14385698
A quantified study of facial asymmetry in 3D faces
With the rapid development of 3D imaging technology, the wide usage of 3D surface information for research and applications is becoming a convenient reality. We focus on a quantified analysis of facial asymmetry of more than 100 3D human faces (individuals). We investigate whether facial asymmetry differs statistically significantly from a bilateral symmetry assumption, and the role of global and local facial asymmetry for gender discrimination.
14419032
Content-based 3D neuroradiologic image retrieval: preliminary results
A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.
5262963
Texture replacement in real images
Texture replacement in real images has many applications, such as interior design, digital movie making and computer graphics. The goal is to replace some specified texture patterns in an image while preserving lighting effects, shadows and occlusions. To achieve convincing replacement results we have to detect texture patterns and estimate the lighting map from a given image. Near regular planar texture patterns are considered in this paper. Given a sample texture patch, a standard tile is computed. Candidate texture regions are determined by mutual information between the standard tile and each image patch. Regions with high mutual information scores are used to estimate the admissible lighting distributions, which is represented by cached statistics. Spatial lighting change constraints are represented by a Markov random field model. Maximum a posteriori estimation of the texture segmentation and lighting map is solved in a stochastic annealing fashion, namely, the Markov chain Monte Carlo method. Visually satisfactory result is achieved using this statistical sampling model.
1966938
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.
1236601
Skewed symmetry groups
We introduce the term skewed symmetry groups and provide a complete theoretical treatment for 2D wallpaper groups under affine transformations. For the first time, a given periodic pattern can be classified not simply by its Euclidean symmetry group but,by its highest "potential" symmetry group under affine deformation. A concise wallpaper group migration map is constructed that separates the 17 affinely deformed wallpaper groups into small, distinct orbits. The practical value of this result includes a novel indexing and retrieval scheme for regular patterns, and a maximal-symmetry-based method for estimating shape and orientation from texture under unknown views.
122978488
Representation and matching of articulated shapes
We consider the problem of localizing the articulated and deformable shape of a walking person in a single view. We represent the non-rigid 2D body contour by a Bayesian graphical model whose nodes correspond to point positions along the contour. The deformability of the model is constrained by learned priors corresponding to two basic mechanisms: local non-rigid deformation, and rotation motion of the joints. Four types of image cues are combined to relate the model configuration to the observed image, including edge gradient map, foreground/background mask, skin color mask, and appearance consistency constraints. The constructed Bayes network is sparse and chain-like, enabling efficient spatial inference through sequential Monte Carlo sampling methods. We evaluate the performance of the model on images taken in cluttered, outdoor scenes. The utility of each image cue is also empirically explored.
9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
10940084
Hamiltonian Graphs with Large Neighborhood Unions
1369726
Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks
While in-network data fusion can reduce data redundancy and, hence, curtail network load, the fusion process itself may introduce significant energy consumption for emerging wireless sensor networks with vectorial data and/or security requirements. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only - fusion cost must also be accounted for. In our prior work, while a randomized algorithm termed MFST is devised toward this end, it assumes that fusion shall be performed at any intersection node whenever data streams encounter. In this paper, we design a novel routing algorithm, called adaptive fusion Steiner tree (AFST), for energy efficient data gathering. Not only does AFST jointly optimize over the costs for both data transmission and fusion, but also AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed at a particular node. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms, including SLT, SPT, and MFST
6564204
A robust method for shape-based 3D model retrieval
Proliferation of 3D models necessitates developing efficient methods for indexing or retrieving the models in a large database. Many previous methods for this purpose defined functions on concentric spheres as approximation of 3D geometry for spherical harmonic transform (SHT). In this paper, we point out that this is not robust as the surface of a model may shift between different shells under perturbation, and multi-layer of surfaces may exist in one shell, making the function definition ambiguous. To solve these problems, we propose a method to characterize 3D shape using delta functions. Then, spherical functions are defined by sampling in the frequency domain of the delta functions for SHT. By doing so, our method can support retrieval with controllable acuity, which benefits wider range of applications and facilitates customization to different users. Experiments have shown that our method is more robust than previous approaches.
903407
On integrating fluid models with packet simulation
Fluid models have been shown to he efficient and accurate in modelling large IP networks. However, unlike packet models, it is difficult to extract packet-level information from them. In this paper, we present a hybrid simulation method that maintains the performance advantage of fluid models while providing detailed packet level information for selected packet traffic flows. We propose two models to account for the interaction between background TCP traffic in a fluid network and foreground packet traffic of interest. The first assumes that the packet traffic poses a negligible load on the fluid network whereas the second accounts for the added load by transforming the packet traffic into fluid flows and solving the resulting enhanced fluid model. The first of these yields an efficient one pass solution algorithm whereas the second requires an additional pass to account for the packet traffic load. We establish the correctness of both approaches and present their implementation within ns-2. Comparisons between the hybrid models and a classical packet simulation show the two pass approach to be quite accurate and computationally efficient.