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.
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.
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
29758508
Corner Detection Based on Finding Edge Points Locally
34060618
A Minimum Boundary Error Framework for Automatic Phonetic Segmentation
This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.
1927942
Tracking and segmenting people with occlusions by a sample consensus based method
One of the most difficult issues in visual tracking is to track people in groups, especially under occlusions. In this paper, we present a novel sample consensus based method, which utilizes both color and spatial information of human bodies, to model the appearance of people. We use this appearance model to segment and track people through occlusions. We show experimental results in several video sequences to validate the effectiveness of the proposed 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.
1424223
Computing Compressed Multidimensional Skyline Cubes Efficiently
Recently, the skyline computation and analysis have been extended from one single full space to multidimensional subspaces, which can lead to valuable insights in some applications. Particularly, compressed skyline cubes in the form of skyline groups and their decisive subspaces provide a succinct summarization and compression of multidimensional subspace skylines. However, computing skyline cubes remains a challenging task since the existing methods have to search an exponential number of nonempty subspaces for subspace skylines. In this paper, we propose a novel and efficient method, Stellar, which exploits an interesting skyline group lattice on a small subset of objects which are in the skyline of the full space. We show that this skyline group lattice is easy to compute and can be extended to the skyline group lattice on all objects. After computing the skyline in the full space, Stellar only needs to enumerate skyline groups and their decisive subspaces using the full space skyline objects. Avoiding searching for skylines in an exponential number of subspaces improves the efficiency and the scalability of subspace skyline computation substantially in practice. An extensive performance study verifies the merits of our new method.
44354759
Binary trees with the largest number of subtrees
This paper characterizes binary trees with n leaves, which have the greatest number of subtrees. These binary trees coincide with those which were shown by Fischermann et al. [Wiener index versus maximum degree in trees, Discrete Appl. Math. 122(1-3) (2002) 127-137] and Jelen and Triesch [Superdominance order and distance of trees with bounded maximum degree, Discrete Appl. Math. 125 (2-3) (2003) 225-233] to minimize the Wiener index.