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Multiple View Geometry in Computer Vision.

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of 'sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems.

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Non-negative Matrix Factorization with Sparseness Constraints

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

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Probabilistic independent component analysis for functional magnetic resonance imaging

This paper presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b/sup 2/=1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: 1) it is ellipse-specific so that even bad data will always return an ellipse; 2) it can be solved naturally by a generalized eigensystem, and 3) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other nonellipse-specific approaches would fail or require computationally expensive iterative refinements.

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Direct least squares fitting of ellipses

Despite their importance for deciphering oculomotor commands, the mechanics of the extraocular muscles and orbital tissues (oculomotor plant) are poorly understood. In particular, the significance of plant nonlinearities is uncertain. Here primate plant dynamics were investigated by measuring the eye movements produced by stimulating the abducens nucleus with brief pulse trains of varying frequency. Statistical analysis of these movements indicated that the effects of stimulation lasted about 40 ms after the final pulse, after which the eye returned passively toward its position before stimulation. Behavior during the passive phase could be approximated by a linear plant model, corresponding to Voigt elements in series, with properties independent of initial eye position. In contrast, behavior during the stimulation phase revealed a sigmoidal relation between stimulation frequency and estimated steady-state tetanic tension, together with a frequency-dependent rate of tension increase, that appeared very similar to the nonlinearities previously found for isometric-force production in primate lateral rectus muscle. These results suggest that the dynamics of the oculomotor plant have an approximately linear component related to steady-state viscoelasticity and a nonlinear component related to changes in muscle activation. The latter may in part account for the nonlinear relations observed between eye-movement parameters and single-unit firing patterns in the abducens nucleus. These findings point to the importance of recruitment as a simplifying factor for motor control with nonlinear plants.

https://www.physiology.org/doi/pdf/10.1152/jn.91045.2008

Dynamics of Primate Oculomotor Plant Revealed by Effects of Abducens Microstimulation

Constrained Active Region Models for Fast Tracking in Color Image Sequences

This chapter will present techniques for the recovery of component spatial and temporal modes from spatio-temporal data sets, in particular from medical imaging data such as that obtained by functional magnetic resonance imaging functional magnetic resonance imaging (fMRI) and optical imaging optical imaging (OI) of brain activity. These techniques were developed in order to help address some current issues involving the nature of the haemodynamic response to neural activity (for more details of this research and further references see [7]).

Analysis of Optical Imaging Data Using Weak Models and ICA

Edge-based binocular correspondence produces a sparse disparity map, available information being distributed along space curves which project to matched image edges. To become useful, these contours must be parsed into describable sections. A novel view of the segmentation/ description process is presented and an effective algorithm based on the model is described.

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Segmentation and description of binocularly viewed contours

The paper describes the Sheffield AIVRU 3D vision system for robotics. The system currently supports model-based object recognition and location; its potential for robotics applications is demonstrated by its guidance of a UMI robot arm in a pick-and-place task. The system comprises: recovery of a sparse depth map using edgebased passive stereo triangulation; grouping, description and segmentation of edge segments to recover a 3D description of the scene geometry in terms of straight lines and circular arcs; statistical combination of 3D descriptions for the purpose of object model creation from multiple stereo views, and the propagation of constraints for within-view refinement; and matching 3D wireframe models to 3D scene descriptions to recover an initial estimate of their position and orientation.

John Porrill

Papers

Dynamics of Primate Oculomotor Plant Revealed by Effects of Abducens Microstimulation

Constrained Active Region Models for Fast Tracking in Color Image Sequences

Analysis of Optical Imaging Data Using Weak Models and ICA

Segmentation and description of binocularly viewed contours

TINA: A 3D Vision System for Pick and Place.