Book Review: Visual Motion of Curves and Surfaces

This is Dr. Ludu\u27s review of the book Visual Motion of Curves and Surfaces by Roberto Cipolla and Peter Giblin. Published by Cambridge University Press in 2009. ISBN: 978-0-521-63251-5

Use of 3D body motion to freeform surface design

This paper presents a novel surface modelling approach by utilising a 3D motion capture system. For designing a large-sized surface, a network of splines is initially set up. Artists or designers wearing motion markers on their hands can then change shapes of the splines with their hands. Literarily they can move their bodies freely to any positions to perform their tasks. They can also move their hands in 3D free space to detail surface characteristics by their gestures. All their design motions are recorded in the motion capturing system and transferred into 3D curves and surfaces correspondingly. This paper reports this novel surface design method and some case studies

Image based visual servoing using algebraic curves applied to shape alignment

Visual servoing schemes generally employ various image features (points, lines, moments etc.) in their control formulation. This paper presents a novel method for using boundary information in visual servoing. Object boundaries are modeled by algebraic equations and decomposed as a unique sum of product of lines. We propose that these lines can be used to extract useful features for visual servoing purposes. In this paper, intersection of these lines are used as point features in visual servoing. Simulations are performed with a 6 DOF Puma 560 robot using Matlab Robotics Toolbox for the alignment of a free-form object. Also, experiments are realized with a 2 DOF SCARA direct drive robot. Both simulation and experimental results are quite promising and show potential of our new method

Cortical depth dependent functional responses in humans at 7T: improved specificity with 3D GRASE

Ultra high fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution. This, along with improved hardware and imaging techniques, allow investigating columnar and laminar functional responses. Using gradient-echo (GE) (T2* weighted) based sequences, layer specific responses have been recorded from human (and animal) primary visual areas. However, their increased sensitivity to large surface veins potentially clouds detecting and interpreting layer specific responses. Conversely, spin-echo (SE) (T2 weighted) sequences are less sensitive to large veins and have been used to map cortical columns in humans. T2 weighted 3D GRASE with inner volume selection provides high isotropic resolution over extended volumes, overcoming some of the many technical limitations of conventional 2D SE-EPI, whereby making layer specific investigations feasible. Further, the demonstration of columnar level specificity with 3D GRASE, despite contributions from both stimulated echoes and conventional T2 contrast, has made it an attractive alternative over 2D SE-EPI. Here, we assess the spatial specificity of cortical depth dependent 3D GRASE functional responses in human V1 and hMT by comparing it to GE responses. In doing so we demonstrate that 3D GRASE is less sensitive to contributions from large veins in superficial layers, while showing increased specificity (functional tuning) throughout the cortex compared to GE

A single mechanism can explain the speed tuning properties of MT and V1 complex neurons

A recent study by Priebe et al., (2006) has shown that a small proportion (27%) of primate directionally selective, complex V1 neurons are tuned for the speed of image motion. In this study, I show that the weighted intersection mechanism (WIM) model, which was previously proposed to explain speed tuning in middle temporal neurons, can also explain the tuning found in complex V1 neurons. With the addition of a contrast gain mechanism, this model is able to replicate the effects of contrast on V1 speed tuning, a phenomenon that was recently discovered by Priebe et al., (2006). The WIM model simulations also indicate that V1 neuron spatiotemporal frequency response maps may be asymmetrical in shape and hence poorly characterized by the symmetrical two-dimensional Gaussian fitting function used by Priebe et al., (2006) to classify their cells. Therefore, the actual proportion of speed tuning among directional complex V1 cells may be higher than the 27% estimate suggested by these authors