41114072
Real-Time 3D Motion Tracking with Known Geometric Models
In this paper a new model-based tracking algorithm is proposed for real-time performance. The matching process includes two aspects of: (1) feature extraction using local minimum energy and (2) global matching of known 3D models against the projected features. The algorithm is robust to change in lighting and background. The small motion hypothesis was used for fitting of feature energy which is defined as the negative absolute value of the edge strength. An autoregressiveAR(1) model is employed for detecting incorrect matches in terms of the feature energy. We have found a new invariance-based method to eliminate false matches caused by strong shadow or occlusion. The invariance is the ratio of trigonometric functions of the angles formed by a polygon. In order to calculate the vertices of the object surface in an image, regression technique in terms of matched features is efficient in our approach. A linear least squares method and the orthonormal rotation matrix are used for motion estimation and pose update of the six degrees of freedom. Also, an Extended Kalman Filter is introduced to guarantee a smooth motion estimation and prediction.
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.
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.
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.
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
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
7713940
ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams
Publisher Summary This chapter implements ATLAS, a powerful database language and system that enables users to develop complete data-intensive applications in structured query language (SQL)—by writing new aggregates and table functions in SQL, rather than in procedural languages as in current Object- Relational systems. As a result, ATLAS' SQL is Turing-complete, and is very suitable for advanced data intensive applications, such as data mining and stream queries. The ATLAS system is now available for download along with a suite of applications including various data mining functions that have been coded in ATLAS' SQL, and execute with a modest performance overhead with respect to the same applications written in C/C++. The demonstration reveals ATLAS' internal architecture by focusing on several of its key components, such as the query rewrite module, the query-plan generation module, and the stream-data management engine.
7789558
Indexing weighted-sequences in large databases
We present an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure where each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence in that each event has a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed enables us to efficiently retrieve from the database all subsequences, possibly noncontiguous, that match a given query sequence both by events and by weights. The index method also takes into consideration the nonuniformfrequency distribution of events in the sequence data. In addition, our method finds a broad range of applications in indexing scientific data consisting of multiple numerical columns for discovery of correlations among these columns. For instance, indexing a DNA microarray that records expression levels of genes under different conditions enables us to search for genes whose responses to various experimental perturbations follow a given pattern. We demonstrate, using real-world data sets, that our method is effective and efficient.
1005847
Color Image Segmentation Using Global Information and Local Homogeneity
This paper proposes a new method of color image segmentation considering both global information and local homogeneity. The method applies the mean shift algorithm in the hue and intensity subspace of HSV. The cyclic property of the hue component is also considered in the proposed method. Experiments on natural color images show promising results.
31489027
A Graph Spectral Approach to Consistent Labelling
In this paper a new formulation of probabilistic relaxation labeling is developed using the theory of diffusion processes on graphs. Our idea is to formulate relaxation labelling as a diffusion process on the vector of object-label probabilities. According to this picture, the label probabilities are given by the state-vector of a continuous time random walk on a support graph. The state-vector is the solution of the heat equation on the support-graph. The nodes of the support graph are the Cartesian product of the object-set and label-set of the relaxation process. The compatibility functions are combined in the weight matrix of the support graph. The solution of the heat-equation is found by exponentiating the eigensystem of the Laplacian matrix for the weighted support graph with time. We demonstrate the new relaxation process on a toy labeling example which has been studied extensively in the early literature, and a feature correspondence matching problem abstracted in terms of relational graphs. The experiments show encouraging labeling and matching results.
15278727
LOCI: Load Shedding through Class-Preserving Data Acquisition
An avalanche of data available in the stream form is overstretching our data analyzing ability. In this paper, we propose a novel load shedding method that enables fast and accurate stream data classification. We transform input data so that its class information concentrates on a few features, and we introduce a progressive classifier that makes prediction with partial input. We take advantage of stream data's temporal locality -for example, readings from a temperature sensor usually do not change dramatically over a short period of time -for load shedding. We first show that temporal locality of the original data is preserved by our transform, then we utilize positive and negative knowledge about the data (which is of much smaller size than the data itself) for classification. We employ both analytical and empirical analysis to demonstrate the advantage of our approach.