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Journal articles on the topic 'Image processing Image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

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 (April 30, 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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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 (February 28, 2018): 925–29. http://dx.doi.org/10.31142/ijtsrd9545.

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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 (April 30, 2018): 1523–26. http://dx.doi.org/10.31142/ijtsrd10995.

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Gaikwad, Anil P., and Bhagyashri R. More. "Digital Watermarking for Image Processing." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 65–67. http://dx.doi.org/10.15373/22501991/jan2013/24.

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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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Krupa, R. Rathna. "An Overview of Image Hiding Techniques in Image Processing." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, no. 02 (April 3, 2014): 01–05. http://dx.doi.org/10.9756/sijcsea/v2i2/0202090202.

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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 (August 9, 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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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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Abdulhamid, Mohanad, and Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation." Radioelectronics. Nanosystems. Information Technologies 12, no. 3 (October 30, 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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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 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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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 (October 31, 2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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12

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 (June 12, 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

Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin, and Ahmed AlGhamdi. "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 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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14

Cawkell, A. E. "Image processing." Information Services & Use 11, no. 5-6 (September 1, 1991): 263–64. http://dx.doi.org/10.3233/isu-1991-115-601.

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15

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

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16

Dowman, I. J. "IMAGE PROCESSING." Photogrammetric Record 9, no. 51 (August 26, 2006): 417–18. http://dx.doi.org/10.1111/j.1477-9730.1978.tb00434.x.

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17

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

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18

Prasad, S. S., and Neelam Bhalla. "Image Processing." Defence Science Journal 52, no. 3 (July 1, 2002): 223–25. http://dx.doi.org/10.14429/dsj.52.2294.

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19

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

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20

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

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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 (April 1, 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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22

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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23

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

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24

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 (October 1998): 1902. http://dx.doi.org/10.1121/1.424210.

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25

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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26

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 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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27

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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28

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 (October 25, 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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29

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

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30

Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 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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Neetu Rani. "Image Processing Techniques: A Review." Journal on Today's Ideas - Tomorrow's Technologies 5, no. 1 (June 28, 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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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, no. 11 (1992): 1439–42. http://dx.doi.org/10.3169/itej1978.46.1439.

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Yasuda, Takami, and 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, no. 11 (1992): 1467–73. http://dx.doi.org/10.3169/itej1978.46.1467.

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A, Soundarya B., Sowmeya D, and Yazhini G. V. "Visual Cryptography for Image Processing: A Survey." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1417–21. http://dx.doi.org/10.31142/ijtsrd8206.

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35

S, Jones Arthi, Kousalya R, and Kumari M. "Manifest Electronic Voting Machine Using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1893–98. http://dx.doi.org/10.31142/ijtsrd11509.

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36

Dhurat, Bhakti, Mayuri Ranmale, S. R. Todmal, and Dipak Mahajan Rushikesh. "Online Toll Payment System Using Image Processing." Journal of Advances and Scholarly Researches in Allied Education 15, no. 3 (May 1, 2018): 17–21. http://dx.doi.org/10.29070/15/56748.

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37

Janmane, Akshay S., Apeksha S. Patil, and Madhuri V. Huilgol. "FPGA Based Real Time Medical Image Processing." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 113–16. http://dx.doi.org/10.9756/bijrce.8214.

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Chavan1, Manjushree K., Anisha V, Jeevika mary A, Joyseelin priyanka J, and Monisha K. "EVALUATION OF SOIL FERTILITY USING IMAGE PROCESSING." International Journal of Current Engineering and Scientific Research 6, no. 6 (June 2019): 50–52. http://dx.doi.org/10.21276/ijcesr.2019.6.6.10.

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39

Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (February 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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Shivajirao Shinde, Bhausaheb. "The Origins of Digital Image Processing & Application areas in Digital Image Processing Medical Images." IOSR Journal of Engineering 1, no. 1 (November 2011): 66–71. http://dx.doi.org/10.9790/3021-0116671.

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41

V, Srujana, Chaithanya P, Ramesh B, Manoranjan S, and Mahesh V. "Crop Analysis Using Image Processing." International Journal of Engineering Technology and Management Sciences 4, no. 3 (May 28, 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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42

Bright, David S. "Basic image processing for the microscopist." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 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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Nagy, Marius, and Naya Nagy. "Image processing: why quantum?" Quantum Information and Computation 20, no. 7&8 (June 2020): 616–26. http://dx.doi.org/10.26421/qic20.7-8-6.

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Quantum Image Processing has exploded in recent years with dozens of papers trying to take advantage of quantum parallelism in order to offer a better alternative to how current computers are dealing with digital images. The vast majority of these papers define or make use of quantum representations based on very large superposition states spanning as many terms as there are pixels in the image they try to represent. While such a representation may apparently offer an advantage in terms of space (number of qubits used) and speed of processing (due to quantum parallelism), it also harbors a fundamental flaw: only one pixel can be recovered from the quantum representation of the entire image, and even that one is obtained non-deterministically through a measurement operation applied on the superposition state. We investigate in detail this measurement bottleneck problem by looking at the number of copies of the quantum representation that are necessary in order to recover various fractions of the original image. The results clearly show that any potential advantage a quantum representation might bring with respect to a classical one is paid for dearly with the huge amount of resources (space and time) required by a quantum approach to image processing.

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44

Tan, Joshia. "Biomedical Image Processing." International Journal of Healthcare Information Systems and Informatics 7, no. 1 (January 2012): 64–67. http://dx.doi.org/10.4018/jhisi.2012010105.

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Likforman-Sulem, Laurence, and Ergina Kavallieratou. "Document Image Processing." Journal of Imaging 4, no. 7 (June 22, 2018): 84. http://dx.doi.org/10.3390/jimaging4070084.

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46

Koschan, Andreas. "Parallel Image Processing." AI Communications 8, no. 1 (1995): 44–45. http://dx.doi.org/10.3233/aic-1995-8107.

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Rainer, R. Kelly, Houston H. Carr, Charles A. Snyder, and Glenda Deneen. "IMAGE PROCESSING SYSTEMS." Information Systems Management 9, no. 3 (January 1992): 30–37. http://dx.doi.org/10.1080/10580539208906879.

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48

Kula, Timothy S., Raymond Konopka, and John A. Cicero. "Image Processing experiments." ACM SIGCSE Bulletin 22, no. 1 (February 1990): 167–70. http://dx.doi.org/10.1145/319059.323441.

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49

Percival, Donald B. "Digital Image Processing." Technometrics 31, no. 1 (February 1989): 124–25. http://dx.doi.org/10.1080/00401706.1989.10488494.

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50

Conci, Aura, and Angel Sanchez. "Scientific Image Processing." Computing in Science & Engineering 13, no. 3 (May 2011): 6–8. http://dx.doi.org/10.1109/mcse.2011.50.

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