OpenGL ES 1.1 implementation based on OpenGL

In this paper, we present an efficient way of implementing OpenGL ES 1.1 3D graphics API library for the environments with hardware-supported OpenGL facility, typically as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it rapidly became the standard 3D graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functions and others. Currently, it is the official 3D graphics API for Google Android, Apple iPhone, Sony PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES 1.1. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a specialized memory management scheme to manage the converted data from the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different, and thus we used completely new software-implementations. Our final implementation of OpenGL ES library provides all of more than 200 functions in the standard specification and passed its conformance test, to show its compliance with the standard. From the efficiency point of view, we measured its execution times for several OpenGL ES-specific application programs and achieved remarkable improvements.     

AutoMed: an automated mediator for multi-issue bilateral negotiations

In this paper, we present AutoMed, an automated mediator for multi-issue bilateral negotiation under time constraints. AutoMed elicits the negotiators preferences and analyzes them. It monitors the negotiations and proposes possible solutions for resolving the conflict. We conducted experiments in a simulated environment. The results show that negotiations mediated by AutoMed are concluded significantly faster than non-mediated ones and without any of the negotiators opting out. Furthermore, the subjects in the mediated negotiations are more satisfied with the resolutions than the subjects in the non-mediated negotiations.     

Rigid body dynamics with a scalable body,quaternions and perfect constraints

In this paper, we present a formulation of the quaternion constraint for rigid body rotations in the form of a standard perfect bilateral mechanical constraint, for which the associated Lagrangian multiplier has the meaning of a constraint force. First, the equations of motion of a scalable body are derived. A scalable body has three translational, three rotational, and one uniform scaling degree of freedom. As generalized coordinates, an unconstrained quaternion and a displacement vector are used. To the scalable body, a perfect bilateral constraint is added, restricting the quaternion to unit length and making the body rigid. This way a quaternion based differential algebraic equation (DAE) formulation for the dynamics of a rigid body is obtained, where the 7×7 mass matrix is regular and the unit length restriction of the quaternion is enforced by a mechanical constraint. Finally, the equations of motion in the form of a DAE are linked to the Newton–Euler equations of motion of a rigid body. The rigid body DAE formulation is useful for the construction of (energy) consistent integrators.     

More than modelling and hiding: towards a comprehensive view of Web mining and privacy

Over the last decade, privacy has been widely recognised as one of the major problems of data collections in general and the Web in particular. This concerns specifically data arising from Web usage (such as querying or transacting) and social networking (characterised by rich self-profiling including relational information) and the inferences drawn from them. The data mining community has been very conscious of these issues and has addressed in particular the inference problems through various methods for “privacy-preserving data mining” and “privacy-preserving data publishing”. However, it appears that these approaches by themselves cannot effectively solve the privacy problems posed by mining. We argue that this is due to the underlying notions of privacy and of data mining, both of which are too narrow. Drawing on notions of privacy not only as hiding, but as control and negotiation, as well as on data mining not only as modelling, but as the whole cycle of knowledge discovery, we offer an alternative view. This is intended to be a comprehensive view of the privacy challenges as well as solution approaches along all phases of the knowledge discovery cycle. The paper thus combines a survey with an outline of an agenda for a comprehensive, interdisciplinary view of Web mining and privacy.     

Efficient compression and network adaptive video coding for distributed video surveillance

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.     

Capturing contextual relationship for effective media search

One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. In this paper, we assume the semantics of media is determined by the contextual relationship in a dataset, and introduce the method to capture the contextual information from a large media (especially image) dataset for effective search. Similarity search in an image database based on this contextual information shows encouraging experimental results.     

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.     

PhotoGeo: a photo digital library with spatial-temporal support and self-annotation

The recent popularity of digital cameras has posed a new problem: how to efficiently store and retrieve the very large number of digital photos captured and chaotically stored in multiple locations without any annotation. This paper proposes an infrastructure, called PhotoGeo, which aims at helping users with the people photo annotation, event photo annotation, storage and retrieval of personal digital photos. To achieve the desired objective, PhotoGeo uses new algorithms that make it possible to annotate photos with the key metadata to facilitate their retrieval, such as: the people who were shown in the photo (who); where it was captured (where); the date and time of capture (when); and the event that was captured. The paper concludes with a detailed evaluation of these algorithms.     

Tracking video objects with feature points based particle filtering

For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.