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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.

/pdf/non-negative-matrix-factorization-with-sparseness-40ldavp7a5.pdf

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.

/pdf/probabilistic-independent-component-analysis-for-functional-2mvcmrc5z0.pdf

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.

/pdf/direct-least-squares-fitting-of-ellipses-43uc3fcv1n.pdf

Direct least squares fitting of ellipses

Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2016.0547

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle

The mechanics of the extraocular muscles and orbital tissue (“oculomotor plant”) can be approximated by a small number of viscoelastic (Voigt) elements in series. Recent analysis of the eye's retur...

Long Time-Constant Behavior of the Oculomotor Plant in Barbiturate-Anesthetized Primate

The cerebellum is thought to implement internal models for sensory prediction, but details of the underlying circuitry are currently obscure. We therefore investigated a specific example of internal-model based sensory prediction, namely detection of whisker contacts during whisking. Inputs from the vibrissae in rats can be affected by signals generated by whisker movement, a phenomenon also observable in whisking robots. Robot novelty-detection can be improved by adaptive noise-cancellation, in which an adaptive filter learns a forward model of the whisker plant that allows the sensory effects of whisking to be predicted and thus subtracted from the noisy sensory input. However, the forward model only uses information from an efference copy of the whisking commands. Here we show that the addition of sensory information from the whiskers allows the adaptive filter to learn a more complex internal model that performs more robustly than the forward model, particularly when the whisking-induced interference has a periodic structure. We then propose a neural equivalent of the circuitry required for adaptive novelty-detection in the robot, in which the role of the adaptive filter is carried out by the cerebellum, with the comparison of its output (an estimate of the self-induced interference) and the original vibrissal signal occurring in the superior colliculus, a structure noted for its central role in novelty detection. This proposal makes a specific prediction concerning the whisker-related functions of a region in cerebellar cortical zone A2 that in rats receives climbing fibre input from the superior colliculus (via the inferior olive). This region has not been observed in non-whisking animals such as cats and primates, and its functional role in vibrissal processing has hitherto remained mysterious. Further investigation of this system may throw light on how cerebellar-based internal models could be used in broader sensory, motor and cognitive contexts.

/pdf/an-internal-model-architecture-for-novelty-detection-4tn861jfyb.pdf

An internal model architecture for novelty detection: Implications for cerebellar and collicular roles in sensory processing

Interaction of stereo and texture cues in the perception of three-dimensional steps

We describe 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: 1) The recovery of a sparse depth map using edge based passive stereo triangulation. 2) The grouping, description and segmentation of edge segments to recover a 3D description of the scene geometry in terms of straight lines and circular arcs. 3) The 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. 4) The matching of 3D wireframe models to 3D scene descriptions, to recover an initial estimate of their position and orientation.

/pdf/tina-the-sheffield-aivru-vision-system-54kt6c69xr.pdf

John Porrill

Papers

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle

Long Time-Constant Behavior of the Oculomotor Plant in Barbiturate-Anesthetized Primate

An internal model architecture for novelty detection: Implications for cerebellar and collicular roles in sensory processing

Interaction of stereo and texture cues in the perception of three-dimensional steps

TINA: the sheffield AIVRU vision system