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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

Biological systems have evolved robust, adaptive control strategies to deal with a wide range of control tasks in time varying systems and environments. The cerebellum is the brain structure particularly associated with the control of skilled movements, the advantageous properties of the cerebellum can be exploited for robotic control applications. In this contribution we present a bioinspired cerebellar control algorithm. We extend the existing cerebellar inspired adaptive filter control algorithm, previously applied to plants of specific order, to the control of general \(n^\mathrm{th }\) order plants. This is done by augmenting the existing cerebellar algorithm with a reference model, a technique used in model reference adaptive control. This augmented cerebellar controller is applied successfully to the simulated control of a general plant, and to the real time control of a dielectric electroactive polymer actuator. This augmented biomimetic control strategy has promise for the control of human-centred robots operating in unstructured environments.

Developing the Cerebellar Chip as a General Control Module for Autonomous Systems

In this paper a planar curve segmentation method based on the analysis of curvature and mean polygonal area is proposed. A rough segmentation of the curve is first produced by a high/low curvature classifier and then refined by a high/low/zero classifier and a knot/straight/convex/concave shape identifier so as to obtain a set of critical knots and shape descriptors for all segments. A straight line or conic approximation is then applied to each segment to generate a symbolic representation of the curve.

/pdf/segmentation-of-planar-curves-using-local-and-global-2dhluxgsw8.pdf

Segmentation of planar curves using local and global behaviour analysis.

System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue.

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System Identification From Multiple Short-Time-Duration Signals

There is substantial evidence that the repeating cerebellar microcircuit implements an adaptive filter algorithm suitable for fine tuning a wide range of sensorimotor skills [1]. Although it is known that the multimodal map layer of superior colliculus receives a massive cerebellar input which directly influences collicular output cells [2], there has been little speculation as to the function of this influence. We suggest here that these cerebellar inputs play a role in calibrating the accuracy of collicular topographic maps.

The cerebellum is a plausible candidate for this role for a number of reasons. It is known to be crucial for accuracy of saccades generated by colliculus. It is also known that cerebellar lesions impair prism adaptation of eye movements. A recent imaging study [3] found direct evidence of cerebellar activation during prism adaptation and suggests that "the cerebellum is particularly involved in [establishing] a correct spatial mapping among visuomotor and sensorimotor coordinates systems". A modeling study [3] of collicular visuomotor mapping concludes that the transformation occurs in brainstem with the cerebellum adjusting it for accuracy, stating that "... the importance of the cerebellum has been neglected in previous modeling studies".

Here we investigate whether the adaptive filter model of the cerebellar microcircuit which has been so successful in conventional sensorimotor contexts can be applied without change to the very different computational problem of calibrating a topographic map driving an orienting response. We propose a model in which (i) unimodal sensory topographic maps constitute a probabilistic representation of target position and that these maps are optimally combined to produce a multimodal map [4] whose peak activity drives the orienting response, (ii) cerebellar input can bias the position of peak map activity (e.g. by a process such as attentional gain modulation [5]), (iii) the cerebellum receives the sensory information that generates the topographic maps on its mossy fibre inputs, and (iv) information about orienting errors caused by miscalibration is made available to the cerebellum on its climbing fibre inputs and drives cerebellar learning.

We demonstrate in simulation that this mechanism can successfully calibrate topographic maps and go on to investigate its computational properties. For example we investigate a fundamental calibration ambiguity in which sensory maps can be miscalibrated in such a way that their effects on the combined estimate cancel. We show that this ambiguity is resolved in a plausible way if error signals are gated whenever a sensor fails to observe the target; a similar gating process has been observed in some motor behaviors [6].We also demonstrate that optimality of the cerebellar learning rule [7] ensures that Purkinje cell synapses carrying cross-talk between sensors are driven to silence, greatly reducing the need to hard-wire the connectivity of parallel fiber inputs to the cerebellar microzones.

https://link.springer.com/content/pdf/10.1186%2F1471-2202-11-S1-P120.pdf

Can cerebellar input calibrate collicular topographic maps

This paper describes the preliminary stages in the development of a predictive feed-forward (PFF) stereo based tracking module. The object of the module is to exploit the spatio-temporal coherence that exists in a sequence of stereo images in the context of providing a visual control mechanism for a mobile vehicle with uncertainty in position. PFF provides a method by which the representation of a 3D scene can be maintained and evolved over time. Furthermore, quickening strategies can utilise the spatio-temporal coherence by exploiting previously obtained depth values and approximate trajectory information in order to accelerate the process that actually achieves the stereo correspondences. Much research in computer vision has been developed in snapshot mode, concentrating attention on a single image or a small number of frames obtained either in synchrony or from a short movie sequence, the computational overheads involved in the analysis of a long sequence of images having proved prohibitive. It has been assumed, albeit implicitly, that algorithms developed in this way would, given appropriate parallel computer architectures, eventually be able to perform in real time on continuous image sequences. That is, the whole process involved in the recovery of scene descriptions would begin afresh upon each image frame and that some [not fully defined] extra module would be responsible for maintaining an evolving model of the environment based upon these descriptions. In the context of the control of a mobile robotic vehicle it is important to distinguish between the twin goals of obtaining an accurate model of the environment and determining the current position within it. Hence dynamic vision can be decomposed into [at least] two important modules: 1) The maintenance of an accurate and, as far as possible, topologically complete scene model. This will include: the combination of multiple views [1,2] to give more complete and robust data; the identification of and inclusion of novel (not previously seen) features; the determination of free space within which the robot can move. 2) The use of visual tracking to provide the control signal required to navigate an autonomous vehicle through an unstructured/partially structured environment using as beacons a subset of the scene features (perhaps identified outside the tracking module itself). The visual through-put and temporal response required by each task is very different. For example, when using visual feed back as a control mechanism it will be necessary to provide a much higher sample rate than that at which the model of the environment needs to be updated. Furthermore, the actual elapsed time

/pdf/predictive-feed-forward-stereo-processing-chyyeklw8d.pdf

John Porrill

Papers

Developing the Cerebellar Chip as a General Control Module for Autonomous Systems

Segmentation of planar curves using local and global behaviour analysis.

System Identification From Multiple Short-Time-Duration Signals

Can cerebellar input calibrate collicular topographic maps

Predictive Feed Forward Stereo Processing