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# 计算机代写|计算机视觉代写Computer Vision代考|CS766 Bayesian inference in vision. Classifiers; probabilistic methods

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## 计算机代写|计算机视觉代写Computer Vision代考|Bayesian inference in vision. Classifiers; probabilistic methods

It is virtually impossible to perform most computer vision tasks in a purely “bottom-up” fashion. Consider the following images, and how impoverished are the data which must support the task of object recognition!

An important “AI” perspective on vision is that vision is knowledge-driven. In this view, all of the front-end image processing is merely a distraction, if not an irrelevancy. What is really needed for vision is not a lot of theorems involving the 2D Fourier transform of the Laplacian of a Gaussian filter, but rather a good interface to an expert system that stores and indexes knowledge about such things as Dalmatian hounds and the general way that dogs behave when following a scent…

This section reviews the basic ideas behind Bayesian inference, which is a method fundamental to probability theory, statistics, and machine learning. Its purpose is to provide a means for integrating prior information (such as general knowledge about the sorts of things that populate the world, their properties and relationships, the metaphysics of objects, etc…) with empirical information gathered from incoming image data. This principle is expressed in the form of a basic rule for relating conditional probabilities in which the “antecedent” and “consequent” are interchanged. The value of this method for computer vision is that it provides a framework for continually updating one’s theory of what one is looking at, by integrating continuously incoming evidence with the best available inference or interpretation so far.

## 计算机代写|计算机视觉代写Computer Vision代考|Decisions under uncertainty

Most real-world tasks (whose solution requires intelligence) involve degrees of uncertainty. Decision-making under uncertainty is especially characteristic in computer vision. The sources of uncertainty may include:

the nature of the data or signals available

the inherent problem of classifying or recognizing them

the unpredictability of the future

the fact that objects and events have probabilities

the uncertainty of causation

the fact that associative knowledge is only probabilistic

the inherent incompleteness or imperfection of processing

possible undecidability of a problem, given all available data

the “ill-posed” nature of many tasks

inherent trade-offs such as speed versus accuracy
But despite these realities, decisions are required. The framework to adopt is that, in a sense, the world consists of probabilities, and that visual processing really amounts to computing probabilities and assigning them.

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