17389325
Analysis of gray level corner detection
Abstract In this paper the analysis of gray level corner detection has been carried out. Performances of various cornerness measures are discussed with respect to four performances of robustness: detection, localization, stability and complexity. We have analyzed the interior differential features of the image surface of these cornerness measures. This paper presents a new approach called gradient-direction corner detector for the corner detection which is developed from the popular Plessey corner detection. The gradient-direction corner detector is based on the measure of the gradient module of the image gradient direction and the constraints of the false corner response suppression.
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
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.
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.
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.
31566264
Cooperation-Based Behavior Design
Robot soccer explores such a research topic that multiple agents work together in a Real-time, Cooperative and Adversarial (RCA) environment to achieve specific objectives. It requires that each agent can not only deal with situations individually, but also present cooperation with its teammates. In this paper, we describe a robot architecture, which addresses "scaling cooperation" among robots, and meanwhile allows each robot to make decisions independently in real-time case. The architecture is based on "ideal cooperation" principle and implemented for Small Robot League in RoboCup. Experimental results prove its effectiveness and reveal several primary characteristics of behaviors in robot soccer.
794384
A Novel Intrusion Detection Method
It is an important issue for the security of network that how to detect new intrusions attack. This paper investigates unsupervised intrusion detection method. A distance definition for mixed attributes, a simple method calculating cluster radius threshold, a outlier factor measured deviating degree of a cluster, and a novel intrusion detection method are proposed in this paper. The experimental results show that the method has promising performance with high detection rate and low false alarm rate, also can detect new intrusion.
7895439
An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition
In this paper, we present a new shape-coding approach, which decouples the shape information into two independent signal data sets; the skeleton and the boundary distance from the skeleton. The major benefit of this approach is that it allows for a more flexible tradeoff between approximation error and bit budget. Curves of arbitrary order can be utilized for approximating both the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals of all boundaries within a frame, so that the overall distortion is minimized. An operational rate-distortion (ORD) optimal approach using Lagrangian relaxation and a four-dimensional direct acyclic graph (DAG) shortest path algorithm is developed for solving the problem. To reduce the computational complexity from O(N(5)) to O(N(3)), where N is the number of admissible control points for a skeleton, a suboptimal greedy-trellis search algorithm is proposed and compared with the optimal algorithm. In addition, an even more efficient algorithm with computational complexity O(N(2)) that finds an ORD optimal solution using a relaxed distortion criterion is also proposed and compared with the optimal solution. Experimental results demonstrate that our proposed approaches outperform existing ORD optimal approaches, which do not follow the same decomposition of the source data.
123058798
Compact representation of multidimensional data using tensor rank-one decomposition
This paper presents a new approach for representing multidimensional data by a compact number of bases. We consider the multidimensional data as tensors instead of matrices or vectors, and propose a tensor rank-one decomposition (TROD) algorithm by decomposing Nth-order data into a collection of rank-1 tensors based on multilinear algebra. By applying this algorithm to image sequence compression, we obtain much higher quality images with the same compression ratio as principal component analysis (PCA). Experiments with gray-level and color video sequences are used to illustrate the validity of this approach.
611504
Utilizing histogram information
Many commercial database management systems (e.g., DB2, Oracle, etc.) make use of histograms of the value distribution of individual attributes of relations in order to make good selections of query execution plans. These histograms contain partial information about the actual distribution, such as which attribute values occur most frequently, and how often each one occurs, and what value occurs at the kth quantile when the values are sorted.In this paper, we quantitatively assess the information gain (or uncertainty reduction) due to each of these types of histogram information both individually and in combination. Correspondingly, we observe how the accuracy of estimating frequencies of individual values improves with the availability of each of these types of histogram information. We suggest guidelines for constructing histograms tailored to each individual attribute depending on the characteristics of its attribute value distribution.
1498767
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.