9599152
A classification based similarity metric for 3D image retrieval
We present a principled method of obtaining a weighted similarity metric for 3D image retrieval, firmly rooted in Bayes decision theory. The basic idea is to determine a set of most discriminative features by evaluating how well they perform on the task of classifying images according to predefined semantic categories. We propose this indirect method as a rigorous way to solve the difficult feature selection problem that comes up in most content based image retrieval tasks. The method is applied to normal and pathological neuroradiological CT images, where we take advantage of the fact that normal human brains present an approximate bilateral symmetry which is often absent in pathological brains. The quantitative evaluation of the retrieval system shows promising results.
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.
13629000
A Group Theoretical Formalization of Contact Motion
The work reported in this paper applies group theory to characterize local surface contact among solids, and makes such a formalization computationally tractable. This paper serves three purposes: (1) to report a new treatment of contacting surfaces as oriented sets and to verify the consistency of their corresponding symmetry groups in a group theoretical formalization framework; (2) to give a concise summary of this group theoretical approach, from theory, to algorithms, to applications; (3) to pinpoint some unsolved problems and possible future directions.
5285595
Periodic Pattern Analysis under Affine Distortions Using Wallpaper Groups
In this paper, the mathematical theory of wallpaper groups is used to construct a computational tool for symmetry analysis of periodic patterns. Starting with a novel peak detection algorithm based on “regions of dominance”, an input periodic pattern can be automatically classified into one of the 17 wallpaper groups. The orbits of stabilizer subgroups within the group lead to a small set of candidate motifs that exhibit local symmetry consistent with the global symmetry of the entire pattern. We further consider affine distorted periodic patterns and show that each such pattern can be classified into a small set of symmetry groups that describe the patterns’ potential symmetries under affine transformation.
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.
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.
1532290
Scalable fluid models and simulations for large-scale IP networks
In this article we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of Transport Control Protocol (TCP) flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths and packet loss/mark probabilities of AQM routers, and average end-to-end throughput and latencies of TCP users. We model different versions of TCP as well as different implementations of RED Random Early Detection (RED), the most popular AQM scheme currently in use. Comparisons between the models and ns simulation show our models to be quite accurate while at the same time requiring substantially less time to solve than packet level simulations, especially when workloads and bandwidths are high.
18353926
Inverse validation for accurate range image registration with structured data
Automatic range image registration is a fundamental yet extremely difficult problem in machine vision. In this paper, we extend a promising registration algorithm, the geometric iterative closest point (GICP), for structured image data. The extended algorithm is based on a relaxation method combining the motion estimation results from the possible correspondences established by the GICP algorithm and those that are further evaluated by an inverse validation procedure. A comparative study based on real images has shown that the extended algorithm is more accurate and robust than the original algorithm.
17761777
Risk-Sensitive Planning with One-Switch Utility Functions: Value Iteration
Decision-theoretic planning with nonlinear utility functions is important since decision makers are often risk-sensitive in high-stake planning situations. One-switch utility functions are an important class of nonlinear utility functions that can model decision makers whose decisions change with their wealth level. We study how to maximize the expected utility of a Markov decision problem for a given one-switch utility function, which is difficult since the resulting planning problem is not decomposable. We first study an approach that augments the states of the Markov decision problem with the wealth level. The properties of the resulting infinite Markov decision problem then allow us to generalize the standard risk-neutral version of value iteration from manipulating values to manipulating functions that map wealth levels to values. We use a probabilistic blocks-world example to demonstrate that the resulting risk-sensitive version of value iteration is practical.
15266073
Improving ICP with easy implementation for free-form surface matching
Automatic range image registration and matching is an attractive but unresolved problem in both the machine vision and pattern recognition literature. Since automatic range image registration and matching is inherently a very difficult problem, the algorithms developed are becoming more and more complicated. In this paper, we propose a novel practical algorithm for automatic free-form surface matching. This method directly manipulates the possible point matches established by the traditional ICP criterion based on both the collinearity and closeness constraints without any feature extraction, image pre-processing, or motion estimation from outliers corrupted data. A comparative study based on a large number of real range images has shown the accuracy and robustness of the novel algorithm.
5645902
On designing incentive-compatible routing and forwarding protocols in wireless ad-hoc networks: an integrated approach using game theoretical and cryptographic techniques
In many applications, wireless ad-hoc networks are formed by devices belonging to independent users. Therefore, a challenging problem is how to provide incentives to stimulate cooperation. In this paper, we study ad-hoc games---the routing and packet forwarding games in wireless ad-hoc networks. Unlike previous work which focuses either on routing or on forwarding, this paper investigates both routing and forwarding. We first uncover an impossibility result---there does not exist a protocol such that following the protocol to always forward others' traffic is a dominant action. Then we define a novel solution concept called cooperation-optimal protocols. We present Corsac, a cooperation-optimal protocol consisting of a routing protocol and a forwarding protocol. The routing protocol of Corsac integrates VCG with a novel cryptographic technique to address the challenge in wireless ad-hoc networks that a link's cost (ie, its type) is determined by two nodes together. Corsac also applies efficient cryptographic techniques to design a forwarding protocol to enforce the routing decision, such that fulfilling the routing decision is the optimal action of each node in the sense that it brings the maximum utility to the node. Additionally, we extend our framework to a practical radio propagation model where a transmission is successful with a probability. We evaluate our protocols using simulations. Our evaluations demonstrate that our protocols provide incentives for nodes to forward packets.