270876
Feature Extraction with Genetic Algorithms Based Nonlinear Principal Component Analysis for Face Recognition
Principal component analysis (PCA) and linear discriminant analysis (LDA) are two commonly used feature extraction techniques. In this paper, a nonlinear evolutionary weighted principal component analysis (EWPCA) based on genetic algorithms is proposed. Similar to LDA, the EWPCA maximizes the ratio of between-class variations to that of within-class variations, and achieves better classification performance than that of traditional PCA. Genetic algorithms are chosen as the searching method to select optimal weights for the EWPCA. In face recognition, evolutionary facial feature obtained by performing EWPCA is used as the representation of original face images. Experimental results on ORL and combo face databases prove that EWPCA outperforms both PCA, kernel PCA and LDA
7829422
Fast Relative Depth Computation for an Active Stereo Vision System
Real-time computation of exact depth is not feasible in an active vision setup. Instead, reliable relative depth information which can be rapidly computed is preferred. In this paper, a stereo cue for computing relative depth obtained from an active stereo vision system is proposed. The proposed stereo cue can be computed purely from the coordinates of points in the stereo pair. The computational cost required is very low. No camera calibration or prior knowledge of the parameters of the stereo vision system is required. We show that the relationship between the relative depth cue and the actual depth in the three-dimensional (3D) space is monotonic. Such a relation is maintained even when the focal length and the vergence angle are changed, so long as the focal lengths of the two cameras are similar. Therefore, real-time implementation in an active vision setup can be realized. Stability analysis shows that the proposed method will be stable in practical situations, unless the stereo camera diverges. Experimental results are presented to highlight the properties and advantages of the proposed method.
2288578
Efficient algorithms for obtaining algebraic invariants from higher degree implicit polynomials for recognition of curved objects
Implicit polynomials can be used for describing a large variety of curved shapes. In most of the earlier works, the emphasis had been on the use of second degree implicit polynomials. To describe more complex curves and surfaces, it is necessary that higher degree implicit polynomials be used. In this paper, we have proposed a tensor based approach for obtaining affine and Euclidean invariants from the coefficients of higher degree implicit polynomials. Our approach is more general and computationally efficient than the matrix based approaches for obtaining invariants. For the Euclidean case, the algorithm can be used for both recognition and pose estimation. In this paper, we have demonstrated our approach for obtaining invariants of 2-D shapes using third and fourth degree implicit polynomials.
35155949
Constrained nonlinear models of fingerprint orientations with prediction
In this paper, we developed an algorithm to model the fingerprint orientation field. The algorithm comprises two steps, orientation prediction and model computation. Orientation prediction is based on piece-wise first-order phase portrait model. It is used to estimate the orientation in areas where there is no ridge information in the input image or the coherence of the orientation field is low. In the model computation, a constrained nonlinear phase portrait algorithm is proposed, which aims to get an accurate mathematical model of the fingerprint orientation field. Compared to the prior works, this algorithm is able to predict orientation even in noisy regions and it integrates the global and local orientation description into a unified mathematical form. Experiments conducted on the first 500 images of the NIST-4 database showed that the proposed algorithm is able to model all the fingerprint orientation patterns except those with very poor quality images where the orientation information cannot be clearly extracted.
29758508
Corner Detection Based on Finding Edge Points Locally
28149553
Fingerprint Indexing Based On Symmetrical Measurement
Due to the small number of classes and uneven distribution, the Henry's fingerprint classification approach is not able to provide an efficient way to narrow down the search when identifying a person in a large database. In this paper, three symmetrical filters are used to measure different orientation structures. We proposed a new fingerprint indexing approach based on these three symmetrical measurements. Experiments conducted on the NIST database 4 show the effectiveness of the proposed approach
14855769
Web Services Enabled E-Market Access Control Model
With the dramtic expansion of global e-markets, companies collaborate more and more in order to streamline their supply chains. Companies often form coalitions to reach the critical mass required to bid on a large volume or wide ranges of products. Meanwhile, they also compete with one another for market shares. Because of the complex relationships among companies, controlling the access to shared information found in e-markets is a challenging task. Currently, there is a lack of comprehensive approach in access control that can be used to maintain data security in e-markets. We propose to integrate several known access control mechanisms such as role-based access control, coalition-based access control, and relationship driven access control into an e-market access control (EMAC) model. In this paper, we present a web services based architecture for EMAC and the associated concepts and algorithms. We also illustrate via an automotive e-market example how the EMAC model can support e-market access control.
5819516
Facial expression decomposition
In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.
134895
TCOZ approach to semantic web services design
Complex Semantic Web (SW) services may have intricate data state, autonomous process behavior and concurrent interactions. The design of such SW service systems requires precise and powerful modelling techniques to capture not only the ontology domain properties but also the services' process behavior and functionalities. In this paper we apply an integrated formal modeling language, Timed Communicating Object Z (TCOZ), to design SW services. Furthermore, the paper presents the development of the systematic translation rules and tools which can automatically extract the SW ontology and services semantic markup from the formal TCOZ design model.
16403613
Gradient Adaptive Image Restoration and Enhancement
Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
15118493
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.