579951
Improving performance of distribution tracking through background mismatch
This paper proposes a new density matching method based on background mismatching for tracking of nonrigid moving objects. The new tracking method extends the idea behind the original density-matching tracker, which tracks an object by finding a contour in which the photometric density sampled from the enclosed region most closely matches a model density. This method can be quite sensitive to the initial curve placements and model density. The new method eliminates these sensitivities by adding a second term to the optimization: the mismatch between the model density and the density sampled from the background. By maximizing this term, the tracking algorithm becomes significantly more robust in practice. Furthermore, we show the enhanced ability of the algorithm to deal with target objects, which possess smooth or diffuse boundaries. The tracker is in the form of a partial differential equation, and is implemented using the level-set framework. Experiments on synthesized images and real video sequences show our proposed methods are effective and robust; the results are compared with several existing methods.
2269279
Model-based segmentation of medical imagery by matching distributions
The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.
10193683
Using Branch Correlation to Identify Infeasible Paths for Anomaly Detection
In this paper, we propose a system called infeasible path detection system (IPDS) to combat memory tampering attacks causing invalid program control flows. In our system, the compiler analyzes correlations between branches and then the analyzed information is conveyed to the runtime system. The runtime system detects dynamic infeasible program paths by combining compiler determined information with runtime information to check the legality of the path taken during execution. IPDS achieves a zero false positive rate and can detect a high percentage of memory tampering for many attacks in which the tampering actually causes a change in control flow. Moreover, IPDS only incurs a modest amount of hardware resource and negligible performance penalty
17846455
Systematic Design of Original and Modified Mastrovito Multipliers for General Irreducible Polynomials
This paper considers the design of bit-parallel dedicated finite field multipliers using standard basis. An explicit algorithm is proposed for efficient construction of Mastrovito product matrix, based on which we present a systematic design of Mastrovito multiplier applicable to GF(2/sup m/) generated by an arbitrary irreducible polynomial. This design effectively exploits the spatial correlation of elements in Mastrovito product matrix to reduce the complexity. Using a similar methodology, we propose a systematic design of modified Mastrovito multiplier, which is suitable for GF(2/sup m/) generated by high-Hamming weight irreducible polynomials. For both original and modified Mastrovito multipliers, the developed multiplier architectures are highly modular, which is desirable for VLSI hardware implementation. Applying the proposed algorithm and design approach, we study the Mastrovito multipliers for several special irreducible polynomials, such as trinomial and equally-spaced-polynomial, and the obtained complexity results match the best known results. Moreover, we have discovered several new special irreducible polynomials which also lead to low-complexity Mastrovito multipliers.
206626032
Association Rules
New association rules are presented for measure of association relationships between patterns. The new association rules are shown to not only measure three well-known association relationships correctly, but also satisfy other criteria for correct measure of association. Comparison with other measures is discussed both theoretically and experimentally. Applications in supervised mining of association rules and in pattern-driven multidimensional pattern analysis are presented.
2269011
Compiler optimizations to reduce security overhead
In this work, we present several compiler optimizations to reduce the overhead due to software protection. We first propose an aggressive rematerialization algorithm which attempts to maximally realize non-trusted values from other trusted values thereby avoiding the security cost for those non-trusted values. We further propose a compiler technique to utilize the secure storage in our machine model efficiently. To optimize the security cost on data that has to be stored in non-trusted storage, we propose a data grouping technique. Security operations can be performed over the group of data instead of over each piece separately. We show an interesting application of the data grouping technique to reduce the security cost. We test the effectiveness of our optimizations on a recently proposed software protection scheme that involves large overhead. Our results show that the above optimizations are effective and reduce the security overhead significantly.
18335624
Static Techniques to Improve Power Efficiency of Branch Predictors
In this paper, we illustrate the application of two static techniques to reduce the activities of the branch predictor in a processor leading to its significant power reduction We introduce the use of a static branch target buffer (BTB) that achieves the similar performance to the traditional branch target buffer but which eliminates most of the state updates thus reducing the power consumption of the BTB significantly We also introduce a correlation-based static prediction scheme into a dynamic branch predictor so that those branches that can be predicted statically or can be correlated to the previous ones will not go through normal prediction algorithm This reduces the activities and conflicts in the branch history table (BHT) With these optimizations, the activities and conflicts of the BTB and BHT are reduced significantly and we are able to achieve a significant reduction (43.9% on average) in power consumption of the BPU without degradation in the performance.