Relevant bibliographies by topics / Image processing Information storage and retrieval systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Image processing Information storage and retrieval systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Image processing Information storage and retrieval systems"

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Aouat, Saliha, and Slimane Larabi. "Object Retrieval Using the Quad-Tree Decomposition." Journal of Intelligent Systems 23, no. 1 (2014): 33–47. http://dx.doi.org/10.1515/jisys-2013-0014.

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AbstractWe propose in this article an indexing and retrieval approach applied on outline shapes. Models of objects are stored in a database using the textual descriptors of their silhouettes. We extract from the textual description a set of efficient similarity measures to index the silhouettes. The extracted features are the geometric quasi-invariants that vary slightly with the small change in the viewpoint. We use a textual description and quasi-invariant features to minimize the storage space and to achieve an efficient indexing process. We also use the quad-tree structure to improve proce
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Philip, G., D. Crookes, and Z. Juhasz. "QUIMaS (Queen's University Image Management System): a museum photographic database." Journal of Information Science 20, no. 3 (1994): 161–74. http://dx.doi.org/10.1177/016555159402000302.

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This paper reports on the construction of an interactive image management system. The system's operation is improved by image processing techniques and parallel processing technology. Although image processing is largely a well understood problem, the technology for supporting it is still immature. Basically, it is a highly computationally intensive activity, and the purpose of our project was to address this very problem using parallel processing technology in the form of transputers. More specifically, we will describe the use of a transputer network in the development of an image database c
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Ai, Qingyao. "Neural generative models and representation learning for information retrieval." ACM SIGIR Forum 53, no. 2 (2019): 97. http://dx.doi.org/10.1145/3458553.3458565.

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Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensio
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Nguyen Mau, Toan, and Yasushi Inoguchi. "Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices." Applied Sciences 10, no. 7 (2020): 2539. http://dx.doi.org/10.3390/app10072539.

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It is challenging to build a real-time information retrieval system, especially for systems with high-dimensional big data. To structure big data, many hashing algorithms that map similar data items to the same bucket to advance the search have been proposed. Locality-Sensitive Hashing (LSH) is a common approach for reducing the number of dimensions of a data set, by using a family of hash functions and a hash table. The LSH hash table is an additional component that supports the indexing of hash values (keys) for the corresponding data/items. We previously proposed the Dynamic Locality-Sensit
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Ahmed, Khawaja Tehseen, Shahida Ummesafi, and Amjad Iqbal. "Content based image retrieval using image features information fusion." Information Fusion 51 (November 2019): 76–99. http://dx.doi.org/10.1016/j.inffus.2018.11.004.

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FENG, DAGAN (DAVID). "CONTENT-BASED RETRIEVAL OF MULTIMEDIA INFORMATION." International Journal of Image and Graphics 01, no. 01 (2001): 83–91. http://dx.doi.org/10.1142/s0219467801000074.

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The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and
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Pillay, Karan Ravindran, and Omkar Upendra Khadilkar. "The Scalable Image Retrieval Systems and Applications." International Journal of Engineering and Computer Science 7, ``11 (2018): 24406–8. http://dx.doi.org/10.18535/ijecs/v7i11.03.

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Advances in information storage and image acquisition technologies have enabled the creation of enormous image datasets. during this situation, it's necessary to develop applicable data systems to with efficiency manage these collections. the most typical approaches use the supposed Content-Based Image Retrieval (CBIR) systems. Basically, these systems attempt to retrieve pictures like a user-defined specification or pattern (e.g., form sketch, image example). Their goal is to support image retrieval supported content properties (e.g., shape, color, texture), typically encoded into feature vec
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Yang, Wenjing, Liejun Wang, Shuli Cheng, Yongming Li, and Anyu Du. "Deep Hash with Improved Dual Attention for Image Retrieval." Information 12, no. 7 (2021): 285. http://dx.doi.org/10.3390/info12070285.

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Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for ima
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D'Attoma, N., P. L. Pavan, M. Bertocco, and P. Dompieri. "Management of image information." Urologia Journal 61, no. 1 (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 jus
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CHANG, CHIN-CHEN, JUN-CHOU CHUANG, and YIH-SHIN HU. "SIMILAR IMAGE RETRIEVAL BASED ON WAVELET TRANSFORMATION." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 02 (2004): 111–20. http://dx.doi.org/10.1142/s021969130400038x.

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In this paper, a novel similar image retrieval scheme based on wavelet transformation will be presented. Our scheme is built upon a block-based query system. Our new scheme employs the wavelet transformation technique to transform each block in the spatial domain to the wavelet domain. Then, from each transformed block, the mean value and the edge types are extracted. These extracted features are then used to compute the similarity between a query image and the images in the database. In order to increase the similarity in the query result, the current block can be further divided into many su
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Dissertations / Theses on the topic "Image processing Information storage and retrieval systems"

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Janicki, James H. "Retrieval from an image knowledge base /." Online version of thesis, 1993. http://hdl.handle.net/1850/12196.

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Zhang, Dengsheng 1963. "Image retrieval based on shape." Monash University, School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8688.

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Ren, Feng Hui. "Multi-image query content-based image retrieval." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070103.143624/index.html.

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Li, Yuanxi. "Semantic image similarity based on deep knowledge for effective image retrieval." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/99.

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A flourishing World Wide Web dramatically increases the amount of images up­loaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the seman­tic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrie
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Lamb, Robert Ray. "An information retrieval system for images from the trace satellite." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/lamb/LambR0508.pdf.

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The ability to identify particular features and structures, such as faces or types of scenery in images, is a topic with many available applications and potential solutions. In this paper we discuss solar images and the results of our preliminary investigation of techniques that can be used to identify solar phenomena in images from the TRACE satellite. Being able to automatically identify various phenomena in solar images is of great interest for scientists studying phenomena on the sun. A set of characteristics that can be quickly extracted from solar images needs to be acquired. These chara
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Spellman, Eric. "Fusing probability distributions with information theoretic centers and its application to data retrieval." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011388.

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Goodrum, Abby A. (Abby Ann). "Evaluation of Text-Based and Image-Based Representations for Moving Image Documents." Thesis, University of North Texas, 1997. https://digital.library.unt.edu/ark:/67531/metadc500441/.

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Document representation is a fundamental concept in information retrieval (IR), and has been relied upon in textual IR systems since the advent of library catalogs. The reliance upon text-based representations of stored information has been perpetuated in conventional systems for the retrieval of moving images as well. Although newer systems have added image-based representations of moving image documents as aids to retrieval, there has been little research examining how humans interpret these different types of representations. Such basic research has the potential to inform IR system designe
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Wong, Chun Fan. "Automatic semantic image annotation and retrieval." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1188.

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Jaisimha, M. Y. "Compound document retrieval in noisy environments /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/6007.

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Ozkan, Gokhan. "Web based database processing for Turkish Navy Officers in USA." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FOzkan.pdf.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2002.<br>Thesis advisor(s): William J. Haga, Gary Porter. Includes bibliographical references (p. 63). Also available online.
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Books on the topic "Image processing Information storage and retrieval systems"

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Image retrieval: Theory and research. Scarecrow Press, 2003.

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Heterick, Robert C. A single system image: An information systems strategy. CAUSE--The Professional Association for Computing and Information Technology in Higher Education, 1988.

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Gong, Yihong. Intelligent Image Databases: Towards Advanced Image Retrieval. Springer US, 1998.

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D'Alleyrand, Marc R. Image storage and retrieval systems: A new approach to records management. Intertext Publications, 1989.

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D'Alleyrand, Marc R. Image storage and retrieval systems: A new approach to records management. McGraw-Hill, 1989.

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Cawkell, A. E. A guide to image processing and picture management. Gower, 1994.

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Exploratory image databases: Content-based retrieval. Academic Press, 2001.

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Conniss, Lynne R. Information seeking behaviour in image retrieval: VISOR I final report. Institute for Image Data Research, University of Northumbria at Newcastle, 2000.

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Julie, Ashford A., Graham Margaret E, Library and Information Commission, and University of Northumbria at Newcastle. Institute for Image Data Research., eds. Information seeking behaviour in image retrieval: VISOR 1 final report. Institute for Image Data Research, University of Northumbria at Newcastle, 2001.

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Wong, Stephen T. C. Medical Image Databases. Springer US, 1998.

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Book chapters on the topic "Image processing Information storage and retrieval systems"

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Jaminet, Jean, Gabriel Esquivel, and Shane Bugni. "Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow." In Proceedings of the 2021 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_1.

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AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.
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Chan, Chee Sheen, and Jer Lang Hong. "A Comparative Study on Image Retrieval Systems." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42051-1_17.

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Bizais, Yves, Anne-Marie Forte, Bernard Gibaud, and Florent Aubry. "Storage and retrieval of medical image data, relationship to image processing and analysis." In Picture Archiving and Communication Systems (PACS) in Medicine. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76566-7_52.

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Hausser, Roland. "Parsing Natural Language into Content for Storage and Retrieval in a Content-Addressable Memory." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13881-2_17.

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"The Storage and Retrieval Technologies of Quantum Images." In Examining Quantum Algorithms for Quantum Image Processing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3799-2.ch002.

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Quantum image processing represents an emerging image processing technology by taking advantage of quantum computation. Quantum image processing faces the first question: How is an image stored in and retrieved from a quantum system? To solve the issue, the authors provide six quantum image representations, which can be divided into three categories. The first, second, and third categories store color information using amplitudes, phases, and basis states, respectively. Next, they design their circuits to implement the storage of quantum image. Then, retrieval methods are introduced. The storage and retrieval technologies of quantum image are the basis and premise condition to process quantum images.
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Srivastava, Prashant, and Ashish Khare. "On Visual Information Retrieval Using Multiresolution Techniques for Web Usage Mining Applications." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch029.

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The proliferation of huge amount of information has made it essential to develop systems that organize and index them for easy access. The advent of World Wide Web has provided immense opportunity to the people across the world to access and share information for different uses ranging from personal to professional. Various web mining techniques are applied to retrieve useful information as well as improvement of existing techniques of mining to search and retrieve useful information from the web. With the growth in the number of devices producing various forms of information, the amount of information is increasing exponentially. Also, these huge amount of information are being shared in the world through various means. Hence, it has become necessary to organize information in such a manner so that access to them is easy and feasible. As the amount of information is increasing rapidly, efficient indexing of information for easy access is becoming quite challenging. Hence, there is a need to search for solutions to solve this problem. The field of information retrieval attempts to solve this problem. Information retrieval is concerned with storage, organization, indexing, and retrieval of information. Information retrieval techniques incorporate several aspects of information to achieve the target of efficient indexing. Since there are several forms of information, their characteristics vary a lot from each other. Image is one such popular form of information which is shared the most among the people around the world. Also, with the presence of numerous image capturing devices, acquisition of image is no longer a difficult task. People enjoy capturing and sharing images through social network. Although image is a complex structure, it is easily understood by people across the world. Also, it has become a popular means of information sharing among people. This chapter discusses information retrieval techniques for image data. Visual Information Retrieval or Content-Based Image Retrieval (CBIR) accepts query in the form of image or image features instead of text. It is concerned with searching and retrieval of images similar to the query given in the form of images. Most of the visual information retrieval techniques are based on processing single resolution of an image. But processing of single resolution of image is not sufficient for efficient retrieval as image is a complex structure and contains varying level of details. Hence, there is a need of multiresolution processing of images. Today, it is very difficult to keep track of number of research papers based on multiresolution analysis as it is widely used for various image-based applications. Also, there are a number of multiresolution techniques available to achieve this. Multiresolution processing has one big advantage that features that are left undetected at one level get detected at another level which is not the case with single resolution analysis. We demonstrate this fact with the help of an experiment using Discrete Wavelet Transform along with the discussion of various multiresolution techniques for visual information retrieval. The experiment helps in explaining the important properties of multiresolution analysis and also provides future scope of research in this field.
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Srivastava, Prashant, and Ashish Khare. "On Visual Information Retrieval Using Multiresolution Techniques for Web Usage Mining Applications." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch012.

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The proliferation of huge amount of information has made it essential to develop systems that organize and index them for easy access. The advent of World Wide Web has provided immense opportunity to the people across the world to access and share information for different uses ranging from personal to professional. Various web mining techniques are applied to retrieve useful information as well as improvement of existing techniques of mining to search and retrieve useful information from the web. With the growth in the number of devices producing various forms of information, the amount of information is increasing exponentially. Also, these huge amount of information are being shared in the world through various means. Hence, it has become necessary to organize information in such a manner so that access to them is easy and feasible. As the amount of information is increasing rapidly, efficient indexing of information for easy access is becoming quite challenging. Hence, there is a need to search for solutions to solve this problem. The field of information retrieval attempts to solve this problem. Information retrieval is concerned with storage, organization, indexing, and retrieval of information. Information retrieval techniques incorporate several aspects of information to achieve the target of efficient indexing. Since there are several forms of information, their characteristics vary a lot from each other. Image is one such popular form of information which is shared the most among the people around the world. Also, with the presence of numerous image capturing devices, acquisition of image is no longer a difficult task. People enjoy capturing and sharing images through social network. Although image is a complex structure, it is easily understood by people across the world. Also, it has become a popular means of information sharing among people. This chapter discusses information retrieval techniques for image data. Visual Information Retrieval or Content-Based Image Retrieval (CBIR) accepts query in the form of image or image features instead of text. It is concerned with searching and retrieval of images similar to the query given in the form of images. Most of the visual information retrieval techniques are based on processing single resolution of an image. But processing of single resolution of image is not sufficient for efficient retrieval as image is a complex structure and contains varying level of details. Hence, there is a need of multiresolution processing of images. Today, it is very difficult to keep track of number of research papers based on multiresolution analysis as it is widely used for various image-based applications. Also, there are a number of multiresolution techniques available to achieve this. Multiresolution processing has one big advantage that features that are left undetected at one level get detected at another level which is not the case with single resolution analysis. We demonstrate this fact with the help of an experiment using Discrete Wavelet Transform along with the discussion of various multiresolution techniques for visual information retrieval. The experiment helps in explaining the important properties of multiresolution analysis and also provides future scope of research in this field.
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Wu, QingE, and Weidong Yang. "A Local Approach and Comparison with Other Data Mining Approaches in Software Application." In Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1884-6.ch001.

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In order to complete an online, real-time and effective aging detection to software, this paper studies a local approach that is also called a fuzzy incomplete and a statistical data mining approaches, and gives their algorithm implementation in the software system fault diagnosis. The application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis is discussed. The performance of each approach is evaluated from the sensitivity, specificity, accuracy rate, error classified rate, missed classified rate, and run-time. An optimum approach is chosen from several approaches to do comparative study. On the data of 1020 samples, the operating results show that the fuzzy incomplete approach has the highest sensitivity, the forecast accuracy that are 96.13% and 94.71%, respectively, which is higher than those of other approaches. It has also the relatively less error classified rate is or so 4.12%, the least missed classified rate is or so 1.18%, and the least runtime is 0.35s, which all are less than those of the other approaches. After the performance, indices are all evaluated and synthesized, the results indicate the performance of the fuzzy incomplete approach is best. Moreover, from the test analysis known, the fuzzy incomplete approach has also some advantages, such as it has the faster detection speed, the lower storage capacity, and does not need any prior information in addition to data processing. These results indicate that the mining approach is more effective and feasible than the old data mining approaches in software aging detection.
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Bhaumik, Hrishikesh, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Content Coverage and Redundancy Removal in Video Summarization." In Intelligent Analysis of Multimedia Information. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0498-6.ch013.

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Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.
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Li, Qing, Yi Zhuang, Jun Yang, and Yueting Zhuang. "Multimedia Information Retrieval at a Crossroad." In Encyclopedia of Multimedia Technology and Networking, Second Edition. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch134.

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From late 1990s to early 2000s, the availability of powerful computing capability, large storage devices, high-speed networking, and especially the advent of the Internet, led to a phenomenal growth of digital multimedia content in terms of size, diversity, and impact. As suggested by its name, “multimedia” is a name given to a collection of data of multiple types, which include not only “traditional multimedia” such as images and videos, but also emerging media such as 3D graphics (like VRML objects) and Web animations (like Flash animations). Furthermore, relevant techniques have been developed for a growing number of applications, ranging from document editing software to digital libraries and many Web applications. For example, most people who have used Microsoft Word have tried to insert pictures and diagrams into their documents, and they have the experience of watching online video clips such as movie trailers from Web sites such as YouTube.com. Multimedia data have been available in every corner of the digital world. With the huge volume of multimedia data, finding and accessing the multimedia documents that satisfy people’s needs in an accurate and efficient manner becomes a nontrivial problem. This problem is referred to as multimedia information retrieval. The core of multimedia information retrieval is to compute the degree of relevance between users’ information needs and multimedia data. A user’s information need is expressed as a query, which can be in various forms such as a line of free text like “Find me the photos of George Washington,” a few keywords like “George Washington photo,” a media object like a sample picture of George Washington, or their combinations. On the other hand, multimedia data are represented using a certain form of summarization, typically called index, which is directly matched against queries. Similar to a query, the index can take a variety of forms, including keywords, visual features such as color histogram and motion vector, depending on the data and task characteristics. For textual documents, mature information retrieval (IR) technologies have been developed and successfully applied in commercial systems such as Web search engines. In comparison, the research on multimedia retrieval is still in its early stage. Unlike textual data, which can be well represented by term vectors that are descriptive of data semantics, multimedia data lack an effective, semantic-level representation that can be computed automatically, which makes multimedia retrieval a much harder research problem. On the other hand, the diversity and complexity of multimedia data offer new opportunities for the retrieval task to be leveraged by the techniques in other research areas. In fact, research on multimedia retrieval has been initiated and investigated by researchers from areas of multimedia database, computer vision, natural language processing, human-computer interaction, and so forth. Overall, it is currently a very active research area that has many interactions with other areas. In the coming sections, we will overview the techniques for multimedia information retrieval, followed by a review on the applications and challenges in this area. Then, the future trends will be discussed, and some important terms in this area are defined at the end of this chapter.
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Conference papers on the topic "Image processing Information storage and retrieval systems"

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Igwe, Selwyn O., and Adel S. Elmaghraby. "Medical image storage system for content-based retrieval." In 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2010. http://dx.doi.org/10.1109/isspit.2010.5711750.

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Bargiotas, Dimitrios, Aphrodite Ktena, Christos Manasis, and Onoufrios Ladoukakis. "A Scalable Low-Cost Automated Storage & Retrieval System." In 2009 16th International Conference on Systems, Signals and Image Processing. IEEE, 2009. http://dx.doi.org/10.1109/iwssip.2009.5367724.

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Kim, Do-Hyung, No-Cheol Park, Sungbin Jeon, and Young-Pil Park. "Novel Method of Crosstalk Analysis in Multiple Image Encryption and Image Quality Equalization Technology." In ASME 2014 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/isps2014-6909.

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Novel crosstalk analysis method is suggested in optical encryption of multiple image. To optimize the total capacity of stored images in optical encryption of multiple images. We analyze the effect of crosstalk noise with each individual image by using suggested method. From the results, individual crosstalk robustness is verified with various target images and unbalance of image qualities among encrypted multiple images could be explained effectively. In addition, simple modulation method is adapted to equalizing image quality and it shows the highly improved results compare to conventional m
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Al-Qahtani, Meshal, Abbes Amira, and Naeem Ramzan. "An efficient information retrieval technique for e-health systems." In 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2015. http://dx.doi.org/10.1109/iwssip.2015.7314225.

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Bowman, Elizabeth K., Barbara D. Broome, V. Melissa Holland, et al. "Complex event processing for content-based text, image, and video retrieval." In 2016 International Conference on Military Communications and Information Systems (ICMCIS). IEEE, 2016. http://dx.doi.org/10.1109/icmcis.2016.7496546.

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"Session MP7a: Machine learning for information retrieval, speech, and image processing (invited)." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335198.

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Yu, Jie, and Hiroshi Yamaura. "Vibration Reduction for Image Transfer Belt System With H∞ Optimal Control Configuration." In ASME 2014 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/isps2014-6925.

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An optimal control architecture based on H∞ norm is presented for the rejection of loading disturbance in a class of image transfer belt modeled as a four-rollers-belt system. The proposed H∞ controller acts on the actuating torque provided by a direct-current motor and optimizes all variables concerning quality of the original image formation. Simulation results responding to a periodic loading disturbance with comparison of the insufficiency and performance limitation in conventional speed control ascertain its effectiveness. It is shown that the image transfer belt system is successfully ma
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Chao, Paul C. P., Jian-Ruei Chen, Che-Hung Tsai, and Wei-Dar Chen. "Design and Realization of High Resolution (640×480) SWIR Image Acquisition System." In ASME 2013 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/isps2013-2917.

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Imaging technology has been in revolutionary progresses in decades with well-developed semiconductor and memory industries. Silicon sensors are used in most of camera and DV, since silicon is the best material for visible light imaging (wavelength from 400nm∼700nm). Short wave infrared (SWIR) requires indium gallium arsenide (InGaAs), composed of chemical compounds including indium arsenide (InAs) and gallium arsenide (GaAs), to cover SWIR spectrum. Wavelength of typical SWIR is defined between 0.7um and 2.5um; SWIR cameras focus on wavelength between 0.9um∼1.7um (In0.53Ga0.47As). Unlike Mid-W
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Yuanzhong Xu. "A model based on three-layer agent of personalized information retrieval systems." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109133.

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Mehring, James W. "Demonstration of dissemination, storage, and retrieval of Defense Mapping Agency digital products over a distributed enterprise network." In 23 Annual AIPR Workshop--Image and Information Systems: Applications and Opportunities, edited by Peter J. Costianes. SPIE, 1995. http://dx.doi.org/10.1117/12.200796.

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