New PDF release: Computer Vision: Models, Learning, and Inference

By Simon J. D. Prince

ISBN-10: 1107011795

ISBN-13: 9781107011793

This contemporary remedy of machine imaginative and prescient makes a speciality of studying and inference in probabilistic versions as a unifying topic. It exhibits the way to use education facts to profit the relationships among the saw photograph information and the facets of the area that we want to estimate, akin to the 3D constitution or the item category, and the way to use those relationships to make new inferences concerning the international from new picture information. With minimum must haves, the booklet starts off from the fundamentals of likelihood and version becoming and works as much as actual examples that the reader can enforce and regulate to construct priceless imaginative and prescient platforms. basically intended for complicated undergraduate and graduate scholars, the distinct methodological presentation can be priceless for practitioners of machine vision.

- Covers state-of-the-art thoughts, together with graph cuts, computing device studying, and a number of view geometry.
- A unified method exhibits the typical foundation for ideas of significant computing device imaginative and prescient difficulties, resembling digicam calibration, face attractiveness, and item tracking.
- greater than 70 algorithms are defined in enough element to implement.
- greater than 350 full-color illustrations magnify the text.
- The remedy is self-contained, together with the entire historical past mathematics.
- extra assets at

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Extra resources for Computer Vision: Models, Learning, and Inference

Sample text

For each case, we will investigate using both generative and discriminative models. At this stage, we won’t present the details of the learning and inference algorithms; these are presented in subsequent chapters anyway. The goal here is to introduce the main types of model used in computer vision, in their most simple form. 3 Example 1: Regression Consider the situation where we make a univariate continuous measurement x and use this to predict a univariate continuous state w. For example, we might predict the distance to a car in a road scene based on the number of pixels in its silhouette.

To calculate the likelihood function θ i=1 P r(xi |θ) at a single data point xi , we simply evaluate the probability density function at xi . I |θ) for a set of points is the product of the individual likelihoods. 1) i=1 where argmaxθ f [θ] returns the value of θ that maximizes the argument f [θ]. To evaluate the predictive distribution for a new data point x∗ (compute the probability that x∗ belongs to the fitted model), we simply evaluate the probability density ˆ using the ML fitted parameters θ.

This is typical of Bayesian solutions: they are more moderate (less certain) in their predictions. In the MAP case, erroneously committing to a single estimate of µ and σ 2 causes overconfidence in our future predictions. 9 a) Categorical probability distribution over six discrete values with parameters {λk }6k=1 where 6k=1 λk = 1. This could be the relative probability of a biased die landing on its six sides. b) Fifteen observations {xi }Ii=1 randomly sampled from this distribution. We denote the number of times category k was observed by Nk so that here the total observations 6 k=1 Nk = 15.

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Computer Vision: Models, Learning, and Inference by Simon J. D. Prince

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