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Artículos de revistas sobre el tema "Image processing"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 7 de diciembre de 2024

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1

Peng, Er Bao y Guo Tong Zhang. "Image Processing Technology Research of On-Line Thread Processing". Advanced Materials Research 908 (marzo de 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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2

Legland, David y Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab". F1000Research 10 (30 de abril de 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 environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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3

Goto, Mitsunori. "8. Image Processing Using ImageJ". Japanese Journal of Radiological Technology 75, n.º 7 (2019): 688–92. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.7.688.

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4

Patel, Bindiya, Dr Pankaj Kumar Mishra y 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 (30 de abril de 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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5

Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь y Cheng Jun. "Guided filtering for solar image/video processing". Solar-Terrestrial Physics 3, n.º 2 (9 de agosto de 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 prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.

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6

Ulkar Huseynova, Anakhanim Mutallimova, Ulkar Huseynova, Anakhanim Mutallimova. "DIGITAL IMAGE PROCESSING". PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 36, n.º 01 (23 de enero de 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 image processing are covered in the first portion, and image operations including quantization, sampling, and alpha compositing are covered in the second. Bitmap storage is covered in the fourth part, and image compression algorithms such as RLE and LZW are covered in the third. The topic of enhancing image quality is covered in the fifth part. The concepts of grouping, segmenting, and object search in an image are covered in the sixth section. The Radon transform is used in the seventh section to discover grid structures and straight lines in an image. Keywords: image processing, quantization, segmenting

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7

Bardhan, Yash, Tejas A. Fulzele y 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 (30 de abril de 2018): 1523–26. http://dx.doi.org/10.31142/ijtsrd10995.

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8

G., Shete S. y Ghadge Nagnath G. "Image Processing in MATLAB 9.3". International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (28 de febrero de 2018): 925–29. http://dx.doi.org/10.31142/ijtsrd9545.

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9

Gaikwad, Anil P. y Bhagyashri R. More. "Digital Watermarking for Image Processing". Paripex - Indian Journal Of Research 2, n.º 1 (15 de enero de 2012): 65–67. http://dx.doi.org/10.15373/22501991/jan2013/24.

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10

Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin y Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU". Mathematical Problems in Engineering 2020 (10 de octubre de 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 of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

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11

Ottapura, Sayooj, Rahul Mistry, Jatin Keni y 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 highest values of R, G, B observed in the image corresponds to white and the lowest values corresponds to darkness. The underwater images are majorly saturated by blue colour because of its short wavelength and in this paper, we aim to enhance the image. We proposed a colour balancing algorithm for normalizing the image. The entire process will first be carried out on a CPU followed by a GPU. We will then compare the speedup obtained. Speedup is an important parameter in the field on image processing since a better speedup can help reduce the computation time significantly while maintaining a higher efficiency.

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12

Strauss, Lourens Jochemus y William ID Rae. "Image quality dependence on image processing software in computed radiography". South African Journal of Radiology 16, n.º 2 (12 de junio de 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. An aluminium step-wedge was imaged using constant mA at tube voltages varying from 40 to 117kV. Signal-to-noise ratios (SNRs) and contrast-to-noise Ratios (CNRs) were calculated from all steps. Contrast variation with object size was evaluated with visual assessment of images of a Perspex contrast-detail phantom, and an image quality figure (IQF) was calculated. Resolution was assessed using modulation transfer functions (MTFs). Results. SNRs for MUSICA2 were generally higher than the other two methods. The CNRs were comparable between the two software versions, although MUSICA2 had slightly higher values at lower kV. The flatfield CNR values were better than those for the processed images. All images showed a decrease in CNRs with tube voltage. The contrast-detail measurements showed that both MUSICA programmes improved the contrast of smaller objects. MUSICA2 was found to give the lowest (best) IQF; MTF measurements confirmed this, with values at 3.5 lp/mm of 10% for MUSICA2, 8% for MUSICA and 5% for flatfield. Conclusion. Both MUSICA software packages produced images with better contrast resolution than unprocessed images. MUSICA2 has slightly improved image quality than MUSICA.

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13

Krupa, R. Rathna. "An Overview of Image Hiding Techniques in Image Processing". SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, n.º 02 (3 de abril de 2014): 01–05. http://dx.doi.org/10.9756/sijcsea/v2i2/0202090202.

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14

Abdulhamid, Mohanad y Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation". Radioelectronics. Nanosystems. Information Technologies 12, n.º 3 (30 de octubre de 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 of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.

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15

Malathi, M. y P. Sinthia. "An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing". Journal of Computational and Theoretical Nanoscience 18, n.º 4 (1 de abril de 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 the edge detection method. Consequently partitioning of an image find the colour changes in the oxidized image. The corrosion portion was recognized by combining the edge recognition and partitioning process. Finally recommended methods provide the 98% accuracy to detect the rust.

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16

Weiss, Scott. "Image processing". ACM Inroads 13, n.º 3 (septiembre de 2022): 56. http://dx.doi.org/10.1145/3555687.

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17

Cawkell, A. E. "Image processing". Information Services & Use 11, n.º 5-6 (1 de septiembre de 1991): 263–64. http://dx.doi.org/10.3233/isu-1991-115-601.

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18

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

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19

Terrell, Trevor J. "Image Processing". IEE Review 37, n.º 10 (1991): 355. http://dx.doi.org/10.1049/ir:19910160.

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20

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

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21

Prasad, S. S. y Neelam Bhalla. "Image Processing". Defence Science Journal 52, n.º 3 (1 de julio de 2002): 223–25. http://dx.doi.org/10.14429/dsj.52.2294.

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22

Dowman, I. J. "IMAGE PROCESSING". Photogrammetric Record 9, n.º 51 (26 de agosto de 2006): 417–18. http://dx.doi.org/10.1111/j.1477-9730.1978.tb00434.x.

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23

Engel, A. "Image processing". Ultramicroscopy 28, n.º 1-4 (abril de 1989): 290–91. http://dx.doi.org/10.1016/0304-3991(89)90310-0.

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24

A, Suguna, Dinesh B V, Nithin S C y Adarsh N S. "Image Processing". International Journal of Innovative Research in Information Security 09, n.º 03 (23 de junio de 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 processing image data for storage, transmission, and representation for autonomous machine perception. Boundaries are described by edges, and as edge detection i s one of the most challenging image processing tasks, it is an issue of fundamental significance .

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25

Toriwaki, Jun-ichiro. "Special Issue Image Processing. 1. Image Processing. Introduction." Journal of the Institute of Television Engineers of Japan 46, n.º 11 (1992): 1386–92. http://dx.doi.org/10.3169/itej1978.46.1386.

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26

Mochizuki, Takashi. "Image processing apparatus and image processing method for use in the image processing apparatus". Journal of the Acoustical Society of America 104, n.º 4 (octubre de 1998): 1902. http://dx.doi.org/10.1121/1.424210.

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27

Maria Riasat. "Research on various image processing techniques". Open Access Research Journal of Chemistry and Pharmacy 1, n.º 1 (30 de diciembre de 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-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.

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28

Laing, Ronald A. "Image Processing of Corneal Endothelial Images". Cornea 6, n.º 1 (1987): 65. http://dx.doi.org/10.1097/00003226-198706010-00053.

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29

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, n.º 11 (1992): 1443–47. http://dx.doi.org/10.3169/itej1978.46.1443.

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30

RajeshwariK Rai. "Applications of Image Processing". Pacific International Journal 1, n.º 2 (30 de junio de 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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31

LIN, YUE-DER, HEN-WEI TSAO y FOK-CHING CHONG. "AN IMAGE PROCESSING ARCHITECTURE TO ENHANCE IMAGE CONTRAST". Biomedical Engineering: Applications, Basis and Communications 14, n.º 05 (25 de octubre de 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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32

Mehraj, Nadiya y 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 (31 de octubre de 2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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33

Rajab Asaad, Renas, Rasan Ismael Ali, Zeravan Arif Ali y Awaz Ahmad Shaaban. "Image Processing with Python Libraries". Academic Journal of Nawroz University 12, n.º 2 (1 de junio de 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 that is collected is primary data, meaning that it is not suitable for direct use in applications, so its need to analyze or pre-process it and then use it. For example: to build a data set that has been used in a model that classifies images as to whether they contain a house or not, depending on an image as input for this program. Our first step will be to collect hundreds of house images, but the problem is that these images will not be of the same dimensions, for example, so it’s to Change its dimensions, i.e., processing it in advance before submitting it to the model. The above is just one of the many reasons why image processing is important for any computer vision application

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34

Nandan, Anamika y Dr Satya Ranjan Pattanaik. "Brain Tumor Detection using Image Processing". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 07 (22 de julio de 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 detect the tumor from the image. But the edges of the image will not be sharp in the early stages of a brain tumor. So here we are applying image segmentation to the image to detect the edges of the images. We have proposed an image segmentation process and a variety of image filtering techniques to obtain image characteristics. Through this entire process, accuracy can be improved. This system is implemented in the Matlab. Keywords : Brain Tumor, classification, Segmentation.

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35

Shitole, Dinesh, Faisal Tamboli y Krishna Motghare Raj Kumar Raj. "Ayurvedic Herb Detection using Image Processing". International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30 de junio de 2019): 491–94. http://dx.doi.org/10.31142/ijtsrd23605.

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36

C Berejena Fungayi, Brighton. "Road Traffic Prioritization using Image Processing". International Journal of Science and Research (IJSR) 12, n.º 6 (5 de junio de 2023): 694–99. http://dx.doi.org/10.21275/sr221129111355.

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37

Hovmöller, Sven. "Image processing and image simulation". Ultramicroscopy 36, n.º 4 (septiembre de 1991): 275–76. http://dx.doi.org/10.1016/0304-3991(91)90120-u.

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38

Han, Hsiao-Yu, Jessen Chen, Yu-Chu Huang, Shyh-Hsing Wang, Yao-Wen Huang y Jane Chang. "Image Processing System for Image Enhancement and Halftone Processing". NIP & Digital Fabrication Conference 20, n.º 1 (1 de enero de 2004): 477–82. http://dx.doi.org/10.2352/issn.2169-4451.2004.20.1.art00105_1.

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39

Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing". International Journal of Online and Biomedical Engineering (iJOE) 16, n.º 12 (19 de octubre de 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 compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

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40

Yagi, Nobuyuki. "Special Issue Image Processing. 3. New Application of Image Processing. 3-1 Image Processing in Broadcasting." Journal of the Institute of Television Engineers of Japan 46, n.º 11 (1992): 1439–42. http://dx.doi.org/10.3169/itej1978.46.1439.

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41

Yasuda, Takami y Shigeki Yokoi. "Special Issue Image Processing. 3. New Application of Image Processing. 3-5 Image Processing in Medicine." Journal of the Institute of Television Engineers of Japan 46, n.º 11 (1992): 1467–73. http://dx.doi.org/10.3169/itej1978.46.1467.

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42

Premalatha, Mrs M., A. Heymath Kumar, M. Manoj Kumar, P. Pavithran y K. Shatyadeep. "Drugged Eye Detection Using Image Processing". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 1577–82. http://dx.doi.org/10.22214/ijraset.2023.50427.

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Abstract: Drugs are a major problem in economic and many losses in worldwide. In this project, an image processing approach is proposed for identifying drugged eye based on convolutional neural network. According to the CNN algorithm, eye image details are taken by the existing packages from the front end used in this project. However, it can take a few moments. So, this proposed system can be used to identify drugged eyes quickly and automatically. The eye images dataset are taken from Kaggle. These images are taken as a training set for this drugged eye detection. This proposed approach is composed of the following main steps that getting input image, Image Preprocessing, identifying reddish places, highlight those affected places, Verifying training set, showing result. Few types of eyes like drugged socially may missed to identify. This approach was tested according to drugged eye type and its' stages, such as drug consumed and not consumed. The algorithm was used for detecting the white area of eye present in given input image. Images were provided for training, such as drugged eye images and normal eye images. Before the image processing, images were converted to color models, because of find out the most suitable color model for this approach. Local Binary Pattern was used for feature extraction and Support erosion method was used for creating the model. According to this approach, drugged eyes can be identified in the average accuracy of 95%.

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43

Shivajirao Shinde, Bhausaheb. "The Origins of Digital Image Processing & Application areas in Digital Image Processing Medical Images". IOSR Journal of Engineering 1, n.º 1 (noviembre de 2011): 66–71. http://dx.doi.org/10.9790/3021-0116671.

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44

Hoshi, Sujin, Kuniharu Tasaki, Kazushi Maruo, Yuta Ueno, Haruhiro Mori, Shohei Morikawa, Yuki Moriya, Shoko Takahashi, Takahiro Hiraoka y Tetsuro Oshika. "Improvement in Dacryoendoscopic Visibility after Image Processing Using Comb-Removal and Image-Sharpening Algorithms". Journal of Clinical Medicine 11, n.º 8 (7 de abril de 2022): 2073. http://dx.doi.org/10.3390/jcm11082073.

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Recently, a minimally invasive treatment for lacrimal passage diseases was developed using dacryoendoscopy. Good visibility of the lacrimal passage is important for examination and treatment. This study aimed to investigate whether image processing can improve the dacryoendoscopic visibility using comb-removal and image-sharpening algorithms. We processed 20 dacryoendoscopic images (original images) using comb-removal and image-sharpening algorithms. Overall, 40 images (20 original and 20 post-processing) were randomly presented to the evaluators, who scored each image on a 10-point scale. The scores of the original and post-processing images were compared statistically. Additionally, in vitro experiments were performed using a test chart to examine whether image processing could improve the dacryoendoscopic visibility in a turbid fluid. The visual score (estimate ± standard error) of the images significantly improved from 3.52 ± 0.26 (original images) to 5.77 ± 0.28 (post-processing images; p < 0.001, linear mixed-effects model). The in vitro experiments revealed that the contrast and resolution of images in the turbid fluid improved after image processing. Image processing with our comb-removal and image-sharpening algorithms improved dacryoendoscopic visibility. The techniques used in this study are applicable for real-time processing and can be easily introduced in clinical practice.

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Neetu Rani. "Image Processing Techniques: A Review". Journal on Today's Ideas - Tomorrow's Technologies 5, n.º 1 (28 de junio de 2017): 40–49. http://dx.doi.org/10.15415/jotitt.2017.51003.

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In today’s scenario image processing is one of the vast growing fields. It is a method which is commonly used to improve raw images which are received from various resources. It is a kind of signal processing. This paper provides an overview of image processing methods. The main concern of this paper is to define various techniques used in different phases of image processing.

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Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing". International Journal of Pattern Recognition and Artificial Intelligence 11, n.º 01 (febrero de 1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.

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A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful applications. In this paper, we discuss several aspects of Bayesian paradigms: uncertainty present in the observed image, prior distribution modeling, Bayesian-based estimation techniques in image processing, particularly, the maximum a posteriori estimator and the Kalman filtering theory, robustness, and Markov random fields and applications.

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Bright, David S. "Basic image processing for the microscopist". Proceedings, annual meeting, Electron Microscopy Society of America 50, n.º 2 (agosto de 1992): 1786–87. http://dx.doi.org/10.1017/s0424820100133564.

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Images are a rich source of easily interpreted information. Equipment capable of handling pictures in computer memory (digital images) is now commonplace, and imaging is becoming an integral part of scientific computing and data analysis. Today, desk top computers are capable of displaying images with no special equipment at all. For example, NIH Image runs on a standard MAC II, and provides tools for contrast enhancement, pseudocolor, sharpening, animation, labeling and particle counting. Scanners or video cameras to input gray level (or color) images are moderately priced, and scanning microscopes are coming equipped for digital data acquisition and control. This tutorial will review some of the basic principles of image processing using example micrographs, and will illustrate how to use some readily available image processing software.Digital images are pictures that reside in the computer as arrays of numbers, each number designating the brightness of an individual picture element or pixel.

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V, Srujana, Chaithanya P, Ramesh B, Manoranjan S y Mahesh V. "Crop Analysis Using Image Processing". International Journal of Engineering Technology and Management Sciences 4, n.º 3 (28 de mayo de 2020): 9–15. http://dx.doi.org/10.46647/ijetms.2020.v04i03.002.

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To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.

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Saxena, Khushboo y Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 3s (18 de marzo de 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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Surin, V. A. "ON PROCESSING NOISY CONTRAST IMAGES". Bulletin of the South Ural State University series "Mathematics. Mechanics. Physics" 13, n.º 1 (2021): 14–21. http://dx.doi.org/10.14529/mmph210102.

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The problem of noise reduction at sharp transitions of brightness in digital noisy contrast images is considered. In addition to the useful signal, digital images obtained by digitizing an analogue signal with a digital photo matrix have a noise component. Moreover, to obtain a digital image in the standard RGB color model, a demosaicing interpolation algorithm must be applied to the image obtained from a digital photo matrix. Due to such transformations, the Gaussian distribution of noise in a digital noisy image is violated. Using a standard image digitization model for noise reduction is not effective. For more effective noise reduction, the digital image is transferred from the RGB color model to the HSV or LAB color model, where the brightness and color components of the digital noise can be filtered separately. Color noise is suppressed in the color channels of the image using a Gaussian filter. Noise reduction in the brightness channel of a digital image is more difficult task, especially at the edge of sharp transitions of brightness. To suppress the brightness noise in contrast images, it is proposed to use a nonlinear filter based on the generalized method of least absolute values (GMLAV). The process of smoothing the contrast noisy transition by the GMLAV-filter is described, and its efficiency is shown in comparison with the median filtration.

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