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Journal articles on the topic 'Image management'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

P.B REDDY, P. B. REDDY, M. VENKATA M VENKATA, and Dr MORUSU SIVA SANKAR Dr MORUSU SIVA SANKAR. "Advantage Of Image Management." International Journal of Scientific Research 3, no. 1 (June 1, 2012): 71–73. http://dx.doi.org/10.15373/22778179/jan2014/25.

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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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Ratib, O. "Imaging Informatics: from Image Management to Image Navigation." Yearbook of Medical Informatics 18, no. 01 (August 2009): 167–72. http://dx.doi.org/10.1055/s-0038-1638658.

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Summary Objective To review the rapid evolution of imaging informatics dealing with issues of management and communication of digital images starting from the era of simple storage and transfer of images to today’s world of interactive navigation in large sets of multidimensional data. Methods This paper will review the initial concepts of Picture Archiving and Communication Systems (PACS) and the early developments and standardization efforts that lead to the deployment of large intra-institutional networks of image distribution allowing radiologists and physicians to access and review images digitally. With the deployment of PACS came along the need for advanced tools for image visualization and image analysis. Results Review of the history of PACS and Imaging Informatics clearly shows that the early developments were focused on the radiologist’s requirements for diagnosis and image interpretation. These early developments lagged behind the rapid adoption of digital imaging in areas outside radiology. Only recently, imaging informatics shifted toward the development of new tools geared toward the needs of other users such as surgeons, referring physicians and care-providers, and even for the patients themselves. Also in the recent years, the development of multimedia and communication tools in the consumer market has influenced the design and strategic development of image management platforms inside and outside healthcare institutions. Conclusions The focus of imaging informatics has clearly shifted in the last decade from basic infrastructure design to complete data and image navigation systems. While the challenge of storing and managing large volumes of imaging data have slowly vanished with the rapid development if information technology, the new challenge emerged from the new requirements of image manipulation and analysis in clinical practice.

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Grycuk, Rafał, Patryk Najgebauer, Miroslaw Kordos, Magdalena M. Scherer, and Alina Marchlewska. "Fast Image Index for Database Management Engines." Journal of Artificial Intelligence and Soft Computing Research 10, no. 2 (April 1, 2020): 113–23. http://dx.doi.org/10.2478/jaiscr-2020-0008.

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AbstractLarge-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.

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Narayanan, S. Lakshmi, Dr K. Sankaranarayanan, and Dr V. Vijayakumari. "Image Quality Aware Secured Image Sharing Method With Guaranteed Digital Rights Management." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1136–45. http://dx.doi.org/10.5373/jardcs/v11sp10/20192920.

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Armstrong, Peter. "Management, image and management accounting." Critical Perspectives on Accounting 13, no. 3 (June 2002): 281–95. http://dx.doi.org/10.1006/cpac.2001.0508.

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CHUBB, CHARLIE, YOSHI INAGAKI, C. COTMAN, B. CUMMINGS, and P. C. Y. SHEU. "SEMANTIC BIOLOGICAL IMAGE MANAGEMENT AND ANALYSIS." International Journal on Artificial Intelligence Tools 13, no. 04 (December 2004): 881–96. http://dx.doi.org/10.1142/s0218213004001879.

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Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research. It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper discusses the feasibility of developing a set of imaging algorithms that allows the user to train an imaging system/database to recognize biological objects of various sorts based on their own criteria. The image software builds a model of the selected objects by reiterative training, evolving the ability (i.e., the underlying rules) to recognize these objects. These objects are in turn archived into a growing database that builds upon the experience of multiple individuals that can be referred to as an object zoo. The user can search these zoos using newly acquired images based on similarly using more narrow or broadened criteria based on a new, semantic database framework called SemanticObjects.

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FLIEGER, Marcin. "IMAGE CRISIS MANAGEMENT." Journal of Science of the Gen. Tadeusz Kosciuszko Military Academy of Land Forces 170, no. 4 (December 2, 2013): 101–13. http://dx.doi.org/10.5604/17318157.1115176.

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BOKOVETS, Viktoria, and Roman PYSMENNYI. "COMPANY IMAGE MANAGEMENT." Herald of Khmelnytskyi National University. Economic sciences 304, no. 2(1) (March 18, 2022): 128–30. http://dx.doi.org/10.31891/2307-5740-2022-304-2(1)-17.

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The article deals with the essence of the image, its types and impact on competitiveness. Factors influencing the external and internal image of the enterprise are highlighted. Identified the main mistakes managers in image management. Have been formed a number of approaches to enterprise image management. Analysis of the essence and content of these approaches to the formation and management of the image of the company makes it possible to improve the composition of the main factors in the formation of the image of the company and specific methods of managing it at specific stages of the production cycle. Using any approach requires prior diagnosis of current state of the image and the formation on this basis the optimal set of management actions taking into account the characteristics of the company and the environment in which it functions. The enterprise, taking into account consumers and the opinion of society and seeks to form not only a positive image, but also an image that will increase the competitiveness of the market and contribute for increasing its efficiency.

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Sempeles, Susan. "Cardiology Image Management." Journal of Clinical Engineering 32, no. 3 (July 2007): 86–87. http://dx.doi.org/10.1097/01.jce.0000282629.23668.e8.

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James, A. E., F. Carroll, D. R. Pickens, J. C. Chapman, R. R. Robinson, H. P. Pendergrass, and R. Zaner. "Medical image management." Radiology 160, no. 3 (September 1986): 847–51. http://dx.doi.org/10.1148/radiology.160.3.3737929.

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Murad, Faris M., Subhas Banerjee, Bradley A. Barth, Yasser M. Bhat, Shailendra S. Chauhan, Klaus T. Gottlieb, Vani Konda, et al. "Image management systems." Gastrointestinal Endoscopy 79, no. 1 (January 2014): 15–22. http://dx.doi.org/10.1016/j.gie.2013.07.048.

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Wang, Wenting, and Younghwan Pan. "Visual Information Management Based on Visual Communication of Deep Learning." Scientific Programming 2022 (January 21, 2022): 1–5. http://dx.doi.org/10.1155/2022/8356414.

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To speed up the buffering of visualization images and ensure the integrity of visualization images, this paper proposes the construction of a virtual packaging model of visualization images based on visual communication of deep learning (DL). Based on the visual communication, the specific calibration steps of the visual image are designed to complete the adjustment of the visual image by using the calibration process of the visual image. We introduce the minimum buffer coefficient of the visual image, calculate the size of the buffer liner of the visual image, correct the thickness of the visual image, and complete the design of the buffer package of the visual image. Finally, the process of constructing the virtual packaging model of the visualized image is constructed to realize the virtual packaging of the visualized image. The simulation results show that the visual image virtual packaging model based on visual communication improves the buffering speed of visual image by 66.6% compared with the visual image virtual packaging model based on machine vision.

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Sawase, Kazuhito, and Hajime Nobuhara. "Management System for Tagged Image Databases Using Lattice Structures." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 2 (March 20, 2010): 155–59. http://dx.doi.org/10.20965/jaciii.2010.p0155.

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Tagged Image Management System based on Lattice Structures (TaILaS), the next-generation management we propose for a large number of images, constructs a concept lattice from image tags using formal concept analysis, then visualizes the Hasse diagram of this lattice for any numbers of images. TaILaS enables users to avoid having to classify images which are difficult to divide into folders. TaILaS runs on a conventional computer (CPU = 2.13 GHz,MM= 2 GB) and is implemented in Processing as a Java-based programming language. Experiments involving 11 subjects using 100 images selected from the Corel Image Gallery confirmed that TaILaS manages huge image databases effectively.

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LEMLEY, J. K. "IMAGE VERSUS REALITY - CHANNEL TUNNEL IMAGE MANAGEMENT." Proceedings of the Institution of Civil Engineers - Civil Engineering 114, no. 1 (February 1996): 12–17. http://dx.doi.org/10.1680/icien.1996.28119.

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LEMLEY, J. K. "IMAGE VERSUS REALITY—CHANNEL TUNNEL IMAGE MANAGEMENT." Proceedings of the Institution of Civil Engineers - Civil Engineering 120, no. 2 (May 1997): 82–83. http://dx.doi.org/10.1680/icien.1997.29331.

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Chemock, Richard S. "Application of an image management system in microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 1992): 1052–53. http://dx.doi.org/10.1017/s0424820100129899.

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One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

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A. Karthika, R., K. Dhinakaran, D. Poorvaja, and A. V. Shanbaga Priya. "Cloud Based Medical Image Data Analytics in Healthcare Management." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 135. http://dx.doi.org/10.14419/ijet.v7i3.27.17744.

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In today’s world, the images form a huge amount of unstructured data from the public and the corporate sector. As a result of the growth of these types of data, modern analytical systems need to interpret and assimilate images. This brings in the need of image processing which involves the transformation from images to analytically organized and structured data. It performs required operation on the given input image and returns the related outputs based on the query. Digital image processing has pushed the envelope for the appraisal in various domains such as healthcare, defense and security, remote sensing, robotic visions, pattern recognitions and satellites. The complication involved in the healthcare domain makes it suitable to explore and induce the concept of image processing and increases the potential for prescriptive analytics. The cloud combined with the Image Processing provides the best environment for analyzing these images using the proposed technique. The proposed work involves processing the input images using the cloud data analytics that provides a user-friendly environment and retrieves the relevant images as well as text for the given user query which produces the outputs that are more efficient in terms of parameters like time, size, security and speed comparatively with the existing data mining process.

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Yang, Judy X., Lily D. Li, and Mohammad G. Rasul. "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 130–36. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1025.

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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Bondestam, S., A. E. Lamminen, and H. J. Aronen. "Efficient Memory Management Strategy in Hypermedia with Interactive MR Images." Acta Radiologica 36, no. 4-6 (July 1995): 556–60. http://dx.doi.org/10.1177/028418519503600447.

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When composing hypermedia with interactive image sets the main problem is to allocate minimal memory of random access memory (RAM). The stand-alone product should be accessible to the public and not require too much memory to maintain interaction between the images and the text. We designed a new hypermedia application using SuperCard and image stacks in PICS format running on a Macintosh LC computer. Memory use was effective since the images were kept in external files outside the application. On browsing cards the images were requested from the image file and each image was displayed within about 1 second as a floating frame on top of the card in the top window. Interactivity when presenting, for example, anatomical parts was achieved by bitmap objects which were activated through the image by pointing at them through the image. By pointing at a text object the corresponding anatomical bitmap object was visualised through the image. The stand-alone version of the application with up to 100 cards runs on a 2 Mb RAM set-up. The maximum sizes of the external image stacks are not dependent on the RAM size.

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Rui, Jian Raymond, and Michael A. Stefanone. "STRATEGIC IMAGE MANAGEMENT ONLINE." Information, Communication & Society 16, no. 8 (October 2013): 1286–305. http://dx.doi.org/10.1080/1369118x.2013.763834.

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D'Attoma, N., P. L. Pavan, M. Bertocco, and P. Dompieri. "Management of image information." Urologia Journal 61, no. 1 (February 1994): 51–54. http://dx.doi.org/10.1177/039156039406100111.

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Medical imaging has become a major investigation tool during the last few years, due to availability of several digitalised imaging modalities (Computed Tomography, Magnetic Resonance, etc.). Interest has grown in medical image management by appropriate information systems. These processing tools defined PACS - systems of acquisition, storage, transmission and communication - must be integrated on the one hand with the RIS - radiological information system (patient, appointment procedure) - and on the other hand with the HIS - hospital information system - to offer sufficient advantages to justify their installation.

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Varga, Margaret, Paul Ducksbury, and Peter Somol. "Medical image management system." International Journal of Healthcare Technology and Management 7, no. 5 (2006): 387. http://dx.doi.org/10.1504/ijhtm.2006.008427.

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Lema, Mark J. "Image Guided Pain Management." Anesthesiology 88, no. 2 (February 1, 1998): 556. http://dx.doi.org/10.1097/00000542-199802000-00051.

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Mostafa, Salwa A. K., Naser El-sheimy, A. S. Tolba, F. M. Abdelkader, and Hisham M. Elhindy. "Wavelet Packets-Based Blind Watermarking for Medical Image Management." Open Biomedical Engineering Journal 4, no. 1 (April 16, 2010): 93–98. http://dx.doi.org/10.2174/1874120701004010093.

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The last decade has witnessed an explosive use of medical images and Electronics Patient Record (EPR) in the healthcare sector for facilitating the sharing of patient information and exchange between networked hospitals and healthcare centers. To guarantee the security, authenticity and management of medical images and information through storage and distribution, the watermarking techniques are growing to protect the medical healthcare information. This paper presents a technique for embedding the EPR information in the medical image to save storage space and transmission overheads and to guarantee security of the shared data. In this paper a new method for protecting the patient information in which the information is embedded as a watermark in the discrete wavelet packet transform (DWPT) of the medical image using the hospital logo as a reference image. The patient information is coded by an error correcting code (ECC), BCH code, to enhance the robustness of the proposed method. The scheme is blind so that the EPR can be extracted from the medical image without the need of the original image. Therefore, this proposed technique is useful in telemedicine applications. Performance of the proposed method was tested using four modalities of medical images; MRA, MRI, Radiological, and CT. Experimental results showed no visible difference between the watermarked and the original image. Moreover, the proposed watermarking method is robust against a wide range of attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, contrast adjustment, and sharpen filter and rotation.

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Zikra, Muhammad, Noriaki Hashimoto, Masaru Yamashiro, Masaki Yokota, and Kojiro Suzuki. "APPLICATION OF VIDEO IMAGES FOR MONITORING COASTAL ZONE IN HASAKI BEACH, JAPAN." Coastal Engineering Proceedings 1, no. 33 (October 18, 2012): 43. http://dx.doi.org/10.9753/icce.v33.management.43.

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The video image analysis methods are used for monitoring the phenomena at the coastal zone. A digital video camera which is mounted in the HORS research building is used to estimate directional wave spectra and bathymetry in shallow water areas. The Bayesian Directional Method is used to estimate directional wave spectra using the group of pixels brightness and non-linear bathymetry inversion is used to estimate shallow water bathymetry using time series of pixel brightness intensity at cross-shore and longshore array. The results are compared with a numerical model and field measurements. The results indicate that video image data could be used to estimate bathymetry and directional wave spectra in shallow water areas.

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Bouchekara, Houssem R. E. H., Bashir O. Sadiq, Sikiru O Zakariyya, Yusuf A. Sha’aban, Mohammad S. Shahriar, and Musab M. Isah. "SIFT-CNN Pipeline in Livestock Management: A Drone Image Stitching Algorithm." Drones 7, no. 1 (December 26, 2022): 17. http://dx.doi.org/10.3390/drones7010017.

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Images taken by drones often must be preprocessed and stitched together due to the inherent noise, narrow imaging breadth, flying height, and angle of view. Conventional UAV feature-based image stitching techniques significantly rely on the quality of feature identification, made possible by image pixels, which frequently fail to stitch together images with few features or low resolution. Furthermore, later approaches were developed to eliminate the issues with conventional methods by using the deep learning-based stitching technique to collect the general attributes of remote sensing images before they were stitched. However, since the images have empty backgrounds classified as stitched points, it is challenging to distinguish livestock in a grazing area. Consequently, less information can be inferred from the surveillance data. This study provides a four-stage object-based image stitching technique that, before stitching, removes the background’s space and classifies images in the grazing field. In the first stage, the drone-based image sequence of the livestock on the grazing field is preprocessed. In the second stage, the images of the cattle on the grazing field are classified to eliminate the empty spaces or backgrounds. The third stage uses the improved SIFT to detect the feature points of the classified images to o8btain the feature point descriptor. Lastly, the stitching area is computed using the image projection transformation.

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Abratt, Russell, and Thabiso Nsenki Mofokeng. "Development and management of corporate image in South Africa." European Journal of Marketing 35, no. 3/4 (April 1, 2001): 368–86. http://dx.doi.org/10.1108/03090560110382075.

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Gathers empirical evidence on how South African organisations manage their corporate image management process. The aim is to establish whether one of the leading models of the corporate image management process is applicable to practitioners in the area. While there is renewed interest in both academic and management circles, relatively few studies exist in the area of image management. In‐depth interviews with ten organisations that substantially changed their images recently were undertaken. The results are reported and confirm that the current knowledge and constructs on the corporate image management process are being put into practice by marketers.

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Zou, XiuFang, Dingju Zhu, Jun Huang, Wei Lu, Xinchu Yao, and Zhaotong Lian. "WGAN-Based Image Denoising Algorithm." Journal of Global Information Management 30, no. 9 (January 2022): 1–20. http://dx.doi.org/10.4018/jgim.300821.

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Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.

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Schaffland, Axel, and Gunther Heidemann. "Heritage and Repeat Photography: Techniques, Management, Applications, and Publications." Heritage 5, no. 4 (December 18, 2022): 4267–305. http://dx.doi.org/10.3390/heritage5040220.

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Repeat photography, the process of locating the camera position of an old photo and taking a new photo from this position, is utilized to protect our natural heritage, by measuring landscape changes between old and new image, and to conserve and recover our architectural and archaeological heritage, by observing changes between images taken before, during, and after conservation efforts or destructive events. Most importantly, repeat photography—in short, rephotography—is connecting us to our heritage on all levels from personal to global: while rephotographing, we connect with the old image and its creator and—when viewing and comparing the old image and the rephotograph—we connect with the tangible, intangible, and natural heritage shown in the images by observing changes and continuities. In this paper, we review publications and applications employing rephotography. We discuss the organization, retrieval, and presentation of rephotographic compilations, their generation by different image registration techniques, and their conservation as rephotographic heritage. Further, we describe challenges in rephotography, discuss how they differ from standard challenges in photography, computer vision, and image registration and present both analog and digital solutions and tools used in practice by rephotographers to address said challenges.

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FLIEGER, Marcin, and Michał FLIEGER. "IMAGE MANAGEMENT IN PUBLIC ADMINISTRATION." Scientific Journal of the Military University of Land Forces 162, no. 4 (October 1, 2011): 422–33. http://dx.doi.org/10.5604/01.3001.0002.3316.

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The article deals with the question of creating and sustaining a strong, positive image of public administration institutions. The idea is to emphasise the importance of putting ef-forts to manage the image actively and to present the undisputed benefits that this process brings. Hence, the paper explains thoroughly the characteristic of an image itself and how it developed to the so-called ‘rational approach’. Then the authors analyse very specific ideas and assumptions which lay at the basis of undertaking measures in the field of public relations, which differ substantially from the motivation in private companies. Moreover, the article scrutinizes crucial groups of the public which might be attracted to the region by its positive perception and profoundly analyses the instruments of image management, at the same focusing on the fields in which public administration institutions ought to take up measures in order to build a positive image.

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Park, C. Whan, Bernard J. Jaworski, and Deborah J. Maclnnis. "Strategic Brand Concept-Image Management." Journal of Marketing 50, no. 4 (October 1986): 135. http://dx.doi.org/10.2307/1251291.

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Stott, Will, Andy Ryan, Ian J. Jacobs, Usha Menon, Conrad Bessant, and Christopher Jones. "OSPACS: Ultrasound image management system." Source Code for Biology and Medicine 3, no. 1 (2008): 11. http://dx.doi.org/10.1186/1751-0473-3-11.

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Bouchenoire, Jean Léon. "Brand Integration: Total Image Management." Design Management Journal (Former Series) 11, no. 2 (April 2000): 10–14. http://dx.doi.org/10.1111/j.1948-7169.2000.tb00013.x.

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Park, C. Whan, Bernard J. Jaworski, and Deborah J. MacInnis. "Strategic Brand Concept-Image Management." Journal of Marketing 50, no. 4 (October 1986): 135–45. http://dx.doi.org/10.1177/002224298605000401.

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Conveying a brand image to a target market is a fundamental marketing activity. The authors present a normative framework, termed brand concept management (BCM), for selecting, implementing, and controlling a brand image over time. The framework consists of a sequential process of selecting, introducing, elaborating, and fortifying a brand concept. The concept guides positioning strategies, and hence the brand image, at each of these stages. The method for maintaining this concept-image linkage depends on whether the brand concept is functional, symbolic, or experiential. Maintaining this linkage should significantly enhance the brand's market performance.

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Watkins, Alexander. "Zotero for Personal Image Management." Art Documentation: Journal of the Art Libraries Society of North America 32, no. 2 (September 2013): 301–13. http://dx.doi.org/10.1086/673519.

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Clark, Timothy, and David Greatbatch. "Management Fashion as Image-Spectacle." Management Communication Quarterly 17, no. 3 (February 2004): 396–424. http://dx.doi.org/10.1177/0893318903257979.

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Brown, N. J. G., K. E. Britton, and D. L. Plummer. "Standardisation in medical image management." International Journal of Medical Informatics 48, no. 1-3 (February 1998): 227–38. http://dx.doi.org/10.1016/s1386-5056(97)00129-9.

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Ranft, Annette L., Robert Zinko, Gerald R. Ferris, and M. Ronald Buckley. "Marketing the Image of Management:." Organizational Dynamics 35, no. 3 (January 2006): 279–90. http://dx.doi.org/10.1016/j.orgdyn.2006.05.003.

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Hirvonen, J., and K. Ikonen. "Image Processing in Combustion Management." IFAC Proceedings Volumes 25, no. 6 (May 1992): 347–50. http://dx.doi.org/10.1016/s1474-6670(17)50929-5.

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Staab, Edward, Laurence P. Clarke, Houston Baker, and Daniel Sullivan. "NCI Image Archive Management Workshop." Academic Radiology 8, no. 7 (July 2001): 690–91. http://dx.doi.org/10.1016/s1076-6332(03)80699-2.

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Furnham, A. F. "Reputation, image and impression management." Personality and Individual Differences 17, no. 1 (July 1994): 153–54. http://dx.doi.org/10.1016/0191-8869(94)90275-5.

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Tsao, Hsuan-Ming. "Image Modalities for Arrhythmia Management." Journal of Arrhythmia 27, Supplement (2011): SY08_5. http://dx.doi.org/10.4020/jhrs.27.sy08_5.

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Yu, Yongqiang, Xuehu Yan, Shudong Wang, Xianhui Wang, and Huan Lu. "Thumbnail Secret Image Sharing in Cloud Storage." Mathematics 10, no. 17 (August 26, 2022): 3076. http://dx.doi.org/10.3390/math10173076.

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In recent years, the amount of data has increased explosively, which has spawned the large-scale development of cloud storage. Increasingly, individuals and enterprises store images in cloud space. The storage security of the cloud is generally guaranteed by encryption, but this can no longer meet the needs of image management and protection. In order to realize the management and loss tolerance of images, this paper proposes a thumbnail secret image sharing method. The proposed scheme combines the advantages of thumbnail-preserving encryption (TPE) and secret image sharing (SIS) with different meaningful shadows. Thumbnails can realize the visual management of stored images, and secret image sharing can realize the perfect security of stored images. The proposed scheme realizes the confidentiality, integrity, and availability of images, which are three elements of information security. Compared with TPE, our scheme not only realizes the visual management of images but also achieves loss tolerance and perfect security. Compared with SIS with different meaningful shadows, our scheme will greatly improve the sharing efficiency and reduce the consumption of computing resources. In this paper, the theoretical analysis and security proof of the proposed scheme are presented. In addition, we also conduct sufficient experiments and comparative explanations.

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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.

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Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

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Xu, Min, and Ya Fen Li. "Corneal Image Analysis and Information Management System Based on Slit Lamp." Applied Mechanics and Materials 599-601 (August 2014): 1842–47. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1842.

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With the development of digital image processing technology and computer technology, the capturing and processing of anterior segment image by computer to acquire feature information is possible. This paper captures the anterior eye segment by slit lamp, and then uses image processing technology to preprocess, extract the edge and fit the edge of the captured image. At the same time the paper establishes the information management system based on SSH framework, which can register patient information, manage patient files and images, and finally realize the paperless medical procedure.

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Sisodia, Omkar, Priyanka Bade, Pujari Ashwini, Mayuri Ambekar, and Prof Shiv Shinde. "Event Management using End-to-End Image Encryption: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3462–66. http://dx.doi.org/10.22214/ijraset.2022.43298.

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Abstract: Encryption-then-Compression (EtC) systems are used to securely transmit images over an untrusted channel provider, and a novel grayscale-based block scrambling image encryption method is developed to improve the security of EtC systems. This approach is intended to make encryption-then-compression (EtC) systems more secure. In comparison to the new encryption technology, the suggested scheme allows for smaller block sizes and a greater number of blocks.Despite the fact that the original image has three colour channels, photos encrypted using the suggested method contain less colour information due to the usage of grayscale images to encrypt the data. These features boost security against threats like jigsaw puzzle solvers and brute-force attacks, among other things. Apart from that, despite the fact that the encrypted photos do not include any colour information, it enables for colour sub-sampling, which can improve the compression speed of the images. In a test, encrypted photographs were posted to and later downloaded from social networking sites, and the findings demonstrated that using advanced compression algorithms, the suggested strategy is successful for ETC systems while still keeping excellent compression performance. Keywords: Event Management, image compression, non-local network, attention mechanism.

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Zhang, Zhen, and Xiang Xie. "Application of Image Processing and Identification Technology for Digital Archive Information Management." Traitement du Signal 39, no. 1 (February 28, 2022): 145–52. http://dx.doi.org/10.18280/ts.390114.

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It is difficult to manually build a digital management system for archive information. This paper explores the application of image processing and identification technology for digital archive information management, trying to provide convenient and swift services to archive mangers and archive information requestors, and to strengthen the standardized management of archive information. Firstly, the authors summarized the difficulties in digitalizing paper archives, and explained how to correct the tilt of digital archives. Next, the adaptive histogram equalization was improved to realize high-quality digitalization of archive images. In addition, the image processing flow was explained for the digitalization of archive images, and the horizontal projection histogram was adopted to quickly extract and detect the archive image texts being digitalized. The proposed image processing approach was proved effective through experiments.

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Kumar G Shukla, Suraj, Aadithya Kandeth, D. Sai Santhiya, and Kayalvizhi Jayavel. "Efficient Traffic Management System." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 926. http://dx.doi.org/10.14419/ijet.v7i3.12.16563.

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Traffic Management is a big issue which impacts us almost daily. Use of technology such as IoT and image processing can lead to a smooth traffic management system. The common reason for traffic congestion is due to the lack of an efficient traffic prioritization system. The internet of things is a network of devices. The embedded systems includes sensors, actuators, and electronics.With software and connectivity locally or over internet helps in transfer of data. Each of these devices is uniquely identifiable in the network and are highly interoperable. Image processing using OpenCV is a technique that is used to process an input image and obtain the traffic densities along various lanes in a junction. Existing traffic management solutions include using RFID tags on vehicles to obtain a vehicle count. This can also be done using ultrasonic sensors. The problem with these methods is that when implemented in a large scale, the cost of the entire system can be exponentially higher than an image processing approach as each vehicle will have to be fitted with an RFID tag. Hence, implementing this model at a largescale level, for example in a metropolitan city will be time consuming and expensive. This has led to the development of our algorithm, which uses image processing and IoT along with CCTV cameras. This system is efficient, as it uses CCTV cameras that are already present in traffic signals of most major metropolitan cities. Hence implementing the system at a largescale level is feasible. This algorithm also takes care of corner cases like heavy utility vehicles and motorbikes. It can also be used at night and during unfavorable weather conditions.The algorithm used detects the density of traffic as opposed to the count of vehicles by taking input images from a CCTV camera, comparing it with a sample image of an empty road and obtaining a match percentage. The traffic density can be found easily using this since it has a disproportionate relation with the match percentage. The traffic signals can be altered accordingly using the traffic density. The output is then sent to the ThingSpeak cloud where it can be analyzed.

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Murphy, Judy A. "Image Management for a Multi-Instrument, Multi-Platform Teaching Facility and Implications for Outreach Programs." Microscopy and Microanalysis 6, S2 (August 2000): 1166–67. http://dx.doi.org/10.1017/s1431927600038320.

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In the digital age, the management of images can be very time consuming when there are several digital image acquisition systems involved. For simplicity in this article, databases have been categorized as active or passive. Active databases are those where the categorization of the image is done during the image acquisition. Passive databases by this definition use saved images that are then categorized after image acquisition. The databases can be further separated based on whether they are multi-platform where they usually use a browser of some sort or Java script, or are specific to a certain platform such that no browser is involved. Some databases allow user defined fields, and some do not. Many of the image database software packages were made for categorizing images after they were collected i.e. passive, and developed for photography, catalogues, newspapers, etc. which do not necessarily cater to the needs in microscopy.

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