16102435
Spatial Intensity Correction of Fluorescent Confocal Laser Scanning Microscope Images
Fluorescent confocal laser scanning microscope (CLSM) imaging has become popular in medical domain for the purpose of 3D information extraction. 3D information is extracted either by visual inspection or by automated techniques. Nonetheless, 3D information extraction from CLSM suffers from significant lateral intensity heterogeneity. We propose a novel lateral intensity heterogeneity correction technique to improve accurate image analysis, e.g., quantitative analysis, segmentation, or visualization. The proposed technique is novel in terms of its design (spatially adaptive mean-weight filtering) and application (CLSM), as well as its properties and full automation. The key properties of the intensity correction techniques include adjustment of intensity heterogeneity, preservation of fine structural details, and enhancement of image contrast. The full automation is achieved by data-driven parameter optimization and introduction of several evaluation metrics. We evaluated the performance by comparing with three other techniques, four quality metrics, and two realistic synthetic images and one real CLSM image.
852427
Multisensor raster and vector data fusion based on uncertainty modeling
We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and transformed multisensor measurements. The proposed uncertainty model includes errors due to (1) each sensor by itself, e.g., sensor noise; (2) transformations of measured values to obtain comparable physical entities for data fusion and/or to calibrate sensor measurements; (3) vector data spatial interpolation that is needed to match different spatial resolutions of multisensor data; and (4) temporal interpolation that has to take place if multisensor acquisitions are not accurately synchronized. The proposed methodology was tested using simulated data with varying (a) amount of sensor noise, (b) spatial offset of point sensors generating vector data, and (c) model complexity of the underlying physical phenomenon. We demonstrated the multisensor fusion approach with a data set from a structural health monitoring application domain.
650244
IC-Colorings and IC-Indices of graphs
Given a coloring f:V(G)->N of graph G and any subgraph H@?G we define f"s(H)=@?"v"@?"V"("H")f(v). In particular, we denote f"s(G) by S(f). The coloring f is called an IC-coloring if for any integer k@?[1,S(f)] there is a connected subgraph H@?G such that f"s(H)=k. Also, we define the IC-index of G to beM(G)=max{S(f):f is an IC-coloring of G}.In this paper we examine some well-known classes of graphs and determine their IC-indices. In addition, several conjectures are proposed.
14063599
Light field morphing using 2D features
We present a 2D feature-based technique for morphing 3D objects represented by light fields. Existing light field morphing methods require the user to specify corresponding 3D feature elements to guide morph computation. Since slight errors in 3D specification can lead to significant morphing artifacts, we propose a scheme based on 2D feature elements that is less sensitive to imprecise marking of features. First, 2D features are specified by the user in a number of key views in the source and target light fields. Then the two light fields are warped view by view as guided by the corresponding 2D features. Finally, the two warped light fields are blended together to yield the desired light field morph. Two key issues in light field morphing are feature specification and warping of light field rays. For feature specification, we introduce a user interface for delineating 2D features in key views of a light field, which are automatically interpolated to other views. For ray warping, we describe a 2D technique that accounts for visibility changes and present a comparison to the ideal morphing of light fields. Light field morphing based on 2D features makes it simple to incorporate previous image morphing techniques such as nonuniform blending, as well as to morph between an image and a light field.
13467926
Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions
Uniform cubic B-spline functions have been used for mapping functions in various areas such as image warping and morphing, 3D deformation, and volume morphing. The injectivity (one-to-one property) of a mapping function is crucial to obtaining desirable results in these areas. This paper considers the injectivity conditions of 2D and 3D uniform cubic B-spline functions. We propose a geometric interpretation of the injectivity of a uniform cubic B-spline function, with which 2D and 3D cases can be handled in a similar way. Based on our geometric interpretation, we present sufficient conditions for injectivity which are represented in terms of control point displacements. These sufficient conditions can be easily tested and will be useful in guaranteeing the injectivity of mapping functions in application areas.
15496995
Real-time pencil rendering
This paper presents a real-time technique for rendering 3D meshes in the pencil drawing style. We analyze the characteristics of pencil drawing and incorporate them into the rendering process, which is fully implemented on a GPU. For object contours, we propose a multiple contour drawing technique that imitates trial-and-errors of human in contour drawing. For interior shading, we present a simple approach for mapping oriented textures onto an object surface. To improve the quality of pencil rendering, we generate and map pencil textures that reflect the properties of graphite pencils and paper. We show several rendering examples that demonstrate the high performance of the proposed technique in terms of speed and quality.
14142156
Out-of-Vocabulary Rejection based on Selective Attention Model
Based on a psychological selective attention theory a new algorithm is developed to provide reliable out-of-vocabulary (OOV) rejection for speech recognition systems in noisy environments. The developed attention model is based on Broadbent's ‘early filtering’ theory, and the attention adaptation process utilizes a gradient-descent error minimization algorithm with error backpropagation rule. The developed model is applied to isolated-word recognition tasks, and much higher in-vocabulary recognition rates are achieved with the same out-of-vocabulary rejection rates.