Text and picture segmentation by the distribution analysis of wavelet coefficients
This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

title: Stochastic modeling of volume images with a 3-D hidden Markov model
abstract: Over the years, researchers in the image analysis community have successfully used various statistical modeling methods to segment, classify and annotate digital images. In this paper, we propose a 3-D hidden Markov model (HMM) for volume image modeling. A computationally efficient algorithm is developed to estimate the model. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Experiments have demonstrated that 3-D HMM outperforms Gaussian mixture model based clustering by an order of magnitude in accuracy.

title: On Zador's entropy-constrained quantization theorem
abstract: Zador's classic result for the asymptotic high-rate behavior of entropy-constrained vector quantization is recast in a Lagrangian form which better matches the Lloyd algorithm used to optimize such quantizers. A proof that the result holds for a general class of distributions is sketched.

title: A source coding approach to classification by vector quantization and the principle of minimum description length
abstract: An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.

title: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries
abstract: We present here SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries), an image retrieval system using semantics classification and integrated region matching (IRM) based upon image segmentation. The SIMPLIcity system represents an image by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into categories which are intended to distinguish semantically meaningful differences, such as textured versus nontextured, indoor versus outdoor, and graph versus photograph. Retrieval is enhanced by narrowing down the searching range in a database to a particular category and exploiting semantically-adaptive searching methods. A measure for the overall similarity between images, the IRM distance, is defined by a region-matching scheme that integrates properties of all the regions in the images. This overall similarity approach reduces the adverse effect of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to a database of about 200,000 general-purpose images demonstrates accurate retrieval at high speed. The system is also robust to image alterations.

title: Significance Of inter-species matches when evolutionary rate varies
abstract: We develop techniques to estimate the statistical significance of gap-free alignments between two genomic DNA sequences, using human-mouse alignments as an example. The sequences are assumed to be sufficiently similar that some but not all of the neutrally evolving regions (i.e., those under no evolutionary constraint) can be reliably aligned. Our goal is to model the situation in which the neutral rate of evolution, and hence the extent of the aligning intervals, varies across the genome. In some cases, this permits the weaker of two matches to be judged as less likely to have arisen by chance, provided it lies in a genomic interval with a high level of background divergence. We employ a Hidden Markov Model to capture variations in divergence rates, and assign probability values to gap-free alignments using techniques related to those used for the same purpose by Blast. Our methods are illustrated in detail using a 1.49 Mb genomic region. Preliminary results using all of human chromosome 22 indicate that these techniques will work for the entire human genome.

title: Multi-channel Noise Reduction in Noisy Environments
abstract: Multi-channel noise reduction has been widely researched to reduce acoustic noise signals and to improve the performance of many speech applications in noisy environments. In this paper, we first introduce the state-of-the-art multi-channel noise reduction methods, especially beamforming based methods, and discuss their performance limitations. Subsequently, we present a multi-channel noise reduction system we are developing that consists of localized noise suppression by microphone array and non-localized noise suppression by post-filtering. Experimental results are also presented to show the benefits of our developed noise reduction system with respect to the traditional algorithms in terms of speech recognition rate. Some suggestions are finally presented for the further research.

title: ECSP: An Efficient Cluster Based P2P Architecture

title: The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
abstract: Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

title: Finite element analysis for a nonlinear diffusion model in image processing
abstract: The optimal error estimate O ( h k +1 ) for a popular nonlinear diffusion model widely used in image processing is proved for the standard k th -order ( k ≥ 1) conforming tensor-product finite elements in the L 2 -norm. The optimal L 2 -estimate is obtained by the integral identity technique [1–3] without using the classic Nitsche duality argument [4].

title: A fuzzy genetic algorithm for driver scheduling
abstract: This paper presents a hybrid genetic algorithm (GA) for the bi-objective public transport driver scheduling problem. A greedy heuristic is used, which constructs a schedule by sequentially selecting shifts, from a very large set of pre-generated legal potential shifts, to cover the remaining work. Individual shifts and the schedule as a whole have to be evaluated in the process. Fuzzy set theory is applied on such evaluations. For individual shifts, their structural efficiency is assessed by fuzzified criteria identified from practical knowledge of the problem domain. A GA is used to derive a near-optimal weight distribution amongst the fuzzified criteria, so that a single-valued weighted evaluation can be computed for each shift. The corresponding schedule constructed utilising the weight distribution is evaluated by the GA’s fitness function, in which the two objectives of minimising the number of shifts and minimising the total cost are formulated as a fuzzy goal. Comparative results on real-world problems are presented.