19415028
Unified model in identity subspace for face recognition
Human faces have two important characteristics: (1) They are similar objects and the specific, variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information. is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people groupA, can be generalized well to facial images of unknown individuals, and can be used to recognize facial images from an unknown people groupB with only one sample per subject. Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.
7641777
GBNet: Deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach
BackgroundCombinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.ResultsWe present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.ConclusionWe thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.
62074016
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
43366543
VLSI functional testing using critical path traces at hardware description language level
A new approach for testing VLSI circuits is presented. Through backward critical path tracing, a test and all faults detectable by the test are generated simultaneously. Therefore, the expensive fault simulation is completely eliminated. We present a critical path test generation procedure for digital systems described by hardware description language (HDL). A multiplication circuit described by a HDL is utilized for demonstrating the test generation method.
45019016
A Simplified Algorithm for Testing Microprocessors
Abstract In this paper, we present a number of algorithms to test the instruction decoding function of microprocessors. The algorithms are based on the knowledge of some timing and control information available to users through microprocessor manuals and data sheets. The tests are functional in nature. We also establish the order of complexity of the algorithms presented in this paper. As an example, the test complexity for a microprocessor is computed and the results are compared with a known algorithm.
31019926
A Functional Testing Method for Microprocessors
A method is presented for functional testing of microprocessors. First, a control fault model at the RTL (register transfer language) level is developed. Based on this model, the authors establish testing requirements for control faults. They present two test procedures to verify the write and read sequences, and use the write and read sequences to test each instruction in the microprocessor. By utilizing k-out-of-m codes, they use fewer tests to cover more faults, thereby reducing the test generation time. >
2536263
Multi-scale Voxel-Based Morphometry Via Weighted Spherical Harmonic Representation
Although the voxel-based morphometry (VBM) has been widely used in quantifying the amount of gray matter of the human brain, the optimal amount of registration that should be used in VBM has not been addressed. In this paper, we present a novel multi-scale VBM using the weighted spherical harmonic (SPHARM) representation to address the issue. The weighted-SPHARM provides the explicit smooth functional representation of a true unknown cortical boundary. Based on this new representation, the gray matter tissue density is constructed using the Euclidean distance map from a voxel to the estimated smooth cortical boundary. The methodology is applied in localizing abnormal cortical regions in a group of autistic subjects.
9589989
Efficient photometric stereo technique for three-dimensional surfaces with unknown BRDF
The present paper focuses on efficient inverse rendering using a photometric stereo technique for realistic surfaces. The technique primarily assumes the Lambertian reflection model only. For non-Lambertian surfaces, application of the technique to real surfaces in order to estimate 3D shape and spatially varying reflectance from sparse images remains difficult. In the present paper, we propose a new photometric stereo technique by which to efficiently recover a full surface model, starting from a small set of photographs. The proposed technique allows diffuse albedo to vary arbitrarily over surfaces while non-diffuse characteristics remain constant for a material. Specifically, the basic approach is to first recover the specular reflectance parameters of the surfaces by a novel optimization procedure. These parameters are then used to estimate the diffuse reflectance and surface normal for each point. As a result, a lighting-independent model of the geometry and reflectance properties of the surface is established using the proposed method, which can be used to re-render the images under novel lighting via traditional rendering methods.
53224637
Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram
13985475
A clustering-based approach for prediction of cardiac resynchronization therapy
This paper presents a method for predicting pacing sites in the left ventricle of a heart and its result can be used to assist device programming in cardiac resynchronization therapy (CRT), which is a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits. In this work, a surface tracking method is proposed to describe the ventricular wall motion and a hierarchical agglomerative clustering technique is applied to radial motion series to find candidate pacing sites. Using clinical MRI data in our experiments, we show that the proposed method performs as well as we expect. Our approach can not only effectively identify suitable pacing sites, but also distinguish patients from normals perfectly to help medical diagnosis.
14826038
A Spatio-Temporal Modeling Method for Shape Representation
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.