Gouchol Pok

Decision-based median filter improved by predictions

This paper presents a decision-based median filtering algorithm in which local image structures are used to estimate the original values of the noisy pixels. The decision whether a pixel is corrupted or not is based on a new decision measure which considers the differences of adjacent pixel values in the rank-ordered sequence. Once the pixels in a noisy image have been classified into uncorrupted and noise-corrupted ones, the blocks containing only the uncorrupted pixels are used to train the predictive relationship between the center pixel and its neighbors, which is represented by a function approximation f. By applying f to noise-corrupted blocks, we could generate the candidates of the original value of a noise-corrupted pixel, and estimate it using median filtering of the candidates.

Texture edge detection by feature encoding and predictive model

Texture boundaries or edges are useful information for segmenting heterogeneous textures into several classes. Texture edge detection is different from the conventional edge detection that is based on the pixel-wise changes of gray level intensities, because textures are formed by patterned placement of texture elements over some regions. We propose a prediction-based texture edge detection method that includes encoding and prediction modules as its major components. The encoding module projects n-dimensional texture features onto a 1-dimensional feature map through the SOFM algorithm to obtain scalar features, and the prediction module computes the predictive relationship of the scalar features with respect to their neighbors sampled from 8 directions. The variance of prediction errors is used as the measure for detection of edges. In the experiments with the micro-textures, our method has shown its effectiveness in detecting the texture edges.

Correlation of Amino Acid Physicochemical Properties with Protein Secondary Structure Conformation

Eight representative physicochemical properties of amino acids are considered to encode each residue and correlative information is examined in relation to the formation of protein secondary structure. Features salient at the coarse level are first gleaned through vector quantization technique and then more refined class-specific features are identified based on the vector element-wise analysis. Effectiveness of the method has been validated in experiments to predict secondary structure using the widely used protein sequence sets. Heuristic rationale for advantage of using physicochemical properties of amino acids over the conventional statistics-based method relying on the frequency of residue occurrence is also presented.

Rotation and gray-scale invariant classification of textures improved by spatial distribution of features

In this paper, we present a framework for texture descriptors based on spatial distribution of textural features. Our approach is based on the observation that regional properties of textures are well captured by correlations among local texture patterns. The proposed method has been evaluated through experiments using real textures, and has shown significant improvements in recognition rates.

Texture classification by a two-level hybrid scheme

In this paper, we propose a novel feature extraction scheme for texture classification, in which the texture features are extracted by a two-level hybrid scheme, by integrating two statistical techniques of texture analysis. In the first step, the low level features are extracted by the Gabor filters, and they are encoded with the feature map indices, using Kohonens SOFM algorithm. In the next step, the encoded feature images are processed by the Gabor filters, Gaussian Markov random fields (GMRF), and Grey level co- occurrence matrix (GLCM) methods to extract the high level features. By integrating two methods of texture analysis in a cascaded manner, we obtained the texture features which achieved a high accuracy for the classification of texture patterns. The proposed schemes were tested on the real microtextures, and the Gabor-GMRF scheme achieved 10 percent increase of the recognition rate, compared to the result obtained by the simple Gabor filtering.

Emerging Patterns Based Methodology for Prediction of Patients with Myocardial Ischemia

Heart disease is the one of the significant health problem in the world. Recently, most serious problem caused by it is that the patient becomes younger. Therefore, it is very important and necessary to find the early symptoms of heart problems for better treatment and effective methodology for predicting the disease. Data mining is the one of the efficient approaches. However, there are still some tasks have to be solved. One is that the result should make it easy to explain the relationship between class label and predictors for the heart disease data. In this paper, redefined T-tree algorithm is used to mine the emerging patterns to perform the work and solve the problem. Also, the aggregate score is considered to build classifier for the prediction work. The algorithms CMAR, CPAR, C4.5 and our method are applied to the dataset and the proposed method shows the better accuracy than others (The accuracy is between 75% to 85%)

Unsupervised texture segmentation based on histogram of encoded Gabor features and MRF model

In this paper, we propose an unsupervised texture segmentation scheme in which Gabor transforms and GMRF model are integrated. The Gabor filters are used to extract low-level textural features. The Gabor feature vectors are mapped to an 1-D space using the Kohnens SOFM algorithm, and then encoded by the feature map indices. The histogram of encoded features over a small window are used to determine the regions of homogeneous textures. From these regions, class-specific parameters for GMRF model are estimated and used to detect exact boundaries of different textures.

Classification of Closed Frequent Patterns Improved by Feature Space Transformation

In some real-world applications, the predefined features are not discriminative enough to represent well the distinctiveness of different classes. Therefore, building a more well-defined feature space becomes an urgent task. The main goal of feature space transformation is to map a set of features defined in a space into a new more powerful feature space so that the classification based on the transformed data can achieve performance gain compared to the performance in the original space. In this paper, we introduce a feature transformation method in which the feature transformation is conducted using the closed frequent patterns. Experiments on real-world datasets show that the transformed features obtained from combining the closed frequent patterns and the original features are superior in terms of classification accuracy than the approach based solely on closed frequent patterns.

New shape-based texture descriptors for rotation invariant texture classification

In this paper, we present a new rotation-invariant texture description scheme based on an explicit transform of texture features to shapes. Texture features are obtained in a short computational time by applying the partial form of Gabor functions. These features are then transformed to 2-D closed shapes, and their moment invariants and global shape descriptors are derived to classify the rotated textures. Experiments using various texture samples showed high rates of correct classifications.

Fast estimation of the number of texture segments using cooccurrence statistics

Estimation of the number of clusters is an essential processing step for various applications. Existing approaches search for an optimal solution by computing and comparing a validity measure for all feasible configurations, and tend to under-estimate the number of clusters incorrectly. We propose a fast and robust method to estimate the number of clusters without adopting an exhaustive search. Our scheme first extracts the relationship of neighboring features, and then uses this information to partition the clusters. The superb performance of the method is verified by the simulation results in determining the number of texture segments in textured images.