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.
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.
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
18351320
Spatial data query support in peer-to-peer systems
The distributed hash table (DHT) mechanisms have been proposed to manage data in very large, structured peer-to-peer (P2P) systems. DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and are hence extremely scalable. However, range queries - which are very common with spatial data - cannot be executed efficiently in these systems because the adoption of uniform hash functions to ensure excellent load balancing unfortunately destroys any existing data locality. In this report we propose a novel technique to preserve spatial locality information while also keeping some of the load balancing properties of DHT based systems. We describe our design as an extension of content-addressable networks (CAN) and illustrate the feasibility of supporting spatial range queries.
13939828
Mutual information regularized Bayesian framework for multiple image restoration
Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
1582462
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distribution. This poses a serious challenge. On the one hand, relying on historical data may increase the chances of learning obsolete models. On the other hand, learning only from the latest data may lead to biased classifiers, as the latest data is often an unrepresentative sample of the current class distribution. The problem is particularly acute in classifying rare events, when, for example, instances of the rare class do not even show up in the most recent training data. In this paper, we use a stochastic model to describe the concept shifting patterns and formulate this problem as an optimization one: from the historical and the current training data that we have observed, find the most-likely current distribution, and learn a classifier based on the most-likely distribution. We derive an analytic solution and approximate this solution with an efficient algorithm, which calibrates the influence of historical data carefully to create an accurate classifier. We evaluate our algorithm with both synthetic and real-world datasets. Our results show that our algorithm produces accurate and efficient classification.
14876394
User Defined Aggregates for Logical Data Languages
A new wave of data-intensive and knowledge-based applications|such as data mining and decision support|require the introduction of complex application-speciic aggregate functions. In this paper, we propose extensions for deductive database systems to support these new applications. We develop constructs, formal semantics, and implementation techniques for user-deened aggregates, and describe their realization in an extended LDL++ system recently built at UCLA. With these extensions, the system can support on-line aggregation, roll-ups for data cubing, temporal aggregates for time-series, iceberg queries, and other recently proposed operators used in decision support and data mining procedures. We then discuss the application of this technology to other DBMSs, and in particular to the SQL3 speciications that support the notion of user-deened aggregates. We show that SQL3 suuers from limitations that severely restrict its use in new applications; thus we propose simple extensions similar to those used for LDL++ to overcome such limitations.
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.