24960929
3D Image Segmentation of Deformable Objects with Shape-Appearance Joint Prior Models
We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of the object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation model using the joint prior information of the shape and image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define joint probability distribution over the variations of object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-appearance model in terms of level set function as opposed to landmark points of the shape. We found the algorithm to be robust to noise, able to handle multidimensional data, and avoiding the need for point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
2345378
Segmentation of 3D deformable objects with level set based prior models
We propose a level set based deformable model for the segmentation of multiple objects from 3D medical images using shape prior constraints. As an extension to the level set distribution model of object shape presented in previous papers, this paper evaluates the performance of the level set representation of the object shape by comparing it with the point distribution model(PDM) using the Chi-square test. We define a maximum a posteriori (MAP) estimation model using level set based prior information to realize the segmentation of the multiple objects. To achieve this , only one level set function is employed as the representation of the multiple objects of interest within the image. We then define the probability distribution over the variations of objects contained in a set of training images. We found the algorithm to be computationally efficient, robust to noise, able to handle multidimensional data, and avoids the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
17586231
Non-rigid Image Registration Using Geometric Features and Local Salient Region Features
We present a novel feature-based non-rigid image registration algorithm using a small number of automatically extracted points and their associated local salient region features. Our automatic registration is a hybrid approach co-optimizing point-based and image-based terms. Motivated by the paradigm of the TPS-RPM algorithm [6], we develop the RHDM (Robust Hybrid Deformable Matching) algorithm by alternatively optimizing correspondences and transformations for registration. The local salient region features and the geometric features, together with the softassign and deterministic annealing techniques, are used for solving correspondences. Thin-plate splines are used for generating a smooth non-rigid spatial transformation. Our algorithm is built to be extremely robust to feature extraction errors. A new dynamic outlier rejection mechanism is described for rejecting outliers and generating accurate spatial mappings. A local refinement technique is used for correcting non-exactly matched correspondences arising from image noise and irregular deformations. In contrast with the TPS-RPM algorithm, which can handle only outliers in one point set, our algorithm is able to handle a considerable number of outliers in both point sets. The experimental results demonstrate the robustness and accuracy of our algorithm.
388734
A statistical signal processing approach to image fusion for concealed weapon detection
A statistical signal processing approach to multisensor image fusion is presented for concealed weapon detection (CWD). This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the model parameters and the fused image. We demonstrate the efficiency of this approach by applying this method to fusion of visual and non-visual images with emphasis on CWD applications.
29686713
A note on Ramsey numbers with two parameters
The Ramsey number R(G1, G2) is the smallest integer p such that for any graph G on p vertices either G contains G1 or G- contains G2, where G- denotes the complement of G. In this paper, some new bounds with two parameters for the Ramsey number R(G1, G2), under some assumptions, are obtained. Especially, we prove that R(K6 - e, K6) ≤ 116 and R(K6 - e, K7) ≤ 202, these improve the two upper bounds for the classical Ramsey number in [S.P. Radziszowski, Small Ramsey number, Electron. J. Combin. DSI (2002) 1-36].
11778214
An Evolutionary Algorithm for Synthesizing Optical Thin-Film Designs
This paper presents an evolutionary approach, called Family Competition Evolutionary Algorithm (FCEA), to design optical thin-film multilayer systems. FCEA integrates self-adaptive mutations, decreasing-based mutations, and four-layer selections to balance exploration and exploitation. One antireflection coating and one narrow-band rejection filter are presented to demonstrate that our approach is a powerful technique. Our approach consistently performs better than other evolutionary algorithms and other published results on these two problems. From experimental results of antireflection coating, our optimal solutions exhibit a pronounced semiperiodic clustering of layers and these solutions also confirm the theoretical prediction between the optical thickness and the best achievable reflectance.
252049
MECA: an extensible, expressive system and language for statically checking security properties
This paper describes a system and annotation language, MECA, for checking security rules. MECA is expressive and designed for checking real systems. It provides a variety of practical constructs to effectively annotate large bodies of code. For example, it allows programmers to write programmatic annotators that automatically annotate large bodies of source code. As another example, it lets programmers use general predicates to determine if an annotation is applied; we have used this ability to easily handle kernel backdoors and other false-positive inducing constructs. Once code is annotated, MECA propagates annotations aggressively, allowing a single manual annotation to derive many additional annotations (e.g., over one hundred in our experiments) freeing programmers from the heavy manual effort required by most past systems.MECA is effective. Our most thorough case study was a user-pointer checker that used 75 annotations to check thousands of declarations in millions of lines of code in the Linux system. It found over forty errors, many of which were serious, while only having eight false positives.