33099797
Compression of cDNA and inkjet microarray images
Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes the microarray image's structure and unique analysis goals. We have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art lossless schemas, yet with added flexibility and information.
2105049
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE
We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of thel1-regularized maximum likelihood estimator in the high-dimensional setting, where the number of nodes in the graph p, the number of edges in the graph s and the maximum node degree d, are allowed to grow as a function of the number of samples n. Our main result provides sufficient conditions on (n, p, d) for thel1-regularized MLE estimator to recover all the edges of the graph with high probability. Under some conditions on the model covariance, we show that model selection can be achieved for sample sizes n = Ω(d log(p)), with the error decaying as O(exp(−c log(p))) for some constant c. We illustrate our theoretical results via simulations and show good correspondences between the theoretical predictions and behavior in simulations.
6762008
Perceptual audio coding using adaptive pre- and post-filters and lossless compression
This paper proposes a versatile perceptual audio coding method that achieves high compression ratios and is capable of low encoding/decoding delay. It accommodates a variety of source signals (including both music and speech) with different sampling rates. It is based on separating irrelevance and redundancy reductions into independent functional units. This contrasts traditional audio coding where both are integrated within the same subband decomposition. The separation allows for the independent optimization of the irrelevance and redundancy reduction units. For both reductions, we rely on adaptive filtering and predictive coding as much as possible to minimize the delay. A psycho-acoustically controlled adaptive linear filter is used for the irrelevance reduction, and the redundancy reduction is carried out by a predictive lossless coding scheme, which is termed weighted cascaded least mean squared (WCLMS) method. Experiments are carried out on a database of moderate size which contains mono-signals of different sampling rates and varying nature (music, speech, or mixed). They show that the proposed WCLMS lossless coder outperforms other competing lossless coders in terms of compression ratios and delay, as applied to the pre-filtered signal. Moreover, a subjective listening test of the combined pre-filter/lossless coder and a state-of-the-art perceptual audio coder (PAC) shows that the new method achieves a comparable compression ratio and audio quality with a lower delay.
116839195
Some results on empirical processes and stochastic complexity
This thesis contains two parts. In Part I, an intuitive and simple approach is used to obtain uniform convergence and Central Limit Theorems for empirical processes indexed by families of functions, for stationary mixing sequences with not too slow a $\beta$-mixing (or $\phi$-mixing) rate. The conditions imposed are in terms of random metric entropies, and the $\beta$-mixing (or $\phi$-mixing) rate. In the case of a Vapnik-Cervonenkis class, the uniform convergence result holds for any strictly stationary sequence with $\beta$-mixing (or $\phi$-mixing) rate $\beta\sb{n}$ (or $\phi\sb{n}$) = $o$($n\sp{-d}$) for some d $>$ 0, while the Central Limit Theorem requires the rate $\beta\sb{n}$ (or $\phi\sb{n}$ = $o$($n\sp{-d}$) for some d $>$ 3. 
In Part II, we use a shortest code length criterion to select the bin width of a histo-gram from a string of i.i.d. observations. Under smoothness conditions on the underlying density on (0, 1), this criterion is shown to be optimal as a code selection criterion in reaching the smallest redundancy within the class of codes under consideration. Furthermore, the criterion consistently selects the underlying density when it is uniform on (0, 1). 
Minimax lower bounds for the redundancy over many smooth families of underlying densities are also derived. In a special case, the lower bound is shown to be achieved almost surely, and in expectation, by a predictive code based on a sequence of histograms. In the case of Sobolev balls, the minimax optimal code is described and a heuristic proof is provided.
828878
Image denoising via lossy compression and wavelet thresholding
Some past work has proposed to use lossy compression to remove noise, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. Building on this theme, we explain why compression (via coefficient quantization) is appropriate for filtering noise from signal by making the connection that quantization of transform coefficients approximates the operation of wavelet thresholding for denoising. That is, denoising is mainly due to the zero-zone and that the full precision of the thresholded coefficients is of secondary importance. The method of quantization is facilitated by a criterion similar to Rissanen's minimum description length principle. An important issue is the threshold value of the zero-zone (and of wavelet thresholding). For a natural image, it has been observed that its subband coefficients can be well modeled by a Laplacian distribution. With this assumption, we derive a threshold which is easy to compute and is intuitive. Experiments show that the proposed threshold performs close to optimal thresholding.
12165073
On the Convergence of Boosting Procedures
A boosting algorithm seeks to minimize empirically a loss function in a greedy fashion. The resulted estimator takes an additive function form and is built iteratively by applying a base estimator (or learner) to updated samples depending on the previous iterations. This paper studies convergence of boosting when it is carried out over the linear span of a family of basis functions. For general loss functions, we prove the convergence of boosting's greedy optimization to the infinimum of the loss function over the linear span. As a side product, these results reveal the importance of restricting the greedy search step sizes, as known in practice through the works of Friedman and others.
40810024
Page segmentation using document model
Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval and interpretation continues to be a challenging problem. An efficient document model is necessary to solve this problem. Document modeling involves techniques of thresholding, skew detection, geometric layout analysis and logical layout analysis. The derived model can then be used in document storage and retrieval. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer and logical analysis.
39489918
An economical contour extraction algorithm for understanding large-size engineering drawings
An algorithm is described which produces the extraction of an object's contours in a binary image while storing only two raster lines at a time during processing. The major data structure used is a block adjacency graph which occupies much less space (typically one hundredth) than the original image. Because this algorithm has very low costs of memory space and processing time, it is a powerful tool for processing large-size engineering drawings. Applications of the method to the recognition of some loop-structure logical symbols are described.<<ETX>>
42842563
A WSRF Based Shopping Cart System
Web Services Resource Framework (WSRF) is a set of specifications that represents a convergence point of the Web services and the Grid services communities. This paper presents our early experience with WSRF. A shopping cart system has been implemented with WSRF supported Globus toolkit 3.9.2 (GT3.9.2). Based on the system, the performance of the WSRF core in GT3.9.2 has also been evaluated.
11512625
Hourglass multimedia content and service composition framework for smart room environments
Pervasive environments with their ubiquitous multimedia devices such as video-enabled cell-phones, laptops, PC tablets, digital cameras, HDTV plasma displays are becoming integral part of our workplaces and homes. Furthermore, these digital multimedia devices make it easier and easier to create and view volumes of content that can be stored throughout the Internet. The big challenge is to organize the distributed content, get the distributed multimedia content composed, produced and distributed to users in customized fashion according to users' preference, and their resource availabilities in pervasive environments. Basically, the challenge is to create Internet Interactive Television where the user/interest group, residing in a pervasive environment, corresponds to the TV program director who decides and/or influences what content(s) will be viewed, how it will be composed, presented and delivered. In this paper, we present a vision how this challenge can be solved. We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments, which considers both users' interest in the content and the presentation capabilities of various display devices. This hourglass-based system relies on our content and service composition framework that allows for customized production, distribution and presentation of multimedia content for pervasive environments. We present the hourglass design, possible solutions for some content composition scenarios and validate the solutions via selected experiments.
41609314
A robust and fast skew detection algorithm for generic documents
Abstract A robust and fast skew detection algorithm based on hierarchical Hough transform is proposed. It is capable of detecting the skew angle for various document images, including technical articles, postal labels, handwritten text, forms, drawings and bar codes. The algorithm is robust even when black margins introduced by photocopying are present in the image and when the document is scanned at a low resolution of 50 dpi. The algorithm consists of two steps. In the first step we quickly extract the centroids of connected components using a graph data structure. Then, a hierarchical Hough transform (at two different angular resolutions) is applied to the selected centroids. The skew angle corresponds to the location of the highest peak in the Hough space. The performance of the algorithm is shown on a number of document images collected from various application domains. The algorithm is not very sensitive to algorithmic parameters. For an A4 size document image scanned at 50 dpi (typically 413 × 575 pixels), our algorithm is able to detect the skew angle with an accuracy of 0.1° in 0.4s of CPU time on a SunSparc 20 workstation.