On conformally flat circle bundles over surfaces

We study surface groups $\Gamma$ in $SO(4,1)$, which is the group of Mobius tranformations of $S^3$, and also the group of isometries of $\mathbb{H}^4$. We consider such $\Gamma$ so that its limit set $\Lambda_\Gamma$ is a quasi-circle in $S^3$, and so that the quotient $(S^3 - \Lambda_\Gamma) / \Gamma$ is a circle bundle over a surface. This circle bundle is said to be conformally flat, and our main goal is to discover how twisted such bundle may be by establishing a bound on its Euler number. By combinatorial approaches, we have two soft bounds in this direction on certain types of nice structures. In this article we also construct new examples, a "grafting" type path in the space of surface group representations into $SO(4,1)$: starting inside the quasi-Fuschsian locus, going through non-discrete territory and back.Comment: 28 pages, 7 figures. Updated from Thesis version: more correct bound of (3/2)n^2, updated exposition in section 3.

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2

Training Methods for Shunting Inhibitory Artificial Neural Networks

This project investigates a new class of high-order neural networks called shunting inhibitory artificial neural networks (SIANN\u27s) and their training methods. SIANN\u27s are biologically inspired neural networks whose dynamics are governed by a set of coupled nonlinear differential equations. The interactions among neurons are mediated via a nonlinear mechanism called shunting inhibition, which allows the neurons to operate as adaptive nonlinear filters. The project\u27s main objective is to devise training methods, based on error backpropagation type of algorithms, which would allow SIANNs to be trained to perform feature extraction for classification and nonlinear regression tasks. The training algorithms developed will simplify the task of designing complex, powerful neural networks for applications in pattern recognition, image processing, signal processing, machine vision and control. The five training methods adapted in this project for SIANN\u27s are error-backpropagation based on gradient descent (GD), gradient descent with variable learning rate (GDV), gradient descent with momentum (GDM), gradient descent with direct solution step (GDD) and APOLEX algorithm. SIANN\u27s and these training methods are implemented in MATLAB. Testing on several benchmarks including the parity problems, classification of 2-D patterns, and function approximation shows that SIANN\u27s trained using these methods yield comparable or better performance with multilayer perceptrons (MLP\u27s)

Services for Business Processes in EA – Are They in Relation?

Business services arguably play a central role in service-based information systems as they would fill in the gap between the technicality of Service-Oriented Architecture and the business processes captured in Enterprise Architecture. Business services have distinctive features that are not typically observed in plain Web services. The representation of business services requires that we view human activity and human-mediated functionality through the lens of computing and systems engineering. We give insights into the modeling of business services and relationships between them. This work sheds light on the analysis, design and reusability of business-aware services that business owners, entrepreneurs and business architects alike would find useful when dealing with their service ecosystem

Formation of high-quality Ag-based ohmic contacts to p-type GaN

Low resistance and high reflectance ohmic contacts on p-type GaN were achieved using an Ag-based metallization scheme. Oxidation annealing was the key to achieve ohmic behavior of Ag-based contacts on p-type GaN. A low contact resistivity of similar to 5x10(-5) Omega cm(2) could be achieved from Me (=Ni, Ir, Pt, or Ru)/Ag (50/1200 angstrom) contacts after annealing at 500 degrees C for 1 min in O(2) ambient. Oxidation annealing promoted the out-diffusion of Ga atoms from the GaN layer, and Ga atoms dissolved in the in-diffused Ag layer with the formation of Ag-Ga solid solution, resulting in ohmic contact formation. Using Ru/Ni/Au (500/200/500 angstrom) overlayers on the Me/Ag contacts, the excessive incorporation of oxygen molecules into the contact interfacial region, and the out-diffusion and agglomeration of Ag, were effectively prevented during oxidation annealing. As a result, a high reflectance of 87.2% at the 460 nm wavelength and a smooth surface morphology could be obtained simultaneously. (C) 2008 The Electrochemical Society.open111618sciescopu

Special Libraries, November 1962

Volume 53, Issue 9https://scholarworks.sjsu.edu/sla_sl_1962/1008/thumbnail.jp

Video classification based on spatial gradient and optical flow descriptors

Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. It is evaluated on three video datasets: KTH, YouTube, and Hollywood2. The results show that the proposed method achieves a higher classification rate, even when it uses only half the number of feature points compared to the dense sampling approach. Moreover, features extracted from the curvature of the motion surface are more discriminative than features extracted from the spatial gradient

Automatic Image Annotation for Semantic Image Retrieval

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images

Skin color detection for face localization in human-machine communications

This paper presents the proposed user interface design for computers whereby users can navigate in a 3D graphics scene and change camera viewpoint via head movement. This human-machine communication relies very much on the performance of its face localization module, which must determine head pose and track head movement. We have employed the skin color detection approach to face localization. The approach is studied and presented. The experimental results show that our chosen methodology is very effective. Furthermore, we demonstrate that skin color detection approach can cope with the variations of skin color and lighting condition