5987381
Algorithm performance contest
This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms.
5267319
Volumetric harmonic brain mapping
We developed two different techniques to study volume mapping problem in Computer Graphics. The first one is to find a harmonic map from a 3 manifold to a 3D solid sphere and the second is a sphere carving algorithm which calculates the simplicial decomposition of volume adapted to surfaces. In this paper, we apply these two techniques to brain mapping problem. We use a tetrahedral mesh to represent the brain volume. The experimental results on both synthetic and brain volume data are reported. We suggest that 3D harmonic mapping of brain volumes to a solid sphere can provide a canonical coordinate system for feature identification and segmentation, as well as anatomical normalization.
3063945
Statistical-based approach to word segmentation
This paper presents a text word extraction algorithm that takes a set of bounding boxes of glyphs and their associated text lines of a given document and partitions the glyphs into a set of text words, using only the geometric information of the input glyphs. The algorithm is probability based. An iterative, relaxation-like method is used to find the partitioning solution that maximizes the joint probability. To evaluate the performance of our test word extraction algorithm, we used a 3-fold validation method and developed a quantitative performance measure. The algorithm was evaluated on the UW-III database of some 1600 scanned document image pages. An area-overlap measure was used to find the correspondence between the detected entities and the ground-truth. For a total of 827, 433 ground truth words, the algorithm identified and segmented 800, 149 words correctly, an accuracy of 97.43%.
3331857
Table Detection via Probability Optimization
In this paper, we define the table detection problem as a probability optimization problem. We begin, as we do in our previous algorithm, finding and validating each detected table candidates. We proceed to compute a set of probability measurements for each of the table entities. The computation of the probability measurements takes into consideration tables, table text separators and table neighboring text blocks. Then, an iterative updating method is used to optimize the page segmentation probability to obtain the final result. This new algorithm shows a great improvement over our previous algorithm. The training and testing data set for the algorithm include 1, 125 document pages having 518 table entities and a total of 10, 934 cell entities. Compared with our previous work, it raised the accuracy rate to 95.67% from 90.32% and to 97.05% from 92.04%.
16121104
3D Surface Matching with Mutual Information and Riemann Surface Structures
Many medical imaging applications require the computation of dense correspondence vector fields that match one surface with another. Surface-based registration is useful for tracking brain change, registering functional imaging data from multiple subjects, and for creating statistical shape models of anatomy. To avoid the need for a large set of manually-defined landmarks to constrain these surface correspondences, we developed an algorithm to automate the matching of surface features. It extends the mutual information method to automatically match general 3D surfaces (including surfaces with a branching topology). We use diffeomorphic flows to optimally align the Riemann surface structures of two surfaces. First, we use holomorphic 1-forms to induce consistent conformal grids on both surfaces. High genus surfaces are mapped to a set of rectangles in the Euclidean plane, and closed genus-zero surfaces are mapped to the sphere. Next, we compute stable geometric features (mean curvature and the conformal factor) and pull them back as scalar fields onto the 2D parameter domains. Mutual information is used as a cost functional to drive a fluid flow in the parameter domain that optimally aligns these surface features. A diffeomorphic surface-tosurface mapping is then recovered that matches anatomy in 3D. Lastly, we present a spectral method that ensures that the grids induced on the target surface remain conformal when pulled through the correspondence field. Using the chain rule, we express the gradient of the mutual information between surfaces in the conformal basis of the source surface. This finite-dimensional linear space generates all conformal reparameterizations of the surface. Illustrative experiments show the method applied to hippocampal surface registration, a key step in subcortical shape analysis in Alzheimer’s disease and schizophrenia.
5121588
Brain Surface Parameterization Using Riemann Surface Structure
We develop a general approach that uses holomorphic 1-forms to parameterize anatomical surfaces with complex (possibly branching) topology. Rather than evolve the surface geometry to a plane or sphere, we instead use the fact that all orientable surfaces are Riemann surfaces and admit conformal structures, which induce special curvilinear coordinate systems on the surfaces. Based on Riemann surface structure, we can then canonically partition the surface into patches. Each of these patches can be conformally mapped to a parallelogram. The resulting surface subdivision and the parameterizations of the components are intrinsic and stable. To illustrate the technique, we computed conformal structures for several types of anatomical surfaces in MRI scans of the brain, including the cortex, hippocampus, and lateral ventricles. We found that the resulting parameterizations were consistent across subjects, even for branching structures such as the ventricles, which are otherwise difficult to parameterize. Compared with other variational approaches based on surface inflation, our technique works on surfaces with arbitrary complexity while guaranteeing minimal distortion in the parameterization. It also offers a way to explicitly match landmark curves in anatomical surfaces such as the cortex, providing a surface-based framework to compare anatomy statistically and to generate grids on surfaces for PDE-based signal processing.
16439255
Privacy Aware Market Basket Data Set Generation: A Feasible Approach for Inverse Frequent Set Mining
Association rule mining has received a lot of attention in the data mining community and several algorithms were proposed to improve the performance of association rule or frequent itemset mining. The IBM Almaden synthetic data generator has been commonly used for performance evaluation. One recent work shows that the data generated is not good enough for benchmarking as it has very different characteristics from real-world data sets. Hence there is a great need to use real-world data sets as benchmarks. However, organizations hesitate to provide their data due to privacy concerns. Recent work on privacy preserving association rule mining addresses this issue by modifying real data sets to hide sensitive or private rules. However, modifying individual values in real data may impact on other, non-sensitive rules. In this paper, we propose a feasible solution to the NPcomplete problem of inverse frequent set mining. Since solving this problem by linear programming techniques is very computationally prohibitive, we apply graph-theoretical results to divide the original itemsets into components that preserve maximum likelihood estimation. We then use iterative proportional fitting method to each component. The technique is experimentally evaluated with two real data sets and one synthetic data set. The results show that our approach is effective and efficient for reconstructing market basket data set from a given set of frequent itemsets while preserving sensitive information.
5326806
Modeling for Optimal Probability Prediction
We present a general modeling method for optimal probability prediction over future observations, in which model dimensionality is determined as a natural by-product. This new method yields several estimators, and we establish theoretically that they are optimal (either overall or under stated restrictions) when the number of free parameters is infinite. As a case study, we investigate the problem of fitting logistic models in finite-sample situations. Simulation results on both artificial and practical datasets are supportive.
207640585
On fuzzy multiattribute decision-making models and methods with incomplete preference information
In the paper by Li (Fuzzy Sets and Systems 106 (1999) 113), a minimum average weighted deviation method was investigated to determine the weights of attributes using incomplete preference information. It is shown in this paper that the method is flawed and can only be meaningful if it is revised. Two general ways are presented to revise it and are justified by using Lagrange multiplier method and Hessian matrix. Four specific revised models and approaches are investigated and examined through a numerical example. Different membership functions are suggested and utilized to normalize decision matrix so that a credible ranking order can be achieved.
15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
16943053
Towards an indoor location system using RF signal strength in IEEE 802.11 networks
Location is one of the most important contexts used in pervasive computing environments. GPS systems are intensely used to detect the location information; they mainly work in outdoor environment. Applications call for precise, easy-to-build, and easy-to-use indoor location systems. This paper presents our work to implement an indoor location determination system for Microsoft-Windows-based platforms using a preexisting IEEE 802.11 wireless network. The location is determined from radio signal strength information collected from multiple base stations at different physical locations. Our experiments show a high accuracy rate of this approach.