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Artículos de revistas sobre el tema "Image processing Digital techniques"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 1 de agosto de 2024

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1

Kumar Saini, Mukesh, Arun Saini y Sachin Gupta. "Digital Image Processing Techniques for Leukemia Detection". International Journal of Science and Research (IJSR) 13, n.º 6 (5 de junio de 2024): 1710–19. http://dx.doi.org/10.21275/sr24627090927.

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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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3

Raghavendra, V., N. Vinay kumar y Manish Kumar. "Latest advancement in image processing techniques". International Journal of Engineering & Technology 7, n.º 2.12 (3 de abril de 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.
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4

Sweeta, J. Anto Germin y Dr Sivagami B. "Contemporary Techniques in Digital Image Processing". International Journal of Computer Science and Engineering 6, n.º 11 (25 de noviembre de 2019): 3–46. http://dx.doi.org/10.14445/23488387/ijcse-v6i11p109.

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Pradhan, Manini Monalisa. "Elimination Noise from Image Using Machine Learning Techniques". Oct-Nov 2023, n.º 36 (20 de octubre de 2023): 27–36. http://dx.doi.org/10.55529/jipirs.36.27.36.

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The Image Processing system is mostly used because of their easy accessibility of powerful personal computers, bulk memory machines with graphics software and others visual application. Of “Image Processing” is applied in a number of applications. These include in area of Remote Sensing in GIS application, Medical Imaging Processing for patient care application, Forensic Studies, Textiles engineering and design, Material science, Military Research, Film industry application, and Document processing, Graphic arts. An image is defined as an array, or a matrix, square pixel arranged in rows and columns. Many image-processing procedures involve making the image as a two-dimensional signal and applying standard signal processing techniques to it. Image processing can be defined by means of a ‘digital image processing’’. The pitch of ‘digital image processing’ states to ‘processing digital’ images through channels of a computer. In this paper Image de-noising through K-SVD algorithm is presented by taking the RGB color with 256*256 sizes 24 bit standardize image.
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6

Mohanty, Sumant Sekhar y Sushreeta Tripathy. "Application of Different Filtering Techniques in Digital Image Processing". Journal of Physics: Conference Series 2062, n.º 1 (1 de noviembre de 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2062/1/012007.

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Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.
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Iqbal, Saima, Wilayat Khan, Abdulrahman Alothaim, Aamir Qamar, Adi Alhudhaif y Shtwai Alsubai. "Proving Reliability of Image Processing Techniques in Digital Forensics Applications". Security and Communication Networks 2022 (31 de marzo de 2022): 1–17. http://dx.doi.org/10.1155/2022/1322264.

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Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing techniques and hence the forensic results loose their legal significance. Furthermore, counter-forensics can impede or mislead the forensic analysis of the digital images. To ensure that any image transformation meet high standards of safety and reliability, more rigorous methods should be applied to image processing applications. To verify the reliability of these transformations, we propose to use formal methods based on theorem proving that can fulfil high standards of safety. To formally investigate binary image processing, in this paper, a reversible formal model of the binary images is defined in the Proof Assistant Coq. Multiple image transformation methods are formalized and their reliability properties are proved. To analyse real-life RGB images, a prototype translator is developed that reads RGB images and translate them to Coq definitions. As the formal definitions and proof scripts can be validated automatically by the computer, this raises the reliability and legal significance of the image forensic applications.
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8

Durai Anand Thangarajan y Sivasangari Rajeswaran. "Bacteria identification using digital image processing". International Journal of Science and Research Archive 12, n.º 2 (30 de julio de 2024): 818–20. http://dx.doi.org/10.30574/ijsra.2024.12.2.1268.

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The identification of bacteria is an important and unavoidable task in medical disciplines and nutritional hygiene. But in the field of microbiology, No direct method is available for determination of bacterial species. The common manual technique is the microscopic sample analysis combined with more than 20 biochemical tests to identify the bacterium. These tests are more time consuming processes and required the training person for conducting these tests. To overcome the above problems the digital image processing can be used. The primary objective of the proposed work is to use the digital image processing techniques to identify the bacteria from the microscopic images. In this work the image of bacterial species were captured using a digital camera attached with the transmission electron microscope. After capturing the image, the preprocessing, segmentation and morphological procedures of digital image processing techniques are used to identify the bacterial species.
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9

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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10

P., Himali, Hardik Modi, Manoj Pandya y M. B. Potdar. "Leukemia Detection using Digital Image Processing Techniques". International Journal of Applied Information Systems 10, n.º 1 (4 de noviembre de 2015): 43–51. http://dx.doi.org/10.5120/ijais2015451461.

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11

Kumar, Arvind y Ashutosh Sharma. "Multiresolution Transform Techniques in Digital Image Processing". International Journal of Computer Applications 123, n.º 12 (18 de agosto de 2015): 44–49. http://dx.doi.org/10.5120/ijca2015905530.

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12

McKenna, S. P. y W. R. McGillis. "Performance of digital image velocimetry processing techniques". Experiments in Fluids 32, n.º 1 (1 de enero de 2002): 106–15. http://dx.doi.org/10.1007/s003480200011.

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13

Döler, W., N. Steinhöfel y A. Jäger. "Digital image processing techniques for cephalometric analysis". Computers in Biology and Medicine 21, n.º 1-2 (enero de 1991): 23–33. http://dx.doi.org/10.1016/0010-4825(91)90032-5.

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14

Patel, Jagrti, Meghna Jain y Papiya Dutta. "Detection of Faults Using Digital Image Processing Technique". Asian Journal of Engineering and Applied Technology 2, n.º 1 (5 de mayo de 2013): 36–39. http://dx.doi.org/10.51983/ajeat-2013.2.1.644.

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This paper presents an approach to automatic detection of fabric defects using digital image processing. In Textile industry automatic fabric inspection is important to maintain the quality of fabric. Fabric defect detection is carried out manually with human visual inspection for a long time. This paper proposes an approach to recognize fabric defects in textile industry for minimizing production cost and time. Fabric analysis is performed on the basis of digital images of the fabric. The recognizer acquires digital fabric images by image acquisition device and converts that image into binary image by restoration and threshold techniques. This paper introduces a method which reduces the manual work. This image processing technique is done using MATLAB 7.10. This research thus implements a textile defect detector with system vision methodology in image processing.
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15

Tanwar, Himanshu y Gaurav Kumar. "Review Paper on Techniques of 2D to 3D Image Reconstruction". International Journal for Research in Applied Science and Engineering Technology 11, n.º 10 (31 de octubre de 2023): 370–74. http://dx.doi.org/10.22214/ijraset.2023.55996.

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Abstract: The review paper emphasize on reducing technologies for 2D and 3D imaging, as well as model conversion. Although the popularity of 3D hardware is growing rapidly in the present era, 3D content is still dominated by its 2D counterpart. There are two main categories of image processing now available in the market, namely analogue and digital image processing. To produce hard copies such as scanned pictures and printouts, with images being the most common output, the analogue IP technique is used. On the other hand, Digital IP is used to manipulate digital images using computers, the outputs are often information related to images, mainly being data on features, edging characteristics, or masks. Image processing techniques, including Machine Learning and Deep Learning, can get more powerful.
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Mishra, Harshita y Anuradha Misra. "Techniques for Image Segmentation: A Critical Review". International Journal of Research in Advent Technology 9, n.º 3 (10 de abril de 2021): 1–4. http://dx.doi.org/10.32622/ijrat.93202101.

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In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.
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17

T, Nayana y Dr T. D. Shashikala. "CLASSIFICATION OF ARECANUT USING DIGITAL IMAGE PROCESSING". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n.º 09 (1 de septiembre de 2023): 1–11. http://dx.doi.org/10.55041/ijsrem25828.

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In agricultural domain research, image processing and machine learning techniques play an important role. One of India’s major cash crops is arecanut. A significant challenge in the field of agriculture is the grouping of arecanut. Arecanut categorization using image processing is an emerging field of research that aims to automate the process of grouping arecanut based on its color and shape using digital images. This paper presents a classification of Arecanut using Convolutional Neural Networks. General Terms Image Processing, Classification Keywords Arecanut classification, Machine learning, Convolutional Neural Networks, Deep learning.
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18

Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration". Applied and Computational Engineering 17, n.º 1 (23 de octubre de 2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.

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Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.
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19

S, Agnes Shifani, Akshaya D, Kaviya M y Kiruthiga K. "A Comprehensive Survey on Crack detection of Bone using various techniques". Bulletin of Scientific Research 2, n.º 2 (19 de agosto de 2020): 1–7. http://dx.doi.org/10.34256/bsr2021.

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Digital image processing plays a key role in manipulation of image and extracting the maximum amount of data from image with help of various algorithm. Digital image correlation algorithm determines the displacement and deformation of pattern across several images. Creating innovation are developing every day in various fields, particularly in restoration condition. Notwithstanding, still some old strategies are very famous. X-ray or CT images are one among the system for identification of bone cracks. during this article, we offer a comprehensive overview of various algorithm and techniques of displacement measurement generally and crack detection especially using digital image processing. we've been successful in highlighting each and each key feature and aspect of crack detection in bone which can take the add this domain further
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20

Yahya, Ali Abdullah. "Teaching Digital Image Processing Topics via Matlab Techniques". International Journal of Information and Education Technology 9, n.º 10 (2019): 729–34. http://dx.doi.org/10.18178/ijiet.2019.9.10.1294.

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21

Wen, Che-Yen y Chiu-Chung Yu. "Fingerprint Pattern Restoration by Digital Image Processing Techniques". Journal of Forensic Sciences 48, n.º 5 (1 de septiembre de 2003): 2002385. http://dx.doi.org/10.1520/jfs2002385.

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22

Vorozhtsov, E. V. "On shock localization by digital image processing techniques". Computers & Fluids 15, n.º 1 (enero de 1987): 13–45. http://dx.doi.org/10.1016/0045-7930(87)90003-x.

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23

Liao, Qinzhuo, Shaohua You, Maolei Cui, Xiaoxi Guo, Murtada Saleh Aljawad y Shirish Patil. "Digital Core Permeability Computation by Image Processing Techniques". Water 15, n.º 11 (24 de mayo de 2023): 1995. http://dx.doi.org/10.3390/w15111995.

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Calculation of REV (representative elementary volume) properties of geological porous media refers to the process of creating a 3D digital representation of a rock sample, typically obtained from imaging techniques such as X-ray microtomography. This technique allows for a detailed analysis of the internal structure and the properties of rocks, as well as precise calculation of various flow parameters. However, one major challenge with calculation of REV properties of geological porous media is the high computational cost required to generate accurate results, especially for large and complex samples. In this study, we constructed 3D digital cores of dune sand and fractured shale using CT scanning technology, and then used two image processing techniques, namely digital core image resampling and cutting, to reduce the computational cost of calculating digital core permeability. Next, a fast permeability calculation method is employed to reduce the complexity of permeability calculation. Finally, we summarized the applicability of different image processing methods to different rock samples, and provided prerequisites for high computational cost digital core permeability calculation.
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24

Kaur, Amanpreet. "Colour Image Segmentation using Background Subtraction with Global and Local Threshold". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (20 de junio de 2021): 1652–57. http://dx.doi.org/10.22214/ijraset.2021.35340.

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Image segmentation is one of the fundamental and essential steps in all the major applications of digital image processing. In this process the digital image is divided into various regions which are also known as segments. These segmented parts of the digital image could be used for further processing like detection of types of objects present in the segmented region, various tumors present in the digital images or the scene understanding process. Usually segmentation is classified as local segmentation and the global segmentation. Image segmentation is also classified on the basis of digital image properties also. In this case it is of two types. First one is non continuity detection and second one is the continuous detection. Various image segmentation techniques are proposed by researchers which have various limitations. Some techniques do not split the region uniformly and other techniques take enough time and memory for the processing of digital image. In this research work both the local and global thresholding concept is used to get the segmented regions of the image. The proposed technique will be able to extract the segmented objects from the digital image. To check the authenticity and efficiency of the proposed technique, it will be compared with other well known techniques of image segmentation using background subtraction of colored digital images. Time of computation, sensitivity and accuracy are used as objective parameters for the performance evaluation of the techniques. For the subjective evaluation visual quality of the digital image is used for performance evaluation.
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25

Ahmed, Awa y Osman Sharif. "Image Processing Techniques-based fire detection". Sulaimani Journal for Engineering Sciences 8, n.º 1 (1 de agosto de 2021): 23–34. http://dx.doi.org/10.17656/sjes.10145.

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In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flame edges in digital images with a timely manner, which has a huge impact on saving life and reducing loss of life.
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Tang, Jinyu. "An Optimized Digital Image Processing Algorithm for Digital Oil Painting". Mobile Information Systems 2022 (31 de mayo de 2022): 1–10. http://dx.doi.org/10.1155/2022/4956839.

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Human civilization’s accomplishments have grown with the passage of time and the advancement of society. With the fast growth of computer networks and information technology, the conventional method of information transmission based on words cannot fulfill the demands of people in the current era. As a result, in this age of extensive information and image processing techniques, images as a means of information sharing are becoming increasingly popular. As we know, digital image processing knowledge has a far-reaching impact in the field of artistic creation, among which the creation of oil painting is facing severe challenges. Aiming at the problem that the effect of digital oil painting is not ideal, this paper aims to study digital oil painting by using digital image processing technology. This paper first uses the image edge recognition based on the improved Canny algorithm to detect the edge of the oil painting image, then uses the nonlinear image enhancement algorithm to enhance the effect of the oil painting image, then uses the improved genetic algorithm to segment the image, and finally enlarges the oil painting image to calibrate the color of the oil painting image. Experiments reveal that the proposed approach outperforms existing algorithms in terms of edge detection data integrity, high-quality coefficient index of image enhancement, picture segmentation running time, and the ability to successfully increase the visual effect of oil painting.
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Lee, Jong Jae, Masanobu Shinozuka y Soo Jin Cho. "Remote Sensing of Bridge Displacement Using Digital Image Processing Techniques". Key Engineering Materials 321-323 (octubre de 2006): 404–9. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.404.

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In this study, an optical method of real-time displacement measurement of such bridges was carried out by means of digital image processing techniques. A commercially available digital video camera combined with a telescopic device takes a motion picture of the target panel with known geometry, which is installed on the measurement location of a bridge. The displacement of the target is calculated based on the captured images in real-time manner using image processing techniques, which require a texture recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and number of pixels moved. For the purpose of verification of the presented method, a laboratory test was made using shaking table test and the measured displacement by image processing techniques was compared with the data from a contact-type sensor, a linear variable differential transformer (LVDT). The proposed method gave close results to a conventional sensor. Field tests were carried out on a bridge with steel plate girders and a bridge with steel box girders. The test results gave sufficient dynamic resolution in frequency as well as the amplitude.
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Wen, Cathlyn Y. y Robert J. Beaton. "Subjective Image Quality Evaluation of Image Compression Techniques". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, n.º 23 (octubre de 1996): 1188–92. http://dx.doi.org/10.1177/154193129604002309.

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Image compression reduces the amount of data in digital images and, therefore, allows efficient storage, processing, and transmission of pictorial information. However, compression algorithms can degrade image quality by introducing artifacts, which may be unacceptable for users' tasks. This work examined the subjective effects of JPEG and wavelet compression algorithms on a series of medical images. Six digitized chest images were processed by each algorithm at various compression levels. Twelve radiologists rated the perceived image quality of the compressed images relative to the corresponding uncompressed images, as well as rated the acceptability of the compressed images for diagnostic purposes. The results indicate that subjective image quality and acceptability decreased with increasing compression levels; however, all images remained acceptable for diagnostic purposes. At high compression ratios, JPEG compressed images were judged less acceptable for diagnostic purposes than the wavelet compressed images. These results contribute to emerging system design guidelines for digital imaging workstations.
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Kavitha S Patil,. "Digital Image and Video Processing: Algorithms and Applications". Journal of Electrical Systems 20, n.º 3s (4 de abril de 2024): 1390–96. http://dx.doi.org/10.52783/jes.1516.

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Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings. When it comes to image and video processing, the input is an image of poor quality, and the output is an image and video of higher quality. Research on algorithms and applications of digital image and video processing is the primary purpose of this study, which aims to investigate these topics extensively. The methodology employed in this study is qualitative research technique. In accordance with the findings of this research, "Image Processing" refers to the process of analyzing images with the objective of determining the significance of objects and identifying them. Image analysts analyze data that has been remotely sensed and attempt to detect, identify, classify, measure, and evaluate the significance of physical and cultural objects, as well as their patterns and spatial relationships. One subcategory of signal processing is known as video processing, and it is distinguished by the fact that the signals that are input and output are video files or video streams. Technology such as television sets, videocassette recorders (VCRs), DVD players, and other devices all make use of video processing algorithms. The processing of images and videos is extremely useful in a variety of contexts.
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Alayat, Abdulsalam Basher y Hend Ali Omar. "Pavement Surface Distress Detection Using Digital Image Processing Techniques". Jurnal Kejuruteraan 35, n.º 1 (30 de enero de 2023): 247–56. http://dx.doi.org/10.17576/jkukm-2023-35(1)-24.

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Road safety and pavement condition are considered top priorities in our civilized societies, and it’s important that the pavement condition remains in an excellent state for a long time. However, eventually, the pavement will get exposed to different types of distresses as a result of traffic loads, rough environment conditions, soil conditions, and underline subgrade. Therefore, to achieve the required standards for the pavement surface roads in our country and provide the best performance: detection and measurements of distresses extension must be included in maintenance preparation. This paper proposes a technique for crack detection based on digital image processing using a programming language called Matrix Laboratory known as MATLAB. The main target is to estimate the pavement’s length, width, and area by capturing the image using a digital camera with the required precautions and image implementation. Secondly, developing an image pre-processing operation to eliminate environmental interference as much as possible and subsequently use the image thresholding method to separate the pixels within the image into two groups to find the thresholding value for image binarization. The method successfully detects and removes the presence of unwanted objects in an image, even in difficult situations where surfaces are less visible. Verification showed good results with an excellent processing time, which can be considered an indicator of pavement crack parameters.
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K Ganapathi Babu, Sunil Kumar Dasari y C Yosepu. "An Overview: Image Processing Techniques and Its Applications". international journal of engineering technology and management sciences 7, n.º 3 (2023): 883–88. http://dx.doi.org/10.46647/ijetms.2023.v07i03.135.

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The use of digital image processing techniques has been widely flourished and they are now used for all kinds of tasks in various areas. Image processing helps in the identification of objects that are invisible, and different techniques makes it faster and cost effective. This paper gives you an overview on various digital image processing techniques and its applications like remote sensing, medical imaging, forensic studies etc.
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Peters, Klaus-Ruediger y Eisaku Oho. "New digital image processing technology for FSEM microscopy". Proceedings, annual meeting, Electron Microscopy Society of America 51 (1 de agosto de 1993): 212–13. http://dx.doi.org/10.1017/s0424820100146904.

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Digital image acquisition and processing can provide many advantages over conventional analog image information handling, i.e., undisturbed access to the “raw data set”, quantitative analysis of the image information, and reduced costs and increased flexibility of image data handling. However, it may principally change microscopy by providing a new facility for instant exhaustive data presentation in acquired images. Detail imaging is one of the basic microscopic tasks but visual access to detail information is cumbersome and often left to post-session data analysis. A dedicated software/hardware technique is now available for automatic “near-real-time” enhancement of image detail information visually not accessible in the “raw data” image. Pertinent image details include spatial dimensions of only a few pixels in size (spatial details) and intensity variations of only a few intensity steps in height (intensity details). While conventional image enhancement techniques often produce serious image artifacts which exclude a closer inspection of enhanced detail information, the new pixel-accurate processing (PAP) technology allows instant image evaluation at an accuracy-level of the raw data through detail enhancement in full-frame images, digital zoom and noise smoothing.
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33

Hussin, R., M. Rizon Juhari, Ng Wei Kang, R. C. Ismail y A. Kamarudin. "Digital Image Processing Techniques for Object Detection From Complex Background Image". Procedia Engineering 41 (2012): 340–44. http://dx.doi.org/10.1016/j.proeng.2012.07.182.

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34

Moon, Ki Hoon, Augusto Cannone Falchetto y Jin Hoon Jeong. "Microstructural analysis of asphalt mixtures using digital image processing techniques". Canadian Journal of Civil Engineering 41, n.º 1 (enero de 2014): 74–86. http://dx.doi.org/10.1139/cjce-2013-0250.

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In this paper, the internal microstructure of asphalt mixture is analyzed through digital image processing (DIP) of two-dimensional asphalt mixture images. A set of 12 mixtures prepared with two binders, two air voids percentages, and different recycled asphalt pavement (RAP) contents is used. First, small asphalt mixture beams of the same size of bending beam rheometer specimens are prepared for the images acquisition. Then, based on mixture volumetric properties, a three-phase material model is obtained. Finally, 2- and 3-point correlation functions of the material phases are numerically evaluated. No significant differences were observed in the microstructure and spatial distributions of aggregates, asphalt mastic, and air voids for asphalt mixtures containing up to 40% of RAP. However, an increase in auto correlation length (ACL) was found for RAP mixtures in comparison with the conventional mixtures.
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35

Cai, Han Ming, Pei Yao Wang y Xiao Mei Song. "Detecting Thread Features Based on Digital Image Processing Techniques". Advanced Materials Research 889-890 (febrero de 2014): 1107–10. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1107.

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Thread features of the traditional measuring method mainly adopts working gauge measurement, due to limitations in the traditional thread features measurement accuracy is relatively low, the efficiency is low, the cost is high. The thread features detection method based on digital image processing techniques using CCD to obtain basic image of thread, processing the thread image, extracting thread outline, calculating thread features through the computer, improves the efficiency, saves the cost.
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36

Devane, Vighnesh, Ganesh Sahane, Hritish Khairmode y Gaurav Datkhile. "Lane Detection Techniques using Image Processing". ITM Web of Conferences 40 (2021): 03011. http://dx.doi.org/10.1051/itmconf/20214003011.

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Lane detection is a developing technology that is implemented in vehicles to enable autonomous navigation. Most lane detection systems are designed for roads with proper structure relying on the existence of markings. The main shortcoming of these approaches is that they might give inaccurate results or not work at all in situations involving unclear markings or the absence of them. In this study one such approach for detecting lanes on an unmarked road is reviewed followed by an improved approach. Both the approaches are based on digital image processing techniques and purely work on vision or camera data. The main aim is to obtain a real time curve value to assist the driver/autonomous vehicle for taking required turns and not go off the road.
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37

Mahankali, Sreenath y Giridhar Valikala. "Comparison of Compressive Strength of M30 Grade Concrete with Destructive and Nondestructive Procedures Using Digital Image Processing as a Technique". Advances in Civil Engineering 2022 (19 de junio de 2022): 1–12. http://dx.doi.org/10.1155/2022/4649660.

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Destructive, semidestructive, and nondestructive methods are used to assess the compressive strength of concrete and its substantial mechanical property. In the destructive method, samples of concrete are crushed and treated under compression to determine its compressive strength. As such, the impact is seen on test results like the method of casting and compaction. The tests on concrete become limited in the destructive method and are confined to predict compressive strength, flexural strength, etc. To overcome its limitations and to study concrete matrix, semidestructive and nondestructive test methods came into limelight. Among nondestructive methods, strength prediction can be carried out using Schmidt’s rebound hammer test, ultrasonic pulse velocity test, image analysis techniques, radioactive tests, etc. Consequently, an advanced technique to predict the strength of the structural element using digital image processing technique has been introduced, and one can have a glimpse of the enlarged image, which quantifies and is used to assess the strength. The various characteristic features associated with the image help to calculate the strength of the structural element. A high-pixel camera is used to take images of concrete cube samples, and they are analyzed with digital image processing techniques and a tool in MATLab or directly by making use of ImageJ software. In addition, digital image processing techniques are being implemented in various fields such as medical, industrial, remote sensing, and engineering. The present paper proposes to cast 150 × 150 × 150 mm-sized M30 grade concrete cube samples and to study their strength after a period of 7 days and subsequently after 28 days. Destructive and nondestructive methods are used, and the samples are analyzed with digital image processing techniques using ImageJ software. The observed findings are discussed in the paper.
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38

SANCHEZ, ANGEL, JOSE F. VELEZ, ANA BELEN MORENO y JOSE L. ESTEBAN. "INTRODUCING ALGORITHM DESIGN TECHNIQUES IN UNDERGRADUATE DIGITAL IMAGE PROCESSING COURSES". International Journal of Pattern Recognition and Artificial Intelligence 15, n.º 05 (agosto de 2001): 789–803. http://dx.doi.org/10.1142/s0218001401001155.

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This paper documents the development and first offering of an undergraduate course in Digital Image Processing at the Rey Juan Carlos University, Madrid (Spain). The paper describes how the appropriate introduction of main Algorithm Design Techniques can successfully assist the students to achieve a comprehensive understanding of image operations and related algorithms. Image processing problems offer a natural way to present real world problems where the students can use their algorithmic knowledge. Furthermore, image processing solutions are needed from a methodological development and require efficient well-designed algorithms. This paper presents an effort in the integration of Algorithm Design Techniques in a Digital Image Processing course with a very practical scope.
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39

Patil, Rajesh y Surendra Bhosale. "Medical Image Denoising Techniques: A Review". International Journal on Engineering, Science and Technology 4, n.º 1 (17 de enero de 2022): 21–33. http://dx.doi.org/10.46328/ijonest.76.

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Medical imaging means the methods and procedures used for creating pictures of various parts of the human body for numerous clinical objectives. These images are constantly gets dirtied by noise during picture acquisition and transmission, resulting in low quality images. Noise is the unwanted signal which corrupts the important and desirable information. The noises can be categorized into different types based on their nature and origin. e.g. Gaussian, the impulsive and speckle noise etc. The removal of noise is very necessary for proper analysis and diagnosis. Filtering noise helps to recreate a high-quality image in digital image processing for further image processing such as segmentation of images, identification, recognition and monitoring, etc. There are various approaches to denoise medical images based on transform approach, machine learning, filtering method and statistical method. These techniques or approaches is subject to noise type exist in the image. To evaluate the denoising performance, parameters like SNR, PSNR etc. are used. This paper takes a review of current denoising techniques.
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40

Rodrigues, Pedro, Manuel João Ferreira y João Luís Monteiro. "Quantum Computation Perspectives in Medical Image Processing". International Journal of Nanotechnology and Molecular Computation 2, n.º 2 (abril de 2010): 16–46. http://dx.doi.org/10.4018/978-1-61520-670-4.ch006.

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The need to increase the complexity of computational methods to produce improvements in functional performance, particularly in medical image processing applications, leads to find suitable physical devices. This chapter describes two ways of adapting the techniques of image processing to quantum devices. This kind of computing can achieve, for some problems, unparalleled performance as compared to classic computing. In the first method, using the quantum Grover’s algorithm how to implement image processing techniques under quantum rules is shown. In the second method, using diffraction and interference, the possibility of using less complex quantum devices for processing digital images is treated. Using leucocytes images, that mode is tested.
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41

FUJIWARA, Tadao, Shuji NISHIHARA y Koji HIROSE. "Digital image processing techniques for color flow-visualization photograph." JOURNAL OF THE FLOW VISUALIZATION SOCIETY OF JAPAN 8, n.º 28 (1988): 32–38. http://dx.doi.org/10.3154/jvs1981.8.32.

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42

Prasad, Jayashree Rajesh. "Review of Advanced Image Processing Techniques: Digital Elevation Model". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 2733–37. http://dx.doi.org/10.22214/ijraset.2022.44422.

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Abstract: This paper gives an in depth insight into the current as well as the previous systems or techniques that have been used in order to perform advanced image processing. These systems or techniques have been implemented or tested in the areas of Germany, The United States of America, India, China, Indonesia and many more. The review aims to explore challenges and opportunities in the field computational and image processing areas of the industry.
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43

FUJIWARA, Tadao, Shuji NISHIHARA y Koji HIROSE. "Color Flow-Visualization Photography and Digital Image Processing Techniques". JSME international journal. Ser. 2, Fluids engineering, heat transfer, power, combustion, thermophysical properties 31, n.º 1 (1988): 39–46. http://dx.doi.org/10.1299/jsmeb1988.31.1_39.

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44

Ozkaya, Yasar A. "Digital image processing and illumination techniques for yarn characterization". Journal of Electronic Imaging 14, n.º 2 (1 de abril de 2005): 023001. http://dx.doi.org/10.1117/1.1902743.

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45

FUJIWARA, Tadao, Shuji NISHIHARA y Koji HIROSE. "Color flow-visualization photography and digital image processing techniques." Transactions of the Japan Society of Mechanical Engineers Series B 53, n.º 493 (1987): 2762–70. http://dx.doi.org/10.1299/kikaib.53.2762.

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46

Bennamoun, M. y A. Bodnarova. "Digital Image Processing Techniques for Automatic Textile Quality Control". Systems Analysis Modelling Simulation 43, n.º 11 (noviembre de 2003): 1581–614. http://dx.doi.org/10.1080/0232929032000115083.

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47

Kaur, Rajvinder y Rupinder Kaur. "A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing". International Journal of Computer Applications 179, n.º 45 (18 de mayo de 2018): 24–27. http://dx.doi.org/10.5120/ijca2018917133.

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48

Gill, Jasmeen, Akshay Girdhar y Tejwant Singh. "A Review of Enhancement and Segmentation Techniques for Digital Images". International Journal of Image and Graphics 19, n.º 03 (julio de 2019): 1950013. http://dx.doi.org/10.1142/s021946781950013x.

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Image enhancement and segmentation are the two imperative steps while processing digital images. The goal of enhancement is to improve the quality of images so as to nullify the effect of poor illumination conditions during image acquisition. Afterwards, segmentation is performed to extract region of interest (ROI) from the background details of the image. There is a vast literature available for both the techniques. Therefore, this paper is intended to summarize the basic as well as advanced enhancement and segmentation techniques under a single heading; to provide an insight for future researches in the field of pattern recognition.
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49

Putra, Edi y Lina Lina. "KAJIAN TENTANG PERBAIKAN KUALITAS CITRA DALAM AIR MENGGUNAKAN METODE DARK CHANNEL PRIOR". Jurnal Ilmu Komputer dan Sistem Informasi 5, n.º 1 (25 de agosto de 2017): 13. http://dx.doi.org/10.24912/jiksi.v5i1.765.

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Digital image processing is a branch of engineering informatics. image processing is an attempt to change your photo or image into another by using certain techniques. The process of changing the image data into digital data by using a computer known as digital image processing. The purpose of this application is to improve the image quality of the water using test images and imagery training, to train image by image is in the water, while the test images by using the image processing Dark Channel Prior. The image that was in the sea water murky and foggy be processed and then do a comparison with the image that is not in the water. The design is made in the form of software applications to improve the quality of the image using the Dark Channel Prior. In this study, created by using Visual Basic 2013.
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50

Saha, Tanusree y Dr Kumar Vishal. "A Study of Application of Digital Image Processing in Medical Field and Medical Image Segmentation by Edge Detection". International Journal of Emerging Science and Engineering 12, n.º 4 (30 de mayo de 2024): 3–8. http://dx.doi.org/10.35940/ijese.g9890.12040324.

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There has been a lot of recent development in the domain of image processing and associated imaging techniques. Medical imaging is about the practice to capture images of different fragments of the body for diagnostic or investigative purposes. The number of imaging procedures performed each week is in the millions. This study presents the promising image processing methods currently in use in the medical field. The rapid expansion of medical imaging can be attributed to advancements in image processing methods, including image recognition, enhancement, and analysis. Utilised in the diagnosis and treatment of patients, image processing techniques have proven to be of immense benefit to surgeons and physicians. The explosion of clinical medical devices is largely attributable to the blend of hardware and image processing techniques, both of which have contributed significantly to medical progress. In recent years, Image segmentation via edge detection plays a vital role by extracting important features of the images like corners, lines and curves to recognized object boundaries which in turns helps the medical expertise to detect diseases in medical analysis and patient care.
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