In this paper, we present a software-based noninvasive system to implement binocular stereo vision with the help of polarized glass or other auxiliary equipments. For any application based on OpenGL, our system can gi...
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We present a novel method for the fast computation of rotation invariant "local binary patterns" (LBP) on 3D volume data. Unlike a previous publication on 3D LBP, this new approach is not limited to "un...
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In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets...
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ISBN:
(纸本)9781424439317
In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets, 3D + time) are used for this work. At first, significant anatomical structures, i.e. leaf surface and midplane are extracted robustly. Using the extracted anatomical structures, a biological reference coordinate system is registered to the leaves. The performed registration allows to determine intra- as well as inter-series spatiotemporal correspondences. Trichomes are localized by first detecting candidates using Hough transform. Then, local 3D invariants are extracted and the candidates are validated using a Support Vector Machine (SVM).
The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervis...
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The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.
One very generic approach towards the construction of features achieves invariance against a certain transformation via integration over the respective mathematical group. In this paper we present a general framework ...
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ISBN:
(纸本)9780889867192
One very generic approach towards the construction of features achieves invariance against a certain transformation via integration over the respective mathematical group. In this paper we present a general framework for invariant feature design via group integration for biomedical image analysis on 3D volumetric data, and show the common mathematical context of several previously published invariant methods which are all covered by this basic framework. We focus on the mathematical design paradigms of such features and provide fast implementation methods. Further we embed a priori knowledge into the design of highly specialized features. Practical applications to these methods are shown on several different datasets with a wide range of different biomedical image analysis problems.
This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is clos...
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This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is closest to it. Some attractive advantages of this learning algorithm are the robustness to noises and the scale of training samples, the straightforward way to handle multi-class tasks, and most of all the capability of processing high dimensional and nonlinear data. In our work, we deduce the NCH algorithm again basing on theories of the computational geometry, from which a simpler implementation of it is presented. Then we apply it to real-world remote problems and compare it with two other state-of-arts classifiers: K-NN and SVM. Experiments in this paper confirm the promising performance of NCH for remote sensing classification.
image quality evaluation is becoming essential in many imageprocessing problems. This paper proposes a new image quality evaluation approach based on decision fusion method of canonical correlation analysis (CCA). By...
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Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much e...
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How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is t...
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Locality Preserving Projection (LPP), as a linear manifold learning algorithm, has attracted much interests in recent years. LPP considers an n1× n2image as a vector in €n1×n2space, and thus is limited by th...
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