Visual surveillance in outdoor environments requires the monitoring of both objects and events. The analysis is generally driven by the target application which, in turn, determines the set of relevant events and obje...
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By the development of network applications, network security issues are getting more and more important. This paper proposes a multiple-pattern matching algorithm for the network intrusion detection systems based on t...
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ISBN:
(纸本)9781424442331
By the development of network applications, network security issues are getting more and more important. This paper proposes a multiple-pattern matching algorithm for the network intrusion detection systems based on the GPU (Graphics Processing Units). The highly parallelism of the GPU computation power is used to inspect the packet content in parallel. The performance of the proposed approach is analyzed through evaluations such as using various texture formats and different implementations. Experimental results indicate that the performance of the proposed approach is twice of that of the modified Wu-Manber algorithm used in Snort. The proposed approach makes a commodity and cheap GPU card as a high performance pattern matching co-processor.
Feature extraction is a major step in all patternrecognition and image processing applications. Conventional feature extraction methods when used for extracting physical quantities like mean, entropy etc. are not sui...
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Feature extraction is a major step in all patternrecognition and image processing applications. Conventional feature extraction methods when used for extracting physical quantities like mean, entropy etc. are not suitable for automation due to complexity of the feature extraction process. In this paper we propose a simple and novel feature extraction technique that decomposes the original image into a series of sparse images using a time varying selection criterion on the spatial plane. Features are then extracted from each of these sparse images. The feature set, when carefully analyzed and interpreted, is seen to perform as well or even better than their conventional counterparts for recognition and classification. The technique is demonstrated to be robust against noise and results in highly discriminatory features. Also, in this paper the technique to obtain shift invariant features is proposed.
The variability of tensor fields is usually analyzed with multivariate statistical distributions. Multivariate distributions model every component of the tensor, which are not invariant under rotation. They therefore ...
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The variability of tensor fields is usually analyzed with multivariate statistical distributions. Multivariate distributions model every component of the tensor, which are not invariant under rotation. They therefore tell very little information about the true shape of the tensor. A statistical analysis on the eigenvalues of the tensor would be more revealing. The eigenvalues determine if a tensor is uniaxial, i.e.: only one eigenvalue is different from zero, isotropic, volume preserving or purely anisotropic. However, the eigenvalues of a normally distributed tensor are not, in general, normally distributed. In this paper, we solve this problem directly for small sizes of samples by determining the probability that the maximum error is within a reasonable bound. When the error is likely to be within a reasonable bound, we consider the eigenvalues of a tensor to be normally distributed along a mean eigendirection. Monte Carlo simulation shows that the computed bound is tight and becomes tighter when the number of sample increases. An application of the method, analysis of deformations on the cortical surface, is presented in this paper. On this data, we found that 80% of the anisotropic deformations could be analyzed by modeling the eigenvalues directly. Thus, the proposed method allows formulating statistical hypothesis directly on eigenvalues in many cases of measured deformations. Although the method was used in only one application, the method could be extended to application involving diffusion MRI or other imaging technique involving tensors.
Processing a video stream to segment foreground objects from the background is a critical first step in many computervision applications. Background subtraction (BGS) is a commonly used technique for achieving this s...
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Most algorithms for real-time tracking of deformable shapes provide sub-optimal solutions for a suitable energy minimization task: The search space is typically considered too large to allow for globally optimal solut...
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Most algorithms for real-time tracking of deformable shapes provide sub-optimal solutions for a suitable energy minimization task: The search space is typically considered too large to allow for globally optimal solutions. In this paper we show that - under reasonable constraints on the object motion - one can guarantee global optimality while maintaining real-time requirements. The problem is cast as finding the optimal cycle in a graph spanned by the prior template and the image. The underlying combinatorial algorithm is implemented on state-of-the-art graphics hardware. Solutions on FPGAs are conceivable. Experimental results demonstrate long-term tracking of cars in real-time, while coping with challenging weather conditions. In particular, we show that the proposed tracking algorithm is highly robust to illumination changes and that it outperforms local tracking methods such as the level set method.
In this paper, we focus on the number of solution for the Perspective-Three-point problem (P3P) in some geometrical constraints of 3 points, which is a common problem in applied mathematics and computervision. We use...
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ISBN:
(纸本)9783540874409
In this paper, we focus on the number of solution for the Perspective-Three-point problem (P3P) in some geometrical constraints of 3 points, which is a common problem in applied mathematics and computervision. We use Wu's zero decomposition method to find a complete triangular decomposition of a practical configuration for the P3P problem. By the Wu's method, we also obtain some sufficient conditions under which there are multi-solution for the P3P problem.
One of the major problems remaining in tracking is occlusion handling. This paper presents a system for exactly this. A human model is defined and each body part is represented by a number of features. For each new im...
While global methods for matching shapes to images have recently been proposed, so far research has focused on small deformations of a fixed template. In this paper we present the first global method able to pixel-acc...
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While global methods for matching shapes to images have recently been proposed, so far research has focused on small deformations of a fixed template. In this paper we present the first global method able to pixel-accurately match non-rigidly deformable shapes across images at amenable run-times. By finding cycles of optimal ratio in a four-dimensional graph - spanned by the image, the prior shape and a set of rotation angles - we simultaneously compute a segmentation of the image plane, a matching of points on the template to points on the segmenting boundary, and a decomposition of the template into a set of deformable parts. In particular, the interpretation of the shape template as a collection of an a priori unknown number of deformable parts - an important aspect of higher-level shape representations - emerges as a byproduct of our matching algorithm. On real-world data of running people and walking animals, we demonstrate that the proposed method can match strongly deformed shapes, even in cases where simple shape measures and optic flow methods fail.
Network-on-Chip (NoC) is a precious approach to handle huge number of transistors by virtue of technology scaling to lower than 50nm. Virtual channels have been introduced in order to improve the performance according...
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ISBN:
(纸本)9781424442331
Network-on-Chip (NoC) is a precious approach to handle huge number of transistors by virtue of technology scaling to lower than 50nm. Virtual channels have been introduced in order to improve the performance according to a timing multiplexing concept in each physical channel. The incremental effect of virtual channels on power consumption has been shown in literatures. The issue of power saving has always been controversial to many designers. In this paper, we introduce a new technique which tries to adaptively mange the number of virtual channels in order to reduce the power consumption while not degrading the performance of the network without any reconfiguration. Our experimental results show the efficiency of our method in a torus topology under different traffic models and Duato routing algorithm with 49% and 30% power saving in the best and worst conditions, respectively.
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