This paper presents the use of a projector-based spatial augmented reality system in an industrial quality assurance setting to highlight spot-weld locations on vehicle panels for manual welding operators. The aim of this work is to improve the precision and accuracy of manual spot-weld placements with the aid of visual cues as a proactive step by the automotive manufacturer to enhance product quality. The prototype system was deployed at General Motors (GM) Holden plant in Elizabeth, Australia on the production line building Holden Cruze vehicles. Production trials were conducted and techniques developed to analyse and validate the precision and accuracy of spot-welds both with and without the visual cues. A reduction of 52 % of the standard deviation of manual spot-weld placement was observed when using augmented reality visual cues. The average standard deviation with-AR assistance (19 panels and 114 spot-welds) was calculated at 1.94 mm compared to without-AR (45 panels and 270 spot-welds) at 4.08 mm. All welds were within the required specification and panels evaluated in this study were used as the final product made available to consumers. The visual cues enabled operators to spot-weld at a higher degree of precision and accuracy.

This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The proposed methodology, which relies on Navier-Stokes equations, is used for processing fluid optical flow by using a succession of stages such as advection, diffusion and mass conservation. A robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework. The diffusion kernel is Gaussian with the covariance matrix defined by the local second derivatives. Such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly. A new approach is developed for detecting fluid flow structures such as vortices. The proposed methodology is applied on artificially generated vector fields as well as on various image sequences.

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This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement captures highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of three widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondences. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing. For patterned clothing techniques such as SIFT produce reliable correspondence.

This paper provides a comparison study among a set of novel algorithms that implement robust diffusion on optical flows. The proposed algorithms combine the anisotropic smoothing ability of the heat kernel and the outlier rejection mechanism of robust statistics algorithms. The diffusion kernel is considered Gaussian, where the covariance matrix is the local Hessian. This enables the kernel to detect significant transitions in the signal. In this study we show that diffusion does not eliminate outliers but rather spreads them around. We calculate the resulting bias induced by diffusing the outliers in their neighbourhood. On the other hand robust statistics operators reject the outliers from the diffusion process. Alpha-trimmed mean and median statistics are considered in combination with the diffusion processing. The proposed algorithms are applied for smoothing optical flow.

This paper proposes a physics-based methodology for the analysis of optical flows displaying complex patterns. Turbulent motion, such as that exhibited by fluid substances, can be modelled using fluid dynamics principles. Together with supplemental equations, such as the conservation of mass, and well formulated boundary conditions, the Navier-Stokes equations can be used to model complex fluid motion estimated from image sequences. In this paper, we propose to use a robust kernel which adapts to the local data geometry in the diffusion stage of the Navier-Stokes formulation. The proposed kernel is Gaussian and embeds the Hessian of the local data as its covariance matrix. The local Hessian models the variation of the flow in a certain neighbourhood. Moreover, we use a robust statistics mechanism in order to eliminate the outliers from the estimation process. The proposed methodology is applied on artificial vector fields and in image sequences showing atmospheric and solar phenomena.