15532502
Illumination normalization for face recognition and uneven background correction using total variation based image models
We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.
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.
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.
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
1080835
Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation
In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms. Keywords—Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.
45004012
Dual-Hamiltonian-path-based multicasting on wormhole-routed star graph interconnection networks
The star graph interconnection network has been recognized as an attractive alternative to the popular hypercube network. In this paper we first address a dual-Hamiltonian-path-based (DHPB) routing model with two virtual channels based on two Hamiltonian paths and a network partitioning strategy for wormhole-routed star graph networks. Then, we propose three efficient multicast routing schemes on basis of such a model. The proposed schemes are network-selection-based (NSB), heuristic-network-selection-based (HNSB), and two-phase heuristic-network-selection-based (TP-HNSB) dual-path routing. All of the three proposed schemes are deadlock-free. Finally, experimental results are given to show our proposed three routing schemes outperform the unicast-based, the Hamiltonian-path, and the single-Hamiltonian-path-based (SHPB) dual-path routing schemes significantly.
41663574
Mining Frequent Tree-Like Patterns in Large Datasets
In this paper, we propose a novel data mining scheme to explore the frequent hierarchical structure patterns, named tree-like patterns, with the relationship of each item on a sequence. By tree-like patterns, we are clear to find out the relation of items between the cause and effect. Finally, we discuss the different characteristics to our mined patterns with others. As a consequence, we can find out that our addressed tree-like patterns can be widely used to explore a variety of different applications.
418775
Progressive image watermarking
Progressive transmission of images is very useful in many applications, especially in image transmission over the Internet. To view an image, people would want to see part of the image while the image transmission is in progress, rather than having to wait until the end of the image transmission. On the other hand, the ease of transmission and copying of images creates the need to use digital watermarking to embed the copyright information seamlessly into the media. We propose a progressive image watermarking scheme. In this scheme, the watermark is embedded in such a way that we can retrieve part of it even when the watermarked image is still being transmitted. As transmission progresses, the retrieved watermark has a decreasing bit error rate. Our proposed methods not only transmit the watermarked image progressively, but also intelligently select watermark embedding locations robust to various attacks.
15759301
Animating human face under arbitrary illumination
The authors present a method to animate a human face under arbitrary lighting conditions. We first acquire the reflectance models of a human face in various expressions by employing a robot to precisely control the light position. By developing a relighting tool, named LUXMASTER, we achieve great freedom to change the illumination condition and get a variety of photorealistic novel renderings of the human face. Then, we introduce morphing techniques to add the time dimension and produce a clip of facial animation with varying illumination and expression. In order to make the morphing work easily, we design an expressive, intuitive and efficient tool, called FLUIDMAN, which at the same time enables an enormous possibility of visual effects.
5643714
On Some Characterisation Problems of Subdomain Testing
Subdomain testing is a very general approach to the selection of test cases. It captures the characteristics of testing strategies that require the test suite to cover some predefined testing requirements. This paper attempts to characterise precisely the failure distributions for the best and worst case of any given subdomain testing strategy. Our analysis has revealed some crucial factors and principles that affect the effectiveness of subdomain testing strategies.