17350574
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
We propose a novel hierarchical, nonlinear model that predicts brain activity in area V1 evoked by natural images. In the study reported here brain activity was measured by means of functional magnetic resonance imaging (fMRI), a noninvasive technique that provides an indirect measure of neural activity pooled over a small volume (≈ 2mm cube) of brain tissue. Our model, which we call the V-SPAM model, is based on the reasonable assumption that fMRI measurements reflect the (possibly nonlinearly) pooled, rectified output of a large population of simple and complex cells in V1. It has a hierarchical filtering stage that consists of three layers: model simple cells, model complex cells, and a third layer in which the complex cells are linearly pooled (called "pooled-complex" cells). The pooling stage then obtains the measured fMRI signals as a sparse additive model (SpAM) in which a sparse nonparametric (nonlinear) combination of model complex cell and model pooled-complex cell outputs are summed. Our results show that the V-SPAM model predicts fMRI responses evoked by natural images better than a benchmark model that only provides linear pooling of model complex cells. Furthermore, the spatial receptive fields, frequency tuning and orientation tuning curves of the V-SPAM model estimated for each voxel appears to be consistent with the known properties of V1, and with previous analyses of this data set. A visualization procedure applied to the V-SPAM model shows that most of the nonlinear pooling consists of simple compressive or saturating nonlinearities.
35646601
Iterated logarithmic expansions of the pathwise code lengths for exponential families
Rissanen's (1978, 1986, 1989, 1996) minimum description length (MDL) principle is a statistical modeling principle motivated by coding theory. For exponential families we obtain pathwise expansions, to the constant order, of the predictive and mixture codelengths used in MDL. The results are useful for understanding different MDL forms.
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.
34993492
Microarray image compression: SLOCO and the effect of information loss
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 require compression that takes advantage of microarray image structure. In this paper we develop a compression scheme for microarray images which can be either lossless or lossy with successive refinements. Existing measures of distortion such as mean squared pixel-wise error and visual fidelity are not appropriate for microarray images. We introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. Furthermore, our scheme has a coded data structure that allows fast decoding and reprocessing of image sub-blocks, and includes summary statistics and image segmentation information. The average lossless compression ratio is 1.83:1 for our cDNA test images and 2.43:1 for our inkjet test images, comparable or better than state-of-the-art lossless schemas, yet with additional structure and information. At an average lossy compression ratio of 8:1 for cDNA microarrays, we find that our scheme minimizes the effects of compression loss compared to other algorithms. We show that the variability in differential gene expression levels extracted from lossily vs. losslessly compressed microarray images is less than both the variability between different arrays and the variability between different extraction algorithms. In fact, lossy compression can improve the estimation of gene expression levels for cDNA images.
3387694
Compression of cDNA microarray images
The cDNA microarray image technology is a powerful tool for monitoring the expressions of thousands of genes simultaneously. An experiment is comprised of hundreds of images, each image easily over 30MB. Since image processing and statistical analysis tools are still under development, the images are always kept. Current focus on the development of standards makes efficient data transmission an important problem. Though the cost of disk space for storage is decreasing, efficient transmission requires compression. We have developed a partially progressive compression scheme for microarray images, allowing for fast decoding and reprocessing of image subsets. The scheme also permits locally varying image distortion or loss. The degree of loss can be chosen on-line, or be based on local parameters such as the spot intensities, or signal-to-noise ratios. The minimum decodable bitrate depends on the initial choice of parameters. We find that a bitrate of 4.1 bpp (cmp 32 bpp uncompressed) is sufficient for most tasks, such as image segmentation, and gene expression level extraction with a variety of existing methods. The lossless bitrate is about 17.5 bpp, comparable to the state-of-the-art lossless schemes, yet with the added flexibility of a progressive scheme. Our scheme has been tested on microarray images from different labs, and on images of varying quality.
125908908
The minimum description length principle in coding and modeling (invited paper)
34993677
Automatic text location in images and video frames
Abstract Textual data is very important in a number of applications such as image database indexing and document understanding. The goal of automatic text location without character recognition capabilities is to extract image regions that contain only text. These regions can then be either fed to an optical character recognition module or highlighted for a user. Text location is a very difficult problem because the characters in text can vary in font, size, spacing, alignment, orientation, color and texture. Further, characters are often embedded in a complex background in the image. We propose a new text location algorithm that is suitable in a number of applications, including conversion of newspaper advertisements from paper documents to their electronic versions, World Wide Web search, color image indexing and video indexing. In many of these applications, it is not necessary to extract all the text, so we emphasize on extracting important text with large size and high contrast. Our algorithm is very fast and has been shown to be successful in extracting important text in a large number of test images.
16993770
Service matchmaking with rough sets
With the wide adoption of open grid services architecture (OGSA) and Web services resource framework (WSRF), the grid is emerging as a service-oriented computing infrastructure for engineers and scientists to solve data and computationally intensive problems. It is envisioned that computing resources in a future grid environment will be exposed as services. Service discovery becomes an issue of vital importance for a wider uptake of the grid. This paper presents RSSM, a rough sets based service matchmaking algorithm for service discovery with an aim to tolerate uncertainty in identifying service properties. The evaluation results show that the RSSM algorithm is more effective in service discovery compared with other mechanisms such as UDDI and OWLS.
32601475
Multi-step-ahead Prediction Based on B-Spline Interpolation and Adaptive Time-Delay Neural Network
The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Motivated by B-spline interpolation and adaptive time-delay neural network (ATNN) which have proven successful in addressing different complicated problems, we aim at investigating the applicability of ATNN for MS prediction and propose a hybrid model SATNN. The annual sunspots and Mackey-Glass equation considered as benchmark chaotic nonlinear systems were selected to test our model. Validation studies indicated that the proposed model is quite effective in MS prediction, especially for single factor time series.
9449451
The Tree Representation of the Graph Used in Binary Image Processing
Abstract A very useful structure, line adjacency graph, used in binary image processing is mapped onto an extended binary tree in this paper. The latter is more convenient for storing, traversing and pruning, and it occupies less memory space than the former.
14866925
A new edge detector based on Fresnel diffraction
This paper proposed an edge detection scheme which is deduced from Fresnel diffraction. Analysis in this paper shows that Fresnel convolution kernel function performs well on edge enhancement when images are transformed into complex functions. Due to its mathematical complexity, the method is simplified into a linear convolution filter. The new edge detector is designed based on the simplified linear filter. Experimental results indicate that the new detector gives quantitative results equal to the Canny detector while it is more simple to be implemented.