Image and Texture Segmentation Using Local Spectral Histograms
We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results

title: A schema-based model for phonemic restoration
abstract: Phonemic restoration is the perceptual synthesis of phonemes when masked by appropriate replacement sounds by utilizing linguistic context. Current models attempting to accomplish acoustic restoration of phonemes, however, use only temporal continuity and produce poor restoration of unvoiced phonemes, and are also limited in their ability to restore voiced phonemes. We present a schema-based model for phonemic restoration. The model employs a missing data speech recognition system to decode speech based on intact portions and activates word templates corresponding to the words containing the masked phonemes. An activated template is dynamically time warped to the noisy word and is then used to restore the speech frames corresponding to the masked phoneme, thereby synthesizing it. The model is able to restore both voiced and unvoiced phonemes with a high degree of naturalness. Systematic testing shows that this model outperforms a Kalman-filter based model.

title: On Connectedness: A Solution Based on Oscillatory Correlation
abstract: A long-standing problem in neural computation has been the problem of connectedness, first identified by Minsky and Papert (1969). This problem served as the cornerstone for them to establish analytically that perceptrons are fundamentally limited in computing geometrical (topological) properties. A solution to this problem is offered by a different class of neural networks: oscillator networks. To solve the problem, the representation of oscillatory correlation is employed, whereby one pattern is represented as a synchronized block of oscillators and different patterns are represented by distinct blocks that desynchronize from each other. Oscillatory correlation emerges from LEGION (locally excitatory globally inhibitory oscillator network), whose architecture consists of local excitation and global inhibition among neural oscillators. It is further shown that these oscillator networks exhibit sensitivity to topological structure, which may lay a neurocomputational foundation for explaining the psychophysical phenomenon of topological perception.

title: Intrinsic generalization analysis of low dimensional representations
abstract: Low dimensional representations of images impose equivalence relations in the image space; the induced equivalence class of an image is named as its intrinsic generalization. The intrinsic generalization of a representation provides a novel way to measure its generalization and leads to more fundamental insights than the commonly used recognition performance, which is heavily influenced by the choice of training and test data. We demonstrate the limitations of linear subspace representations by sampling their intrinsic generalization, and propose a nonlinear representation that overcomes these limitations. The proposed representation projects images nonlinearly into the marginal densities of their filter responses, followed by linear projections of the marginals. We use experiments on large datasets to show that the representations that have better intrinsic generalization also lead to better recognition performance.

title: Texture classification using spectral histograms
abstract: Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using chi(2)-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.

title: Image Segmentation Based on Oscillatory Correlation
abstract: We study image segmentation on the basis of locally excitatory, globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a lateral potential for each oscillator so that only oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of the lateral potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions but without affecting those corresponding to major regions. We show that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and we illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real gray-level images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation.

title: An Edge-Preserving Image Reconstruction Using Neural Network
abstract: This paper presents an image restoration model based on the implicit function theorem and edge-preserving regularization. We then apply the model on the subband-coded images using the artificial neural network. The edge information is extracted from the source image as a priori nowledge to recover the details and reduce the ringing artifact of the subband-coded image. The multilayer perceptron model is employed to implement the restoration process. The main merit of the presented approach is that the neural network model is massively parallel with strong robustness for the transmission noise and parameter or structure perturbation, and it can be realized by VLSI technologies for real-time applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experimental results showed that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.

title: Theoretical Foundation for Nonlinear Edge-Preserving Regularized Learning Image Restoration
abstract: Image restoration is an important issue in image processing, which helps recovering of degraded images caused by various factors in different circumstances. Neural networks models have been successfully applied in handling image restoration problems and some progress and promising results have been reported in the past. A popular neural networks model for image restoration is the Hopfield network due to its ability on dealing with optimization problems. A degraded image may have multiple corresponding solutions, i.e., the restored images, and the obtained solution can be sub-optimal which is related to a local minimum point in the weight space of the Hopfield network. This paper gives an algebraic characterization on images and shows that images with lower complexity can be restored uniquely from their degraded images and edge information. The obtained result in this paper establishes a mathematical basis for employing the mapping neural networks to realize a learning based image restoration scheme. The linear image restoration model is firstly generalized to a nonlinear one to broaden the scope of application. Secondly, we view the image restoration task as a set of approximation problems in a high dimensional space, and a mapping relationship between the degraded images with edge information and the source images is then built using feed-forward neural networks and the well trained neural network can be used to restore the degraded images in realtime. Computer simulations demonstrate the effectiveness of the learning image restoration techniques proposed in this paper.

title: Design and implementation of the GLIF3 guideline execution engine
abstract: We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

title: Trading off between Misclassification, Recognition and Generalization in Data Mining with Continuous Features
abstract: This paper aims at developing a data mining approach for classification rule representation and automated acquisition from numerical data with continuous attributes. The classification rules are crisp and described by ellipsoidal regions with different attributes for each individual rule. A regularization model trading off misclassification rate, recognition rate and generalization ability is presented and applied to rule refinement. A regularizing data mining algorithm is given, which includes self-organizing map network based clustering techniques, feature selection using breakpoint technique, rule initialization and optimization, classifier structure and usage. An Illustrative example demonstrates the applicability and potential of the proposed techniques for domains with continuous attributes.

title: Semantics modeling based image retrieval system using neural networks
abstract: Semantics based image retrieval techniques show a promising direction to the development of CBIR systems. To design such systems, semantics modeling is one of the most difficult tasks. This paper aims to develop a semantics modeling approach using neural networks. In our work, a neural network is utilized to memorize the semantic patterns within the images. An intelligent image retrieval system is designed based on this model. User's relevance feedback is used for enhancing the retrieval performance. Experimental results from the prototype system demonstrate the effectiveness of the proposed approach.