaluate some aspect of an image not

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37

Laing, Ronald A. "Image Processing of Corneal Endothelial Images." Cornea 6, no. 1 (1987): 65. http://dx.doi.org/10.1097/00003226-198706010-00053.

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38

Suenaga, Yasuhito. "Special Issue Image Processing. 3. New Application of Image Processing. 3-2 Image Processing for Better Communication-Recognition of Human Images-." Journal of the Institute of Television Engineers of Japan 46, no. 11 (1992): 1443–47. http://dx.doi.org/10.3169/itej1978.46.1443.

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39

RajeshwariK Rai. "Applications of Image Processing." Pacific International Journal 1, no. 2 (2018): 55–56. http://dx.doi.org/10.55014/pij.v1i2.41.

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This research paper is an attempt to explore the applications of image processing in various fields such as healthcare and public services. This paper provides an overview of the importance of image processing and the various tools that can be used for analyzing videos and images.

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40

LIN, YUE-DER, HEN-WEI TSAO, and FOK-CHING CHONG. "AN IMAGE PROCESSING ARCHITECTURE TO ENHANCE IMAGE CONTRAST." Biomedical Engineering: Applications, Basis and Communications 14, no. 05 (2002): 215–17. http://dx.doi.org/10.4015/s1016237202000310.

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To have a good image contrast is an important issue in medical images. This paper introduces a feedback-type image processing architecture that can enhance image contrast without further digital image processing technique, e.g. histogram equalization. Compared with the conventional open-loop imaging system, the images derived by the proposed method has a full-range histogram without causing image distortion, and this is difficult to attain for open-loop imaging system.

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41

Nandan, Anamika, and Dr Satya Ranjan Pattanaik. "Brain Tumor Detection using Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36565.

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It is very difficult for doctors to detect a brain tumor at an early stage. MRI images are more susceptible to noise and other environmental disturbances. Therefore, it becomes difficult for doctors to determine the tumor and its causes. So, we came up with a system in which the system will detect a brain tumor from images. Here we are converting an image to a grayscale image. We apply filters to the image to remove noise and other environmental clutter from the image. The system will process the selected image using preprocessing steps. At the same time, different algorithms are used to detec

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42

Dr., Inderdeep Verma, Sharma Aditi, Upadhyay Khushi, and Manjari. "TURBIDITY DETECTION USING IMAGE PROCESSING." International Journal of Engineering Sciences & Emerging Technologies 10, no. 6 (2021): 154–60. https://doi.org/10.5281/zenodo.5211306.

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<em>The motive behind this project is to design a technique to determine turbidity of water samples using image processing. Traditional method of turbidity detection implies various manual and heavy procedures. By using image processing, it can be done in a simple way and that too anywhere and anytime. The proposed technique consists of water sample images captured by HD camera and applying concepts of image processing to those captured images. This method is more reasonable and scientific.</em>

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43

Jyotika, Kapur, and J. Baregar Akshay. "Security Using Image Processing." International Journal of Managing Information Technology (IJMIT 5, no. 2 (2013): 13 to 21. https://doi.org/10.5281/zenodo.3865132.

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Using image stitching and image steganography security can be provided to any image which has to be sent over the network or transferred using any electronic mode. There is a message and a secret image that has to be sent. The secret image is divided into parts.The first phase is the Encrypting Phase, which deals with the process of converting the actual secret message into ciphertext using the AES algorithm. In the second phase which is the Embedding Phase, the cipher text is embedded into any part of the secret image that is to be sent. Third phase is the Hiding Phase, where steganography is

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44

Kotsiubivska, Kateryna, and Viktoria Tymoshenko. "Mathematical Methods of Image Processing." Digital Platform: Information Technologies in Sociocultural Sphere 2, no. 1 (2019): 41–54. https://doi.org/10.31866/2617-796x.2.1.2019.175653.

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The purpose of the study is to study the specificity of image encoding by spline interpolation, and the equation of the indicated method with other mathematical methods of encoding and image processing. Research methods. The mathematical and algorithmic models and methods of solving the problem of smoothing on the basis of spline approximation, as well as the possibility of using an appropriate mathematical apparatus for encoding and image processing. The novelty of the research is the isolation of the compression algorithm of images based on the methods of spline approximation. This

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45

Rajab Asaad, Renas, Rasan Ismael Ali, Zeravan Arif Ali, and Awaz Ahmad Shaaban. "Image Processing with Python Libraries." Academic Journal of Nawroz University 12, no. 2 (2023): 410–16. http://dx.doi.org/10.25007/ajnu.v12n2a1754.

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Today, computer vision is considered one of the most important sub-fields of artificial intelligence, due to the variety of its applications and capabilities to transfer the human ability to understand and describe a scene or image to the computer, so that it becomes able to recognize objects, shapes, colors, behavior and other capabilities of understanding the image. Image processing is one of the branches of computer science, and it is a way to perform some operations on an image in order to obtain an improved model for this image or extract some useful information from it. Often the data th

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46

Mehraj, Nadiya, and Harveen Kour. "Data Processing Through Image Processing using Gaussian Minimum Shift Keying." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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47

Hovmöller, Sven. "Image processing and image simulation." Ultramicroscopy 36, no. 4 (1991): 275–76. http://dx.doi.org/10.1016/0304-3991(91)90120-u.

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48

Han, Hsiao-Yu, Jessen Chen, Yu-Chu Huang, Shyh-Hsing Wang, Yao-Wen Huang, and Jane Chang. "Image Processing System for Image Enhancement and Halftone Processing." NIP & Digital Fabrication Conference 20, no. 1 (2004): 477–82. http://dx.doi.org/10.2352/issn.2169-4451.2004.20.1.art00105_1.

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49

Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compres

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50

Saxena, Khushboo, and Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 253–58. http://dx.doi.org/10.17762/ijritcc.v11i3s.6232.

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Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

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