121595784
Face recognition using shading-based curvature attributes
In this paper, we apply shape-from-shading to face images to extract fields of surface normals and make estimates of surface curvature attributes. The attributes studied include minimum and maximum curvature, mean and Gaussian curvature, and, curvedness and shape-index. The curvature attributes are encoded as histograms and are used to perform recognition using a number of distance and similarity measures including the Euclidean distance, the Shannon entropy, the Renyi entropy and the Tsallis entropy. We compare the results obtained using the different curvature attributes and the different entropy measures. Using precision-recall curves, we find that the best performance is delivered when the Shannon entropy is applied to histograms of shape index and curvedness.
32291231
Face Recognition with Region Division and Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern of facial convexity and concavity. Spin images [4] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 98% on CMU PIE sub- database.
737421
Recognising trajectories of facial identities using kernel discriminant analysis
We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the nonlinear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories. q 2003 Elsevier B.V. All rights reserved.
53246102
Informal prototyping of continuous graphical interactions by demonstration
Informal prototyping tools have shown great potential in facilitating the early stage design of user interfaces. How-ever, continuous interactions, an important constituent of highly interactive interfaces, have not been well supported by previous tools. These interactions give continuous visual feedback, such as geometric changes of a graphical object, in response to continuous user input, such as the movement of a mouse. We built Monet, a sketch-based tool for proto-typing continuous interactions by demonstration. In Monet, designers can prototype continuous widgets and their states of interest using examples. They can also demonstrate com-pound behaviors involving multiple widgets by direct ma-nipulation. Monet allows continuous interactions to be eas-ily integrated with event-based, discrete interactions. Con-tinuous widgets can be embedded into storyboards and their states can condition or trigger storyboard transitions. Monet achieves these features by employing continuous function approximation and statistical classification techniques, without using any domain specific knowledge or assuming any application semantics. Informal feedback showed that Monet is a promising approach to enabling more complete tool support for early stage UI design.
9099317
String extraction from color airline coupon image using statistical approach
A novel technique is presented in this paper to extract strings in color images of both business settlement plan (BSP) and non-BSP airline coupon. The essential concept is to remove non-text pixels from complex coupon images, rather than extract strings directly. First we transfer color images from RGB to HSV space, which is approximate uniformed, and then remove the black component of images using the property of HSV space. A statistical approach called principal components analysis (PCA) is applied to extract strings by removing the background decorative pattern based on priori environment. Finally, a method to validate and improve performance is present.
8805137
Classification of Text Documents
The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83%, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.
12311851
GABI-Kat SimpleSearch: an Arabidopsis thaliana T-DNA mutant database with detailed information for confirmed insertions
Insertional mutagenesis approaches, especially by T-DNA, play important roles in gene function studies of the model plant Arabidopsis thaliana. GABI-Kat SimpleSearch () is a Flanking Sequence Tag (FST)-based database for T-DNA insertion mutants generated by the GABI-Kat project. Currently, the database contains >108 000 mapped FSTs from ∼64 000 lines which cover 64% of all annotated A.thaliana protein-coding genes. The web interface allows searching for relevant insertions by gene code, keyword, line identifier, GenBank accession number of the FST, and also by BLAST. A graphic display of the genome region around the gene or the FST assists users to select insertion lines of their interests. About 3500 insertions were confirmed in the offspring of the plant from which the original FST was generated, and the seeds of these lines are available from the Nottingham Arabidopsis Stock Centre. The database now also contains additional information such as segregation data, gene-specific primers and confirmation sequences. This information not only helps users to evaluate the usefulness of the mutant lines, but also covers a big part of the molecular characterization of the insertion alleles.