Texture-Based Image Retrieval by Edge Detection Matching GLCM
Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional co-occurrence matrices and Yaopsilas approach.

title: A Novel Approach Using Edge Detection Information for Texture Based Image Retrieval
abstract: Most texture-based image retrieval system just consider an original image of coarseness, contrast and roughness, actually there are many texture information in the edge image. In this paper, a method combining both edge information and gray level co-occurrence matrix properties is proposed to improve the retrieval performance. The proposed method gives encouraging results when comparing its retrieval performance to that of the Yao's method, in the same image database.

title: Leopard Image Retrieval using Region of Interest and Texture Feature
abstract: In content based image retrieval system, texture based natural image retrieval can not extract texture feature efficiently and accurately, because the nature image is very complex and most of method extract feature use the whole nature image. In this paper, a new method is proposed for leopard texture image retrieval. Usually, the habitat of leopard is grass or tree, there is large difference between leopard image background and foreground, so it is easy to separate the leopard region from background using Canny edge detection, and then extract region of interest of foreground. The similarity of two images will be determined by the similarities between pairs of region of interest. The experimental results on the leopard texture image database demonstrate that the proposed method is encouraging with an average successful retrieval rate of 95.66%

title: ROI Based Natural Image Retrieval Using Color and Texture Feature
abstract: Region of interest(ROI) plays an important role in image analysis. In this paper, an efficient approach for content based image retrieval combining both color and texture features using three ROIs is proposed. Firstly, segment image to three parts using K-means algorithm. Secondly, select three ROIs from the three parts and then extract color features and texture features of ROIs. The similarity of two images will be determined by the similarities between pairs of ROIs. The experimental results demonstrate that the proposed method is encouraging with a successful retrieval rate.

title: An Improved Fuzzy Image Enhancement Algorithm
abstract: An effective method for fuzzy image enhancement was presented by Russo, which was controlled by tuning of one parameter. The fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. On the basis of these, in this paper, we applied Russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. Firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; Secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; Finally, compared with Russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and Gaussian noise.

title: A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data
abstract: DNA microarray experiments generating thousands of gene 
expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the ”curse of dimensionality problem”. An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for dimensionality reduction of gene expression data which is optimal in the sense of least square error. In this paper we propose a new dimensionality reduction technique for specific bioinformatics applications based on Independent component Analysis(ICA). Being able to exploit higher order statistics to identify a linear model result, this ICA based dimensionality reduction technique 
outperforms PCA from both statistical and biological 
significance aspects. We present experiments on NCI 60 
dataset to show this result.

title: Humanoid Robot Gait Generation Based on Limit Cycle Stability
abstract: This paper presents the gait generation and mechanical design of a humanoid robot based on a limit cycle walking method-Virtual Slope Control. This method is inspired by Passive Dynamic Walking. By shortening the swing leg, the robot walking on level ground can be considered as on a virtual slope. Parallel double crank mechanisms and elastic feet are introduced to the 5 DoF robot leg, to make the heelstrike of the swing leg equivalent to the point-foot collision used in Virtual Slope Control. In practical walking, the gait is generated by connecting the two key frames in the sagittal and lateral plane with sinusoids. With the addition of leg rotational movement, the robot achieves a fast forward walking of 2.0leg/s and accomplishes omnidirectional walking favorably.

title: Multimodal Registration using the Discrete Wavelet Frame Transform
abstract: Image registration is a critical step in medical image analysis. In this paper, a novel image registration method based on the discrete wavelet frame transform (DWFT) and the sum of absolute distance (SAD) method is proposed. First, the multimodal images are decomposed by DWFT, which is shift-invariant compared to traditional dyadic wavelet transforms. Then the energy maps are computed from the details sub-band images. Finally, genetic algorithm (GA) is adopted to obtain the minimum SAD between the two energy maps. The proposed method is tested on 50 pairs of two-dimensional T1-weighted and T2-weighted modal images. Experimental results demonstrated that the proposed method can achieve high accuracy in the rigid transformation case

title: On Efficient and Effective Association Rule Mining from XML Data
abstract: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently and effectively. In XAR-Miner, raw XML data are first transform ed to either an Indexed Content Tree (IX-tree) or M ulti-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection in the AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized meta-patterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent under-generalization or over-generalization. Resultant generalized meta-patterns are used to generate large ARs that meet the support and confidence levels. An efficient AR mining algorithm is also presented based on candidate AR generation in the hierarchy of generalized meta-patterns. The experiments show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML docume nts than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.