Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

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

A formalism is described for the statistical combination of geometrical information from multiple sensors. This is illus trated by its applications to stereo vision, including the com bination of multiple stereo views to increase the accuracy of a wire frame model and the consistent imposition of geometri cal constraints on such a model.

Optimal combination and constraints for geometrical sensor data

Two experiments are described in which the effects of scaling vertical disparities on the perceived amplitudes of dome-shaped surfaces depicted with horizontal disparities were examined. The Mayhew and Longuet-Higgins's theory and the regional-disparity-correction theory of Garding et al predict that scaling should generate a change in perceived depth appropriate to the viewing distance simulated by the scaled vertical disparities. Significant depth changes were observed, by means of a nulling task in which the vertical-disparity-scaling effect was cancelled by the observer choosing a pattern of horizontal disparities that made the dome-shaped surface appear flat. The sizes of the scaling effects were less than those predicted by either theory, suggesting that other cues to fixation distance such as oculomotor information played an appreciable role. In conditions in which 50% of the texture elements were given one value of vertical-disparity scaling and the remaining 50% were left unscaled, the size of the scaling effect on perceived depth could be accounted for by equally weighted pooling of the vertical-disparity information unless the two scalings were very dissimilar, in which case the lower scaling factor tended to dominate. These findings are discussed in terms of a Hough parameter estimation model of the vertical-disparity-pooling process.

Pooling of Vertical Disparities by the Human Visual System

The authors describe a deformable model of the human iris, which forms part of a system for accurate offline measurement of binocular eye movements, particularly cyclotorsion (torsion), from video image sequences. At least two existing systems measure torsion from infrared video images by pupil tracking followed by cross-correlation of bandpass filtered iris sectors. Unfortunately, pupil expansion and contraction reduce the accuracy of this method. In addition, infrared iris images typically contain very little texture, so correlation can be unreliable. A five-parameter deformable model of the iris was therefore developed for analysing images taken in visible light. This model can translate (horizontal and vertical eye motion), rotate (torsion) and scale both uniformly and radially (pupil changes). A series of software simulations and hardware tests suggest that torsion measurements obtained with the model are repeatable and accurate to within 0.1°. This performance is illustrated by analysing binocular torsion during fixation on a static target; the results match previously published data from other equipment.

Deformable model of the human iris for measuring ocular torsion from video images

Perception of shaded three-dimensional figures is inherently ambiguous, but this ambiguity can be resolved if the brain assumes that figures are lit from a specific direction. Under the Bayesian framework, the visual system assigns a weighting to each possible direction, and these weightings define a prior probability distribution for light-source direction. Here, we describe a non-parametric maximum-likelihood estimation method for finding the prior distribution for lighting direction. Our results suggest that each observer has a distinct prior distribution, with non-zero values in all directions, but with a peak which indicates observers are biased to expect light to come from above left. The implications of these results for estimating general perceptual priors are discussed.

/pdf/where-is-the-light-bayesian-perceptual-priors-for-lighting-5c3lpah9bp.pdf

Where is the light? Bayesian perceptual priors for lighting direction

The statistical combination of information from multiple sources is considered. The particular needs of the target application, stereo vision, require that the formulation be adequate to deal with highly correlated errors and constraints, and that it deal naturally with geometrical data.

John Porrill

Papers

Optimal combination and constraints for geometrical sensor data

Pooling of Vertical Disparities by the Human Visual System

Deformable model of the human iris for measuring ocular torsion from video images

Where is the light? Bayesian perceptual priors for lighting direction

Optimal combination of multiple sensors including stereo vision