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.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
3052468
Performance Modeling and Analysis for Resource Scheduling in Data Grids
Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.
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.
12892539
Experimental analysis of mode switching techniques in pen-based user interfaces
Inking and gesturing are two central tasks in pen-based user interfaces. Switching between modes for entry of uninterpreted ink and entry of gestures is required by many pen-based user interfaces. Without an appropriate mode switching technique, pen-based interactions in such situations may be inefficient and cumbersome. In this paper, we investigate five techniques for switching between ink and gesture modes in pen interfaces, including a pen-pressure based mode switching technique that allows implicit mode transition. A quantitative experimental study was conducted to evaluate the performance of these techniques. The results suggest that pressing a button with the non-preferred hand offers the fastest performance, while the technique of holding the pen still is significantly slower and more prone to error than the other techniques. Pressure, while promising, did not perform as well as the non-preferred hand button with our current implementation.
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.
36756485
Face Recognition with Irregular Region Spin Images
This paper explores how spin images can be constructed using shapefrom-shading information and used for the purpose 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 [6] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on spin image histograms for an arrangement of image patches. Comparing to our previous spin image approach, the current one has two basic difference: Euclidean distance is replaced by geodesic distance; Irregular face region is applied to better fit face contour. 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 93% on CMU PIE sub-database.