Topic
Parametric Image
About: Parametric Image is a research topic. Over the lifetime, 311 publications have been published within this topic receiving 6095 citations.
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Papers
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TL;DR: In this article, an object function is approximated by a series of known model functions such as box car and polynomial functions and the solution of the object function depends on a finite number of unknowns whereupon the model parameters can be perfectly resolved to obtain infinite or superresolution.
65 citations
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TL;DR: NLRRSC is a reliable and robust parametric imaging algorithm for dynamic PET studies and provides a reliable estimate of glucose metabolite uptake rate with a comparable image quality compared to Patlak analysis.
65 citations
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28 Mar 1995TL;DR: A fully automatic system for 2D model-based image coding of human faces for potential applications such as video telephony, database image compression, and face recognition that has been successfully tested on a database of nearly 2000 facial photographs.
Abstract: We present a fully automatic system for 2D model-based image coding of human faces for potential applications such as video telephony, database image compression, and face recognition. The system operates by locating a face in the input image, normalizing its scale and geometry and representing it in terms of a compact parametric image model obtained with a Karhunen-Loeve basis. This leads to a compact representation of the face that can be used for both recognition as well as image compression. Good-quality facial images are automatically generated using approximately 100-bytes worth of encoded data. The system has been successfully tested on a database of nearly 2000 facial photographs.
59 citations
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TL;DR: A modification of this procedure is suggested whereby the estimation of the parameters is performed using statistics which allow for efficient use of cellular or neighborhood image processors rather than those computed using the usual arithmetic operations.
53 citations
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TL;DR: This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context that is particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable.
Abstract: Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. The authors' approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. The authors apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. The authors present results on a variety of real range and intensity images. >
47 citations