A method for stereo-vision-based tracking for robotic applications

Vision-based tracking of an object using perspective projection inherently results in non-linear measurement equations in the Cartesian coordinates. The underlying object kinematics can be modelled by a linear system. In this paper we introduce a measurement conversion technique that analytically transforms the non-linear measurement equations obtained from a stereo-vision system into a system of linear measurement equations.We then design a robust linear filter around the converted measurement system. The state estimation error of the proposed filter is bounded and we provide a rigorous theoretical analysis of this result. The performance of the robust filter developed in this paper is demonstrated via computer simulation and via practical experimentation using a robotic manipulator as a target. The proposed filter is shown to outperform the extended Kalman filter (EKF).

'Matching' the club to the sponsor : perceptions of Australian Football League club sponsorship managers

Corporate organisations spend significant amounts of money on sponsorship in an attempt to achieve distinct marketing and overall business objectives. There is strong support within the literature that a corporate organisation will be more attracted to sponsoring a sport organisation when there is a ‘match’ between the characteristics of the organisations. This capacity to ‘match’ characteristics has been investigated predominantly from the corporate sponsors’ perspective. The focus of this research is to identify this organisational ‘match’ from an Australian Football League (AFL) sponsorship manager’s perspective. A series of semi-structured in-depth interviews were conducted with the sponsorship/marketing managers from seven Melbourne-based AFL clubs. Arising from this, the importance of ‘organisation match’ as a precursor for sponsorship was determined. Results indicate that AFL sponsorship managers believe that presenting their team as an organisational ‘match’ with a potential sponsor is extremely important, providing scope for better synergies and greater strategic partnerships. This is strongly aligned through the off-field culture that the team portrays as well as the on-field success that the team has. Sponsors seeking sport organisations that have clearly defined and communicated images and brands can therefore attain a source of competitive advantage in competitive sponsorship markets.

A novel bipolar time-spread echo hiding based watermarking method for stereo audio signals

In this paper, a novel bipolar time-spread (TS) echo hiding based watermarking method is proposed for stereo audio signals, to overcome the low robustness problem in the traditional TS echo hiding method. At the embedding, echo signals with opposite polarities are added to both channels of the host audio signal. This improves the imperceptibility of the watermarking scheme, since added watermarks have similar effects in both channels. Then decoding part is developed, in order to improve the robustness of the watermarking scheme against common attacks. Since these novel embedding and decoding methods utilize the advantage of two channels in stereo audio signals, it significantly reduces the interference of host signal at watermark extraction which is the main reason for error detection in the traditional TS echo hiding based watermarking under closed-loop attack. The effectiveness of the proposed watermarking scheme is theoretically analyzed and verified by simulations under common attacks. The proposed echo hiding method outperforms conventional TS echo hiding based watermarking when their perceptual qualities are similar.

Touching space : using motion capture and stereo projection to create a virtual haptics of dance

This paper describes the work of a group of artists in Australia who used real-time motion capture and 3D stereo projection to create a large-scale performance environment in which dancers seemed to "touch" the volume. This project re-versions Suzanne Langer's 1950s philosophy of dance as "virtual force" to realize the idea of a "virtual haptics" of dance that extends the dancer's physical agency literally across and through the surrounding spatial volume. The project presents a vision of interactive dance performance that "touches" space by visualizing kinematics as intentionality and agency. In doing so, we suggest the possibility of new kinds of human-computer interfaces that emphasize touch as embodied, nuanced agency that is mediated by the subtle qualities of whole-body movement, in addition to more goal-oriented, task-based gestures such as pointing or clicking.

Graph matching with subdivision surfaces for texture synthesis on surfaces

Existing texture synthesis-from-example strategies for polygon meshes typically make use of three components: a multi-resolution mesh hierarchy that allows the overall nature of the pattern to be reproduced before filling in detail; a matching strategy that extends the synthesized texture using the best fit from a texture sample; and a transfer mechanism that copies the selected portion of the texture sample to the target surface. We introduce novel alternatives for each of these components. Use of p2-subdivision surfaces provides the mesh hierarchy and allows fine control over the surface complexity. Adaptive subdivision is used to create an even vertex distribution over the surface. Use of the graph defined by a surface region for matching, rather than a regular texture neighbourhood, provides for flexible control over the scale of the texture and allows simultaneous matching against multiple levels of an image pyramid created from the texture sample. We use graph cuts for texture transfer, adapting this scheme to the context of surface synthesis. The resulting surface textures are realistic, tolerant of local mesh detail and are comparable to results produced by texture neighbourhood sampling approaches.

A robust solution to the stereo-vision-based simultaneous localization and mapping problem with steady and moving landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot-landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.

3D sparse feature model using short baseline stereo and multiple view registration

This paper outlines a methodology to generate a distinctive object representation offline, using short-baseline stereo fundamentals to triangulate highly descriptive object features in multiple pairs of stereo images. A group of sparse 2.5D perspective views are built and the multiple views are then fused into a single sparse 3D model using a common 3D shape registration technique. Having prior knowledge, such as the proposed sparse feature model, is useful when detecting an object and estimating its pose for real-time systems like augmented reality.

Echo hiding based stereo audio watermarking against pitch-scaling attacks

In audio watermarking, the robustness against pitch-scaling attack, is one of the most challenging problems. In this paper, we propose an algorithm, based on traditional time-spread(TS) echo hiding based audio watermarking to solve this problem. In TS echo hiding based watermarking, pitch-scaling attack shifts the location of pseudonoise (PN) sequence which appears in the cepstrum domain. Thus, position of the peak, which occurs after correlating with PN-sequence changes by an un-known amount and that causes the error. In the proposed scheme, we replace PN-sequence with unit-sample sequence and modify the decoding algorithm in such a way it will not depend on a particular point in cepstrum domain for extraction of watermark. Moreover proposed algorithm is applied to stereo audio signals to further improve the robustness. Experimental results illustrate the effectiveness of the proposed algorithm against pitch-scaling attacks compared to existing methods. In addition to that proposed algorithm also gives better robustness against other conventional signal processing attacks.

Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.

Generating comprehensible summaries of rushes sequences based on robust feature matching

This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.

Linking identities and viewpoints in home movies based on robust feature matching

The identification of useful structures in home video is difficult because this class of video is distinguished from other video sources by its unrestricted, non edited content and the absence of regulated storyline. In addition, home videos contain a lot of motion and erratic camera movements, with shots of the same character being captured from various angles and viewpoints. In this paper, we present a solution to the challenging problem of clustering shots and faces in home videos, based on the use of SIFT features. SIFT features have been known to be robust for object recognition; however, in dealing with the complexities of home video setting, the matching process needs to be augmented and adapted. This paper describes various techniques that can improve the number of matches returned as well as the correctness of matches. For example, existing methods for verification of matches are inadequate for cases when a small number of matches are returned, a common situation in home videos. We address this by constructing a robust classifier that works on matching sets instead of individual matches, allowing the exploitation of the geometric constraints between matches. Finally, we propose techniques for robustly extracting target clusters from individual feature matches.