8716970
Active contours for tracking distributions
A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments.
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.
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.
5468472
HIDE: an infrastructure for efficiently protecting information leakage on the address bus
XOM-based secure processor has recently been introduced as a mechanism to provide copy and tamper resistant execution. XOM provides support for encryption/decryption and integrity checking. However, neither XOM nor any other current approach adequately addresses the problem of information leakage via the address bus. This paper shows that without address bus protection, the XOM model is severely crippled. Two realistic attacks are shown and experiments show that 70% of the code might be cracked and sensitive data might be exposed leading to serious security breaches.Although the problem of address bus leakage has been widely acknowledged both in industry and academia, no practical solution has ever been proposed that can provide an adequate security guarantee. The main reason is that the problem is very difficult to solve in practice due to severe performance degradation which accompanies most of the solutions. This paper presents an infrastructure called HIDE (Hardware-support for leakage-Immune Dynamic Execution) which provides a solution consisting of chunk-level protection with hardware support and a flexible interface which can be orchestrated through the proposed compiler optimization and user specifications that allow utilizing underlying hardware solution more efficiently to provide better security guarantees.Our results show that protecting both data and code with a high level of security guarantee is possible with negligible performance penalty (1.3% slowdown).
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.
13956689
The Decidability of the First-Order Theory of Knuth-Bendix Order
Two kinds of orderings are widely used in term rewriting and theorem proving, namely recursive path ordering (RPO) and Knuth-Bendix ordering (KBO). They provide powerful tools to prove the termination of rewriting systems. They are also applied in ordered resolution to prune the search space without compromising refutational completeness. Solving ordering constraints is therefore essential to the successful application of ordered rewriting and ordered resolution. Besides the needs for decision procedures for quantifier-free theories, situations arise in constrained deduction where the truth value of quantified formulas must be decided. Unfortunately, the full first-order theory of recursive path orderings is undecidable. This leaves an open question whether the first-order theory of KBO is decidable. In this paper, we give a positive answer to this question using quantifier elimination. In fact, we shall show the decidability of a theory that is more expressive than the theory of KBO.
7781593
Kernel based 2D symmetrical principal component analysis for face classification
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.
1373345
Leakage-proof program partitioning
Due to limited available memory (of the order of Kilobytes) on embedded devices (such as smart cards), we undertake an approach of partitioning a whole program. The program partitions are down-loaded from the server on demand into the embedded device just before execution. We devise a novel method of partitioning the code and data of the program such that no information regarding the control flow and behavior of the program is leaked out. In other words, by observing the program partitions that are down-loaded from the server, one is unable to guess which branches are taken in the program. The partitioning scheme generates minimal safe program regions which do not reveal the control flow information when transmitted on demand but any further partitioning of which will reveal such an information. Minimal safe regions allow minimal usage of memory on the embedded device and thus enable maximal number of multiple applications to simultaneously reside on the embedded device. The minimal safe regions can be merged on demand to create larger safe regions to improve runtime efficiency. The larger the number of regions to be downloaded on the device, the lower is the performance of the application and vice-versa. Our approach handles this tradeoff between the number of simultaneous applications that would reside on the embedded device and the performance of each application. This approach is scalable both to the number of simultaneous applications and to the amount of memory available. The worst case performance degradation is shown to be below 10% with the benefit of allowing arbitrarily large/multiple applications to be deployed.