In this paper we address the problem of reliably fitting parametric and semi-parametric models to spots in high density spot array images obtained in gene expression experiments. The goal is to measure the amount of l...
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A technique for the construction of invariant features of 3D sensor-data is proposed. Invariant grey-scale features are characteristics of grey-scale sensor-data which remain constant if the sensor-data is transformed...
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ISBN:
(纸本)0769507506
A technique for the construction of invariant features of 3D sensor-data is proposed. Invariant grey-scale features are characteristics of grey-scale sensor-data which remain constant if the sensor-data is transformed according to the action of a transformation group. The proposed features are capable of recognizing 3D objects independent of their orientation and position, which can be used e.g. in medical image analysis. The computation of the proposed invariants needs no preprocessing like filtering, segmentation, or registration. After the introduction of the general theory for the construction of invariant features for 3D sensor-data, the paper focuses on the special case of 3D Euclidean motion which is typical for rigid 3D objects. Due to the fact that we use the function of local support the calculated invariants are also robust with respect to independent Euclidean motion, articulated objects, and even topological deformations. The complexity of the method is linear in the data-set size which may be too high for large 3D objects. Therefore approaches for the acceleration of the computation are given. First experimental results for artificial 3D objects are presented in the paper to demonstrate the invariant properties of the proposed features.
Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In ...
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Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fur calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.
In this paper,we presented a displacement field estimation algorithm based on a relaxed smoothness constraint;this algorithmcan preserve discontinuities in the displacement field to some *** image data is irregular an...
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In this paper,we presented a displacement field estimation algorithm based on a relaxed smoothness constraint;this algorithmcan preserve discontinuities in the displacement field to some *** image data is irregular and the images are noisy,the method produces some big residual errors in the residual *** this paper we propose an improved displacement field estimation algorithm which uses the displacement information obtained using blockmatching to modify the matching *** results show,this leads to smaller residual error maps, without introducing block artefacts,as would happen in the case of simple block matching when there is much noise in the *** the displacement filed using this method is more consistent than using a method without additional block matching.
Presents a framework for the geometric interpretation of a single polarization image taken of specular reflecting objects. The task of recovering 3D shape information of specular objects from intensity images is a dif...
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ISBN:
(纸本)0769507506
Presents a framework for the geometric interpretation of a single polarization image taken of specular reflecting objects. The task of recovering 3D shape information of specular objects from intensity images is a difficult if not impossible one, as no intensity based features are available. Polarization analysis provides additional features to overcome the problem. In particular the orientation of polarized light is measured, from which constraints on the object surface can be deduced. We show how to perform shape analysis from a single polarization image. The method is applied to local deformation measuring. Along with a discussion of polarization and related geometric issues, algorithms are presented and substantiated by experiments.
We presented a displacement field estimation algorithm based on a relaxed smoothness constraint; this algorithm can preserve discontinuities in the displacement field to some extent. Because image data is irregular an...
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We presented a displacement field estimation algorithm based on a relaxed smoothness constraint; this algorithm can preserve discontinuities in the displacement field to some extent. Because image data is irregular and the images are noisy, the method produces some large residual errors in the residual maps. We propose an improved displacement field estimation algorithm which uses the displacement information obtained using block-matching to modify the matching result. Experimental results show, this leads to smaller residual error maps, without introducing block artefacts, as would happen in the case of simple block-matching when there is much noise in the background. Also the displacement filed using this method is more consistent than using a method without additional block-matching.
It has been shown that the branch and bound technique is effective for the design of finite wordlength optimal digital filters. This technique is however expensive in computing time. In this paper, we present a robust...
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Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at speci...
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Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at specific spot locations. Many spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots the authors use robust M-estimators. When the parametric method fails, they use a novel, robust semi-parametric method which can handle spots of different shapes accurately. They present the results for real data and compare the complexity of the two methods.
The concept of "nearness", which has been dealt with as soon as one started studying digital images, finds one of its rigorous forms in the notion of proximity space. It is this notion, together with "n...
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A method for zip code recognition is presented. 2D binary images are input to HAVNET, a neural network which employs the Hausdorff distance as a similarity metric to train the weights which are required to represent t...
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