8607029
Illumination Brush: Interactive Design of All-Frequency Lighting
We present an appearance-based user interface for artists to efficiently design customized image-based lighting environments. 1 Our approach avoids typical iterations of parameter editing, rendering, and confirmation by providing a set of intuitive user interfaces for directly specifying the desired appearance of the model in the scene. Then the system automatically creates the lighting environment by solving the inverse shading problem. To obtain a realistic image, all-frequency lighting is used with a spherical radial basis function (SRBF) representation. Rendering is performed using precomputed radiance transfer (PRT) to achieve a responsive speed. User experiments demonstrated the effectiveness of the proposed system compared to a previous approach.
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.
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.
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.
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. >
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.
8270756
MetaDL: A Digital Library of Metadata for Sensitive or Complex Research Data
Traditional digital library systems have certain limitations when dealing with complex or sensitive (e.g. proprietary) data. Collections of digital libraries have to be accessed individually and through non-uniform interfaces. By introducing a level of abstraction, a Meta-Digital Library or MetaDL, users gain a central access portal that allows for prioritized queries, evaluation and rating of the results, and secure negotiations to obtain primary data. This paper demonstrates the MetaDL architecture with an application in brain imaging research, BrassDL, the Brain Support Access System Digital Library. BrassDL is currently under development. This paper describes a theoretical framework behind it, addressing aspects from metadata extraction and system-supported negotiations to legal, ethical and sustainability issues.
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.
10716292
Spatial Reflectance Recovery under Complex Illumination from Sparse Images
A major challenge in inverse reflectometry is the acquisition of spatially varying materials. In this paper, we introduce a method to recover spatial reflectance from a sparse set of images under general illumination. Specifically, we first remove the high-frequency varying diffuse reflection term by using a low-order spherical harmonic approximation. This allows us to directly estimate the specular properties with a cluster fitting process, which simplifies the fitting processes and addresses the problem of data inadequacy for sparse images. As a result, we can reconstruct a truly spatially varying BRDF model of the surface from less than 10 images. Experimental results will be presented in order to demonstrate the effectiveness of the proposed algorithm.
33440658
A New Technique for Program Code Compression in Embedded Microprocessor
Program code compression has become a critical technology nowadays, and it can promote the performance of microprocessor systems. But compression techniques nowadays can hardly achieve both satisfactory compression ratio and low hardware complexity. A new program code compression method is proposed in this paper. Experiment results show that the code sizes of ARM and OR1200 microprocessor can be efficiently reduced by 32.2% and 36.9% individually with this algorithm, resulting to a sharp decrease in memory traffic. A hardware decompressing prototype is also presented, revealing its advantage in ultra high speed decompression and low hardware complexity.
17699029
Spherical parameterization for 3D surface analysis in volumetric images
We propose a new spherical parameterization algorithm for general genus-zero surfaces that aims to control the area and length distortions at the same time. Prior work either considers only the length distortion or applies only to volumetric surfaces. Our algorithm starts from an initial parameterization, performs local and global smoothing steps alternately to improve the mapping quality, and converges to the solution. The local method minimizes the area distortion at a local submesh by solving a linear system as well as controls the length distortion. The global method attempts to reduce the area distortion by equalizing its distribution over the whole sphere. We perform experimental studies showing the effectiveness of the technique. Our new algorithm has many applications in medical image analysis, computer vision, graphics and multimedia databases.