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Donggang Yu Author Vitae Tuan D. Pham Author Vitae Author Vitae Stephen T.C. Wong Author Vitae 《Pattern recognition》2009,42(4):498-508
Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method. 相似文献
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Fethallah Benmansour Laurent D. Cohen 《Journal of Mathematical Imaging and Vision》2009,33(2):209-221
In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal
path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically
using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the
topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain
visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal
paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving
extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar
idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to
3D data with promising results.
相似文献
Laurent D. CohenEmail: |
976.
This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned
mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function
is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures
are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and
stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show
that our approach consistently outperforms other kernel based face recognition methods.
相似文献
Alex KotEmail: |
977.
Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system.A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system. 相似文献
978.
The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning. 相似文献
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