Duoscope

The ambiguous representation of spatial depth in Thornton Walker’s painting The Homage creates a peculiar sense in which the ‘whereness’ of depicted objects and atmosphere cannot be ascertained by, either perspectival convention or perceptual strategies. This visual-spatial ambiguity resonates with my interested in ‘broken’ stereography. Hence, ‘duoscopy’ refers to the limitations of binocular vision when the object of perception is itself duplicitous.

Real-time measurement of radiocarpal joint angle during dart-thrower's movement

Recently, a renewed attention has been drawn into the dart-thrower's motion as the radiocarpal joint is unique to humans and this is believed to have played a pivotal role in human evolution. Considering the importance of the motion and the complexity of the wrist joint, there have been many articles discussing the kinematics behind this movement. CT scan techniques have been used in a number of these research activities. Due to limitations in the speed of the image acquisition, the positions of the wrist were recorded in static postures. To our knowledge, a data acquisition for the motion with realtime capturing has not been reported. This paper presents the use of a 3D vision-based motion capture device. Leap Motion Controller (LMC), for measuring the radiocarpal joint angles during the dart-thrower's motion in a real-time analysis. The practical capability of the LMC in measuring dart-thrower's motion was examined in a trial involving four subjects and the angles were compared to the angles acquired from an inertial measurement unit (IMU). The results confirmed the LMC can successfully be used in the application of measuring radiocarpal kinematics" of dart-thrower's motion.

3D depth estimation using wavelet analysis based stereo vision approach

A complete and highly robust 3D reconstruction algorithm based on stereo vision is presented. The developed system is capable of reconstructing dimensionally accurate 3D models of the objects and is very simple and cost effective due to its prominent software dependency and minimal hardware involvevment unlike existing systems.

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.

Predatory vision : 3D imaging and the transformation of screen-space

Despite Wheatstone’s academic interests in the device, the stereoscope languished somewhat as an optical toy. Yet the advent of 3D screen-spaces for home and mass entertainment suggests today’s consumers and practitioners of screen culture hold the view that screen culture will be ‘improved’ through 3D imaging technologies. Like cinema and photography, stereoscopic 3D imaging has the potential to transform visual culture. But what is transformed, as optics and electronic imaging techniques deliver Alice in Wonderland in 3D? This paper links the advent of 3D cinema and TV to the notion that vision is itself a ‘technology of the visual’. As such, our innate binocular stereoacuity is ripe for exploitation by developers of 3D imaging technologies. I argue that contemporary 3D imaging marks an epistemological visual-perceptual shift: toward screenspaces becoming spaces for potential action. Such a shift entails seeing as doing rather than seeing as thinking. 3D imaging exploits binocular vision’s spatial acuity (stereopsis), but is effective only for objects within near distal space. The 3D effect tapers off dramatically for objects only some metres away, because the two retinal images lack significant lateral disparity (difference) to trigger stereopsis: the imagery flattens out and becomes ‘monoscopic’. Information available from conventional 2D media entails a peculiarly unspecified spatiality. Perceptually, the contents of a conventional cinematic screen are like those of a painting: they are situated neither near nor far, and constitute a shared and ambiguous visual space. Our own eyes are like those of a cat: frontally placed for predatory action. The visuality of 3D screen-spaces assumes a perceptuality of the near-by and close at hand, since this is the structure of the visible information to which stereopsis is adapted to respond. Noting the binocular acuity of predatory animals, as well as some etymological links, this paper examines the implications of perceptually ‘capturing’ the sensation of visually solid objects in one’s immediate space. Stereopsis is about decisive action within an immediate environment: but it also presupposes the single viewpoint of an active observer toward which the 3D imagery is targeted.

Fast vision-based catheter 3D reconstruction

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters, where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors can not be employed in the way they are in rigid robotic manipulators. In this paper, a high speed vision-based shape sensing algorithm for real-time 3D reconstruction of continuum robots based on the views of two arbitrary positioned cameras is presented. The algorithm is based on the closed-form analytical solution of the reconstruction of quadratic curves in 3D space from two arbitrary perspective projections. High-speed image processing algorithms are developed for the segmentation and feature extraction from the images. The proposed algorithms are experimentally validated for accuracy by measuring the tip position, length and bending and orientation angles for known circular and elliptical catheter shaped tubes. Sensitivity analysis is also carried out to evaluate the robustness of the algorithm. Experimental results demonstrate good accuracy (maximum errors of  ±0.6 mm and  ±0.5 deg), performance (200 Hz), and robustness (maximum absolute error of 1.74 mm, 3.64 deg for the added noises) of the proposed high speed algorithms.

Depth estimation using multi-wavelet analysis based stereo vision approach

The problem of dimensional defects in aluminum die- casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo cameras pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

Depth estimation using multiwavelet analysis based stereo vision approach

The problem of dimensional defects in aluminum die-casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo camera pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

3D leadership for national renewal

A perpetual immigrant, Professor Frederick relates his life course that brought him to New Zealand and describes what he found here. One of the country’s early advocates of the “knowledge economy” path to economic development, Frederick outlines his vision of leadership for the new millennium that will help restore New Zealand to the top half of the OECD and grow the cake for the prosperity of all. He relates what we expect from our leaders as well as his personal vision to leadership in New Zealand.

Human action recognition based on 3D SIFT and LDA model

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

Hypothesis verification using parametric models and active vision strategies

This paper proposes a methodology for determining the shape and ultimately the functionality of objects from intensity images; 2D analytic functions are used to track 3D features during known camera motions. Three analytic functions are proposed that describe the relationship between pairs of points that are either stationary or moving depending on whether the points are on occluding boundaries or otherwise. Many of the problems of correspondence are reduced by using foveation, known camera motion, and active vision methods. The three analytic functions are shown to enable hypothesis refinement of the functionality of a number of 3D objects without full 3D information about the shape.

Obtaining functional parametric models using active vision strategies

This paper addresses the problem of determining which 3D shape is present, and more importantly, the dimensions of the shape in a scene. This is performed in an active vision system because it reduces the complexity of the problem through the use of gaze stabilization, choice of foveation point, and selective processing by adaptively processing regions of interest. In our case, only a small number of equations and parameters are needed for each shape and these are incorporated into functional descriptions of the shapes.

Emerging hypothesis verification using function-based geometric models and active vision strategies

This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is that the camera can foveate and track particular features. It is argued that simple 2D analytic descriptions of the movement of edges can infer 3D shape while the camera is moved. This uses an advantage of foveation i.e. the problem becomes object centred. The problem of correspondence for numerous edge points is overcome by the use of a tree based representation for the competing hypotheses. Numerous hypothesis are maintained simultaneously and it does not rely on a single kinematic model which assumes constant velocity or acceleration. The numerous advantages of this strategy are described.

Obtaining function models using active vision strategies

This paper addresses the problem of determening which 3D shape is present, and more importantly, the dimensions of the shape within a scene. This is performed in an active vision system because it reduces the complexity of the problem through the use of gaze stabilisation, choice of foveation point and selective processing by adaptively processing regions of interest. In our case only a small number of equations and parameters are needed for each shape. For example, a container has width and height. These are incorporated into functional descriptions of the shapes.