The brain functional connectivity network is complex, generally constructed using correlations between the regions of interest (ROIs) in the brain, corresponding to a parcellation atlas. The brain is known to exhibit ...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
The brain functional connectivity network is complex, generally constructed using correlations between the regions of interest (ROIs) in the brain, corresponding to a parcellation atlas. The brain is known to exhibit a modular organization, referred to as "functional segregation." Generally, functional segregation is extracted from edge-filtered, and optionally, binarized network using community detection and clustering algorithms. Here, we propose the novel use of exploratory factor analysis (EFA) on the correlation matrix for extracting functional segregation, to avoid sparsifying the network by using a threshold for edge filtering. However, the direct usability of EFA is limited, owing to its inherent issues of replication, reliability, and generalizability. In order to avoid finding an optimal number of factors for EFA, we propose a multiscale approach using EFA for node-partitioning, and use consensus to aggregate the results of EFA across different scales. We define an appropriate scale, and discuss the influence of the "interval of scales" in the performance of our multiscale EFA. We compare our results with the state-of-the-art in our case study. Overall, we find that the multiscale consensus method using EFA performs at par with the *** relevance: Extracting modular brain regions allows practitioners to study spontaneous brain activity at resting state.
Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face...
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ISBN:
(纸本)9789898565471
Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image. In this paper we describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this, we first detect artifacts based on the statistical difference between the target image and a set of pre-processed images in the database. Next, we eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments. We illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques.
作者：
Varun JainHao ZhangGraphics
Usability and Visualization (GrUVi) Laboratory School of Computing Science Simon Fraser University Burnaby BC Canada
We present an algorithm for finding a meaningful vertex-to-vertex correspondence between two 3D shapes given as triangle meshes. Our algorithm operates on embeddings of the two shapes in the spectral domain so as to n...
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We present an algorithm for finding a meaningful vertex-to-vertex correspondence between two 3D shapes given as triangle meshes. Our algorithm operates on embeddings of the two shapes in the spectral domain so as to normalize them with respect to uniform scaling and rigid-body transformation. Invariance to shape bending is achieved by relying on geodesic point proximities on a mesh to capture its shape. To deal with stretching, we propose to use non-rigid alignment via thin-plate splines in the spectral domain. This is combined with a refinement step based on the geodesic proximities to improve dense correspondence. We show empirically that our algorithm outperforms previous spectral methods, as well as schemes that compute correspondence in the spatial domain via non-rigid iterative closest points or the use of local shape descriptors, e.g., 3D shape context
Direct volume rendering of scalar fields uses a transfer function to map locally measured data properties to opacities and colors. The domain of the transfer function is typically the one-dimensional space of scalar d...
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Direct volume rendering of scalar fields uses a transfer function to map locally measured data properties to opacities and colors. The domain of the transfer function is typically the one-dimensional space of scalar data values. This paper advances the use of curvature information in multi-dimensional transfer functions, with a methodology for computing high-quality curvature measurements. The proposed methodology combines an implicit formulation of curvature with convolution-based reconstruction of the field. We give concrete guidelines for implementing the methodology, and illustrate the importance of choosing accurate filters for computing derivatives with convolution. Curvature-based transfer functions are shown to extend the expressivity and utility of volume rendering through contributions in three different application areas: nonphotorealistic volume rendering, surface smoothing via anisotropic diffusion, and visualization of isosurface uncertainty.
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