15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
4650146
A New Coarse-to-Fine Framework for 3D Brain MR Image Registration
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.
46178429
Task Migration in All-Port Wormhole-Routed 2D Mesh Multicomputer
In a mesh multicomputer, submeshes are scheduled to perform jobs according to some processor allocation schemes, each job assigned to occupy processors of one submesh with an appropriate size. In order to assign the region for the incoming jobs, a task compaction is needed to produce a larger contiguous free region. The overhead of task compaction is primarily relied on designing an efficient task migration scheme. In this paper, we aim at the 2D mesh multicomputers with supporting wormhole routing in all-port communication model as the target machine. First, a certain and a relieved constraints between two submeshes for task migration are proposed. We next propose two task migration schemes following one of the constraints in 2D mesh multicomputers. We also analyze and discuss the routing phases for task migration. Finally, we compare the proposed task migration schemes via performance analysis.
8821639
Realistic video avatar
We present a system for the implementation of a photorealistic avatar using video captured from a user. This is achieved by constructing the dynamic video texture map and combining it with the 3D mesh model of the user to render the photorealistic avatar. The dynamic video texture map reflects the user's facial expressions and is generated by composing the cylindrical texture map of the user with constant updates either directly from the input video or derived from a temporal and spatial interpolation scheme. To derive the 3D mesh model for the user, we define feature points on a generic model and use the feature points identified on the video sequence to deform and obtain the avatar model. The goal of this realistic video avatar project is to provide a vivid representation of participants with a more realistic quality avatar, as compared to using only the facial animation parameters defined in MPEG-4 without the corresponding image updates. This scheme is suitable for conferencing applications because it requires much lower bandwidth than live video, and yet provides a 3D avatar representation for any virtual environment.
17694458
Optimal broadcasting on incomplete star graph interconnection networks
Broadcasting is an important collective communication operation in many applications which use parallel computing. In this paper, we focus on designing broadcasting algorithms for general incomplete star graphs. We propose two optimal one-to-all broadcasting algorithms for incomplete star graphs with a single-port communication model. An incomplete star graph with N nodes, where (n - 1)! < N < n!, is a subgraph of an n-dimensional star graph. The first scheme is single-message one-to-all broadcasting that takes O(nlogn) steps. The second one is multi-message one-to-all broadcasting that takes O(nlogn + m) steps.
13146679
A Broadcasting Algorithm in Star Graph Interconnection Networks
5909245
Modeling Gene Expression with Differential Equations
We propose a differential equation model for gene expression and provide two methods to construct the model from a set of temporal data. We model both transcription and translation by kinetic equations with feedback loops from translation products to transcription. Degradation of proteins and mRNAs is also incorporated. We study two methods to construct the model from experimental data: Minimum Weight Solutions to Linear Equations (MWSLE), which determines the regulation by solving under-determined linear equations, and Fourier Transform for Stable Systems (FTSS), which refines the model with cell cycle constraints. The results suggest that a minor set of temporal data may be sufficient to construct the model at the genome level. We also give a comprehensive discussion of other extended models: the RNA Model, the Protein Model, and the Time Delay Model.