41225454
Segmenting images corrupted by correlated noise
Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of non-overlapping homogeneous regions. This paper describes a new segmentation procedure which is designed to segment images corrupted by correlated noise. This procedure is based on Rissanen's (1989) minimum description length principle.
16239260
Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment
An usual assumption underlying the use of wavelet shrinkage is that the regression function is assumed to be either periodic or symmetric. However, such an assumption is not always realistic. This paper proposes an effective method for correcting the boundary bias introduced by the inappropriateness of such periodic or symmetric assumption. The idea is to combine wavelet shrinkage with local polynomial regression, where the latter regression technique is known to possess excellent boundary properties. Simulation results from both the univariate and bivariate settings provide strong evidence that the proposed method is extremely effective in terms of correcting boundary bias.
7698976
Segmenting Images Corrupted by Correlated Noise
Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of nonoverlapping homogeneous regions. The main contribution of this paper is the development of a new segmentation procedure which is designed to segment images corrupted by correlated noise. This new segmentation procedure is based on Rissanen's minimum description length (MDL) principle and consists of two components: 1) an MDL-based criterion in which the "best" segmentation is defined as its minimizer; and 2) a merging algorithm which attempts to locate this minimizer. The performance of this procedure is illustrated via a simulation study, with promising results.
2086088
Pattern generation using likelihood inference for cellular automata
Cellular automata are discrete dynamical systems which evolve on a discrete grid. Recent studies have shown that cellular automata with relatively simple rules can produce highly complex patterns. We develop likelihood-based methods for estimating rules of cellular automata aimed at the re-generation of observed regular patterns. Under noisy data, our approach is equivalent to estimating the local map of a stochastic cellular automaton. Direct computations of the maximum likelihood estimates are possible for regular binary patterns. The likelihood formulation of the problem is congenial with the use of the minimum description length principle as a model selection tool. We illustrate our method with a series of examples using binary images.
39601893
Smoothing parameter selection for smoothing splines: a simulation study
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk estimation methods have never been reported in the literature. Empirical conclusions from and recommendations based on the simulation results will be provided. One noteworthy empirical observation is that the popular method, generalized cross-validation, was outperformed by another method, an improved Akaike Information criterion, that shares the same assumptions and computational complexity.
14075649
Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms
This article proposes a new nonparametric procedure for estimating log spectra. This procedure consists of three major components: (1) a novel statistical model for modelling the unknown target log spectrum, (2) an AIC-based model selection criterion for choosing a 'best' fitting model, and (3) a genetic algorithm for effectively searching the 'best' fitting model. Numerical experiments are conducted to evaluate and compare the practical performance of the proposed procedure with some other common log spectral estimation procedures appearing in the literature. These other procedures include wavelet techniques, kernel smoothing and regression spline fitting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures.
7230571
Image Representation Using 2D Gabor Wavelets
This paper extends to two dimensions the frame criterion developed by Daubechies for one-dimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision, since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly-responding cortical neurons (called simple cells) are best modeled as a family of self-similar 2D Gabor wavelets. We therefore derive the conditions under which a set of continuous 2D Gabor wavelets will provide a complete representation of any image, and we also find self-similar wavelet parametrization which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis. Approximating a "tight frame" generates redundancy which allows low-resolution neural responses to represent high-resolution images.
16028378
Improving the Performance of Infomax Using Statistical Signal Processing Techniques
In this paper, we present a new method that speeds up the convergence of the infomax algorithm proposed by Bell and Sejnowski. One effect of the infomax algorithm is that the 2nd order and 4th order statistical correlations are reintroduced to the signals during the learning process due to the optimization with respect to the complete signal statistics. We show that repetitively forcing 2nd and 4th order correlations to zero speeds up the convergence and improves separating sources with fewer data points.
21281832
Progressive mesh metamorphosis
This paper describes a new integrated scheme for metamorphosis between two closed manifold genus-0 polyhedral models. Spherical parameterizations of the source and target models are created first. To control the morphing, any number of feature vertex pairs is specified and a fold-over free warping method is used to align two spherical embeddings. Our method does not create a merged meta-mesh or execute re-meshing to construct a common connectivity for morphs. Alternatively, a scheme for the progressive connectivity transformation of two spherical parameterizations is employed to generate the intermediate meshes. A novel semi-overlay with a geomorph scheme is proposed to reduce the popping effects caused by the connectivity transformation. We demonstrate several examples of aesthetically pleasing morphing sequences using the proposed scheme. Copyright © 2005 John Wiley & Sons, Ltd.
10214085
Influence of promoter potency on the transcriptional effects of YY1, SRF and Msx-1 in transient transfection analysis
Potent viral promoters/enhancers are often used to achieve high level expression of ectopic genes in transient transfection analysis. By using a GAL4-responsive transcription assay system, we show that the use of potent eukaryotic expression vectors can lead to biased transcriptional effects. Three functionally diverse transcription factors, YY1, SRF and Msx-1, were examined and each was found to exhibit a strong transrepression function in the context of the DNA binding domain of GAL4 when expressed from the cytomegalovirus (pCMV) or simian virus 40 (pSV) promoters/enhancers. An internal 15 amino acid domain of YY1 mediating transrepression in the viral promoter setting was identified. This GAL4-mediated transcriptional repression could, however, be completely relieved by using the yeast alcohol dehydrogenase promoter (pADH) to drive gene expression, which is approximately 100-fold weaker than canonical pCMV and pSV in cultured mammalian cells. In addition, low level expression achieved with the pADH vector unveiled the intrinsic transactivation functions of YY1 and SRF previously not observed with the GAL4 assay system. Our results highlight a potential pitfall in conventional pCMV- and pSV-based transfection assays and suggest that the use of a low level expression system may be preferable in most transcriptional analysis.
767826
The Spectral Independent Components of Natural Scenes
We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.