29851215
Unified probabilistic models for face recognition from a single example image per person
This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach, 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.
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.
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.
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. >
61766535
Photometric stereo and weather estimation using internet images
We extend photometric stereo to make it work with internet images, which are typically associated with different viewpoints and significant noise. For popular tourism sites, thousands of images can be obtained from internet search engines. With these images, our method computes the global illumination for each image and the surface orientation at some scene points. The illumination information can then be used to estimate the weather conditions (such as sunny or cloudy) for each image, since there is a strong correlation between weather and scene illumination. We demonstrate our method on several challenging examples.
22846762
SCENS: a system for the mediated sharing of sensitive data
We introduce SCENS, a secure content exchange negotiation system suitable for the exchange of private digital data that reside in distributed digital repositories. SCENS is an open negotiation system with flexibility, security and scalability. SCENS is currently being designed to support data sharing in scientific research, by providing incentives and goals specific to a research community. However, it can easily be extended to apply to other communities, such as government, commercial and other types of exchanges. It is a trusted third party software infrastructure enabling independent entities to interact and conduct multiple forms of negotiation.
40046861
A New CORDIC Algorithm and Software Implementation Based on Synchronized Data Triggering Architecture
A new CORDIC algorithm based on sign bits prediction is presented. It is an effective method for transforming arbitrary iterations of classic CORDIC to simple matrix multiplications. At the same time, this algorithm is implemented based on synchronized data triggering architecture (SDTA). Experimental results show that the average speed-up of transcendental functions tested based on our version CORDIC compared with mathematic library supported in gcc3.2.2 is up to 283% under the given Neuron microprocessor.
10374803
Hemispherical Harmonic Surface Description and Applications to Medical Image Analysis
The use of surface harmonics for rigid and nonrigid shape description is well known. In this paper we define a set of complete hemispherical harmonic basis functions on a hemisphere domain and propose a novel parametric shape description method to efficiently and flexibly represent the surfaces of anatomical structures in medical images. As the first application of hemispherical harmonic theory in shape description, our technique differs from the previous surface harmonics shape descriptors, all of which don't work efficiently on the hemisphere-like objects that often exist in medical anatomical structures (e.g., ventricles, atriums, etc.). We demonstrate the effectiveness of our approach through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for surface modeling efficiency is described and the comparison results demonstrated that our method outperformed the previous approaches using spherical harmonic models.
53224637
Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram