7238782
Boundary Finding with Prior Shape and Smoothness Models
We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape, and combined models, respectively. Indeed, snakes, modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available.
1746465
Pyramidal search of maximum coherence direction for biomedical image interpolation
A multiresolution technique for biomedical image interpolation is presented. It is an extension of the work on directional coherence interpolation (DCI) - a novel gray-level image interpolation method that interpolates the missing image data along the smoothed maximum coherence directions (MCDs). We propose to apply a pyramidal search strategy for MCD estimation. This coarse-to-fine scheme requires less computation time by starting with the reduced amount of data and propagating searching results to finer resolutions. In addition, it also improves robustness compared with our previous single resolution DCI.
10128966
Detecting image orientation based on low-level visual content
Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.
14524485
Boundary finding with correspondence using statistical shape models
We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.
9203132
Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression
This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected. The novel formulation of the problem allows merging model-driven with data-driven methods, and therefore unifies these two currently separate modes of fMRI analysis. In addition, multiresolution signal analysis is achieved and developed. Other advantages of the approach are: avoidance of interpolation after motion estimation, embedded removal of low-frequency noise components, and easy incorporation of multi-run, multi-subject, and multi-task studies into the framework.
793367
3D Brain surface matching based on geodesics and local geometry
The non-rigid registration of surfaces is a complex and difficult task for which there are many important applications, such as comparing shape between deformable objects and comparing associated function. This paper presents a new approach for brain surface matching by determining the correspondence of 3D point sets between pairs of surfaces. The algorithm is based on shape using a combination of geodesic distance and surface curvature. There are two major procedures involved. An initial sparse set of corresponding points is first generated by matching local geometrical features. Geodesic distance interpolation is then employed hierarchically in order to capture the complex surface. By this scheme, surface correspondence and triangulation are computed simultaneously. Experiments applied to human cerebral cortical surfaces are shown to evaluate the approach. It is shown that the proposed method performs well for both surface matching and surface shape recovery.
17258193
Spatiotemporal video segmentation based on graphical models
This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.
16068554
The SIMBA user alert service architecture for dependable alert delivery
Alerts refer to the delivery of user-subscribed information to the user. As the number of alert services and the types of information delivery devices increase, a new model that allows users to manage alert delivery and avoid alert overflow is needed. The unique dependability challenge in the management of alerts is in the proper use of redundancy to achieve timeliness and reliability without being unduly intrusive or cumbersome. We describe the design, implementation, and user experience of an alert service architecture, called SIMBA. SIMBA utilizes Instant Messaging with acknowledgements as the universal, reliable alert delivery channel, with emails being the fallback channel. All alerts that a user subscribes to are first directed to the user's MyAlertBuddy, which allows centralized delivery preference customization and acts as a personal alert router to protect the privacy of user addresses. Delivery modes, each of which involves multiple user addresses to accommodate communication failures, are supported as an abstraction for specifying personalized dependability levels. A working implementation of the SIMBA system, which integrates five different types of alert services, is described. Challenges and techniques in maintaining a highly available MyAlertBuddy to avoid single-point of failure are discussed. The concept of exception handling automation is introduced for enhancing the robustness of applications that drive third-party communication client software through automation interfaces.
6864548
A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process
This paper revisits the fuzzy logarithmic least squares method (LLSM) in the analytic hierarchy process and points out its incorrectness in the normalization of local fuzzy weights, infeasibility in deriving the local fuzzy weights of a fuzzy comparison matrix when the lower bound value of a non-normalized fuzzy weight turns out to be greater than its upper bound value, uncertainty of local fuzzy weights for incomplete fuzzy comparison matrices, and unreality of global fuzzy weights. A modified fuzzy LLSM, which is formulated as a constrained nonlinear optimization model, is therefore suggested to tackle all these problems. A numerical example is examined to show the applicability of the modified fuzzy LLSM and its advantages.
207640585
On fuzzy multiattribute decision-making models and methods with incomplete preference information
In the paper by Li (Fuzzy Sets and Systems 106 (1999) 113), a minimum average weighted deviation method was investigated to determine the weights of attributes using incomplete preference information. It is shown in this paper that the method is flawed and can only be meaningful if it is revised. Two general ways are presented to revise it and are justified by using Lagrange multiplier method and Hessian matrix. Four specific revised models and approaches are investigated and examined through a numerical example. Different membership functions are suggested and utilized to normalize decision matrix so that a credible ranking order can be achieved.
14088697
Approximate shortest paths in anisotropic regions
Our goal is to find an approximate shortest path for a point robot moving in a planar subdivision with <i>n</i> vertices. Let ρ ≥ 1 be a real number. Distances in each face of this subdivision are measured by a convex distance function whose unit disk is contained in a concentric unit Euclidean disk, and contains a concentric Euclidean disk with radius 1/ρ. Different convex distance functions may be used for different faces, and obstacles are allowed. These convex distance functions may be asymmetric. For all ε ∈ (0, 1), and for any two points <i>v<inf>s</inf></i> and <i>v<inf>d</inf></i>, we give an algorithm that finds a path from <i>v<inf>s</inf></i> to <i>v<inf>d</inf></i> whose cost is at most (1 + ε) times the minimum cost. Our algorithm runs in <i>O</i> (ρ<sub>2</sub>logρ/ε<sup>2</sup>n<sup>3</sup> log (ρn/ε)) time. This bound does not depend on any other parameters; in particular, it does not depend on the minimum angle in the subdivision. We give applications to two special cases that have been considered before: the weighted region problem and motion planning in the presence of uniform flows. For the weighted region problem with weights in [1, ρ] ∪ {∞}, the time bound of our algorithm improves to <i>O</i> (ρ<sub>2</sub>logρ/εn<sup>3</sup> log (ρn/ε)).