258700
Cervical Cancer Detection Using SVM Based Feature Screening
We present a novel feature screening algorithm by deriving relevance measures from the decision boundary of Support Vector Ma- chines. It alleviates the "independence" assumption of traditional screen- ing methods, e.g. those based on Information Gain and Augmented Vari- ance Ratio, without sacrificing computational efficiency. We applied the proposed method to a bottom-up approach for automatic cervical cancer detection in multispectral microscopic thin PAP smear images. An initial set of around 4,000 multispectral texture features is effectively reduced to a computationally manageable size. The experimental results show significant improvements in pixel-level classification accuracy compared to traditional screening methods.
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.
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.
5302961
A Group Theoretic Formalization of Surface Contact
The surface contacts between solids are always associated with a set of symmetries of the contacting surfaces. These symmetries form a group known as the symmetry group of the surface. In this article we develop a group theoretic formalization for describing surface contact between solids. In particular we define (1) primitive and compound features of a solid, (2) a topological characterization of these features, and (3) the symmetry groups of primitive and compound features. The symmetry group of a feature is a descriptor of the fea ture that is at once abstract and quantitative. We show how to use group theory concepts to describe the exact relative motion (position) of solids under surface contacts, which can be either rigid or articulated. The central result of this article is to prove the following: 1. When primitive features of a solid are mutually dis tinct, 1-congruent or 2-congruent, the symmetry group of a compound feature can be expressed in terms of the intersection of the symmetry groups of its primitive features. 2. When two solids have surface contact, their rela tive positions can be expressed as a coset of their common symmetry group, which in turn can be ex pressed in terms of the intersections of the symmetry groups of the primitive features involved in this con tact. These results show that using group theory to formalize surface contacts is a general approach for specifying spatial relationships and forms a sound basis for the automation of robotic task planning. One advantage of this formulation is its ability to express continuous motions between two surface- contacting solids in a computational manner and to avoid combinatorics arising from multiple relationships, especially from discrete symmetries in the assembly parts and their fea tures. At the end of this article, a geometric representation for symmetry groups and an efficient group intersection algorithm using characteristic invariants are described.
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.
848425
Location awareness in unstructured peer-to-peer systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.
39241507
Feature selection using a proximity-index optimization model
Abstract One of the recurring issues in pattern recognition problems has been feature extraction and selection. This paper addresses this issue from a different pespective. Without assuming any particular classificaiton algorithm, it first suggests that one extract as much information as conveniently possible in several pattern-information domains. This paper later suggests applying the proposed Proximity-Index method, to select a significantly smaller, yet optimal feature subset. This method is formally described and is successfully applied to a waveform classification problem. The features selected by the algorithm are used to classify ten signal classes and produce a very encouraging recognition performance of 87.00% on 200 samples. This method is computationally inexpensive and particularly useful for large data set problems.
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.
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.
6989862
Using Prosody for Automatic Sentence Segmentation of Multi-party Meetings
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter) We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR) We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.