3357707
A method for document zone content classification
This paper describes an algorithm to classify each given document zone into one of nine classes and provides a protocol for its performance evaluation. The classification scheme uses an optimized binary decision tree and Viterbi algorithm for HMM to find the optimal solution. Our algorithm was trained and tested on a total of 24,177 zones within the 1600 images from UWCDROM III database. Its accuracy rate is 98.45% with a mean false alarm rate of 0.50%.
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.
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.
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.
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%.
2521415
Brain Surface Conformal Parameterization
We develop a general approach that uses holomorphic 1forms 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. We can then automatically partition the surface using a critical graph that connects zero points in the conformal structure on the surface. The trajectories of iso-parametric curves canonically partition a surface into patches. Each of these patches is either a topological disk or a cylinder and can be conformally mapped to a parallelogram by integrating a holomorphic 1-form defined on the surface. 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 generates grids on surfaces for PDE-based signal processing.
10282937
Decoding Algorithm in Statistical Machine Translation
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.
15352869
Error Resilient Video Coding using Redundant Pictures
Coding of redundant pictures is supported in the latest international video coding standard H.264 (also known as MPEG-4 part 10 or AVC). This paper proposes a standard-compliant way to encode and decode redundant pictures for improved error resilience. The method is based on a combination of picture-level reference picture selection, reference picture list ordering, and a hierarchical allocation of redundant pictures, which can efficiently prevent temporal error propagation without relying on feedback information. Simulation results show that the method outperforms the optimal loss-aware rate-distortion optimized intra refresh method. The proposed algorithm has been adopted into the H.264 joint model.
7748348
Shape fitting with outliers
Given a set <i>H</i> of <i>n</i> hyperplanes in <i>R<sup>d</sup></i>, we present an algorithm that <i>e</i>-approximates the extent between the top and bottom <i>k</i> levels of the arrangement of <i>H</i> in time <i>O(n + (k/e)<sup>c</sup>)</i>, where <i>c</i> is a constant depending on <i>d</i>. The algorithm relies on computing a subset of <i>H</i> of size.<i>O(k/e<sup>d-1</sup>)</i>, in near linear time, such that the <i>k</i>-level of the arrangement of the subset approximates that of the original arrangement. Using this algorithm, we propose efficient approximation algorithms for shape fitting with outliers for various shapes. This is the first algorithms to handle outliers efficiently for the shape fitting problems considered.
15418757
Fingerprint classification by directional fields
Fingerprint classification provides an important fingerprint index and can reduce fingerprint matching time in a large database. A good classification algorithm can give an accurate index that is able to search a fingerprint database more effectively. We present a fingerprint classification algorithm that is based on directional fields. We compute directional fields of fingerprint images and detect singular points (cores). Then, we extract features that we define from fingerprint images. We also use k-means classifier and 3-nearest neighbor to classify features and distinguish which fingerprint is Arch, Left Loop, Right Loop, or Whorl. Experimental results show a significant improvement in fingerprint classification performance. Moreover, the time required for the classification algorithm is reduced.
44707452
Diagnosis of clustered faults for identical degree topologies
Huang et al, [1998] studied the diagnosis of clustered faults and wafer testing. They proposed a diagnosis algorithm for a probabilistic fault model in simple rectangular grid structures. In this paper, we extend their results and study the diagnosis algorithm for arbitrary identical degree topologies. Our results are useful and valid for a large class of topologies. We investigate the local and global performance of the algorithm under several important fault distributions: Bernoulli failure distribution, Gamma failure distribution, and exponential failure distribution. We demonstrate that the diagnosis scheme can identify almost all nodes successfully even if the percentage of fault-free units is low (much lower than 50%) while almost all units are guaranteed to be correctly identified. In addition, we show that the performance of the algorithm is insensitive to the changes of the percentage.