7470395
A Simulation-Based 3D Axon Axis Extraction in Confocal Fluorescence Microscopy Images
Neuron axon analysis is a fundamental mean to investigate nervous system in neurobiology and often requires collecting a great amount of statistical information. Automated extraction of axons axis in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons with several touchings in 3D volumes, a novel simulation based approach with regard to volume modeling and scenario mapping is presented. Given the simulation model, the properties of dataset and the nature of axon (e.g., continuity) are converted to physics laws including the principle of minimum potential energy, law of buoyancy, and law of inertia. Experimental results on axon volumes show the proposed algorithm can effectively handle axon touching problem and actually extract multiple axon axes.
225470
A 3D Self-Adjust Region Growing Method for Axon Extraction
Neuron axon analysis is an important means to investigate disease mechanisms and signaling pathways in neurobiology and often requires collecting a great amount of statistical information and phenomena. Automated extraction of axons in 3D microscopic images posts a key problem in the field of neuron axon analysis. To address tortuous axons in 3D volumes, a self-adjust region growing approach referring to surface modeling and self-adjustment which takes advantage of the nature of axon (e.g., continuity), is presented. Experimental results on axon volumes show that the proposed scheme provides a reliable solution to axon retrieving and overcomes several common drawbacks from other existing methods.
2401406
Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification
A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.
14078787
Maximum Margin Clustering Made Practical
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (MMC) is a recent approach that aims at extending large margin methods to unsupervised learning. However, its optimization problem is nonconvex and existing MMC methods all rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programs (SDP). Though SDP is convex and standard solvers are available, they are computationally very expensive and only small data sets can be handled. To make MMC more practical, we avoid SDP relaxations and propose in this paper an efficient approach that performs alternating optimization directly on the original nonconvex problem. A key step to avoid premature convergence in the resultant iterative procedure is to change the loss function from the hinge loss to the Laplacian/square loss so that overconfident predictions are penalized. Experiments on a number of synthetic and real-world data sets demonstrate that the proposed approach is more accurate, much faster (hundreds to tens of thousands of times faster), and can handle data sets that are hundreds of times larger than the largest data set reported in the MMC literature.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
13932512
Joint Transmit Rate, Power and Antenna Allocation for MIMO Systems with Multimedia Traffic
This paper proposes an adaptive transmission scheme for MIMO systems to provide different bit error rates and transmission rates for multimedia traffic. The adaptive transmission scheme allocates antennas, rate and power jointly according to the feedback information to satisfy the diverse QoS requirements of the multimedia traffic. Furthermore, an efficient search algorithm with low complexity is proposed for practical implementation. Simulation results show that the proposed scheme improves the spectral efficiency while guaranteeing the QoS requirements of multimedia traffic. Moreover, the proposed search algorithm achieves close optimal performance with great complexity reduction.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.