Journal•ISSN: 0377-2063
Iete Journal of Research
Taylor & Francis
About: Iete Journal of Research is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 0377-2063. Over the lifetime, 5408 publications have been published receiving 18500 citations.
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TL;DR: The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing, and future research directions are suggested.
Abstract: Retrieving information from the Web is becoming a common practice for internet users. However, the size and heterogeneity of the Web challenge the effectiveness of classical information retrieval techniques. Content-based retrieval of images and video has become a hot research area. The reason for this is the fact that we need effective and efficient techniques that meet user requirements, to access large volumes of digital images and video data. This paper gives a brief survey of current CBIR (Content Based Image Retrieval) methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing. Furthermore future research directions are suggested.
151 citations
TL;DR: The notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration are discussed.
Abstract: This paper is concerned with a systematic exposition of the usefulness of two-dimensional (2-D) discrete Gaussian Markov random field (GMRF) models for image processing applications. Specifically, we discuss the following topics; notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration.
146 citations
TL;DR: Diseases is an unusual circumstance that affects single or more parts of a human’s body and because of lifestyle and patrimonial, different kinds of disease are increasing day by day.
Abstract: Diseases is an unusual circumstance that affects single or more parts of a human’s body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those ...
118 citations
TL;DR: The links between learning and uncertainty are reviewed from three perspectives: statistical theories such as the Kalman filter, psychological models in which differential attention is paid to stimuli with an effect on the speed of learning associated with those stimuli, and neurobiological data on the influence of the neuromodulators acetylcholine and norepinephrine on learning and inference.
Abstract: It is a commonplace in statistics that uncertainty about parameters drives learning. Indeed one of the most influential models of behavioural learning has uncertainty at its heart. However, many popular theoretical models of learning focus exclusively on error, and ignore uncertainty. Here we review the links between learning and uncertainty from three perspectives: statistical theories such as the Kalman filter, psychological models in which differential attention is paid to stimuli with an effect on the speed of learning associated with those stimuli, and neurobiological data on the influence of the neuromodulators acetylcholine and norepinephrine on learning and inference.
109 citations
TL;DR: In this article, a mixed method is proposed for finding stable reduced order models of single-input-single-output large-scale systems using Pade approximation and the clustering technique, which guarantees stability of the reduced order model when the original high order system is stable.
Abstract: A mixed method is proposed for finding stable reduced order models of single-input- single-output large-scale systems using Pade approximation and the clustering technique. The denominator polynomial of the reduced order model is determined by forming the clusters of the poles of the original system, and the coefficients of numerator polynomial are obtained by using the Pade approximation technique. This method guarantees stability of the reduced order model when the original high order system is stable. The methodology of the proposed method is illustrated with the help of examples from literature.
103 citations