Extraction of Text Objects in Video Documents: Recent Progress
Text extraction in video documents, as an important research field of content-based information indexing and retrieval, has been developing rapidly since 1990s. This has led to much progress in text extraction, performance evaluation, and related applications. By reviewing the approaches proposed during the past five years, this paper introduces the progress made in this area and discusses promising directions for future research.

title: A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition
abstract: A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature space statistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.

title: Image Mining: Trends and Developments
abstract: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining.

title: Using temporal logic to specify adaptive program semantics
abstract: Computer software must dynamically adapt to changing conditions. In order to fully realize the benefit of dynamic adaptation, it must be performed correctly. The correctness of adaptation cannot be properly addressed without precisely specifying the requirements for adaptation. This paper introduces an approach to formally specifying adaptation requirements in temporal logic. We introduce A-LTL, an adaptation-based extension to linear temporal logic, and use this logic to specify three commonly used adaptation semantics. Composition techniques are developed and applied to A-LTL to construct the specification of an adaptive program. We introduce adaptation semantics graphs to visually represent the adaptation semantics, which can also be used to automatically generate specification for adaptive programs.

title: Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS
abstract: The idea of multisensor integration is to use multiple sensors for measuring the same variables, where each sensor has its own accuracy, reliability and drawbacks. The sensor information is integrated by some data integration algorithms. In this paper, Radial Basis Function (RBF) neural networks and multisensor data fusion technology are combined and used in the fault detection and diagnosis of sensors hardware faults in the Satellite Attitude Measurement System (SAMS). The fusion method of the RBF neural networks is adopted. By using the combination method the outputs of the system are more accurate and reliable than each individual sensor. Research results show that this method for the detection and diagnosis of the sensors hardware faults in the SAMS is feasible and more effective, and for the sensors which measure the same attitude angle, using the method of firstly integration, then faults diagnosis, finally connection with the measurement system, the systematic measurement precision and performance-price-ratio can be improved.

title: Texture-Based Image Retrieval by Edge Detection Matching GLCM
abstract: 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: 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%