1. An object location method, comprising: analysing data comprising a plurality of objects wherein each object exhibits a plurality of features, and extracting said features from said data; matching features stored in a database with those extracted from said data, and deriving a prediction of the object, wherein each feature extracted from the data provides a vote for at least one prediction; expressing the prediction to be analysed in a Hough space, wherein the objects to be analysed are described by n parameters and each parameter defines a dimension of the Hough space, where n is an integer of at least one; providing a constraint implemented by applying a higher weighting to votes which agree with votes from other features than those votes which do not agree with votes from other features; finding local maxima in the Hough space using the weighted votes; and identifying the predictions associated with the local maxima to locate the objects provided in the data, wherein the constraint is provided by minimising the information entropy of p(y|Xω,θ) with respect to θ, where y is the prediction of the object in Hough Space H which is the space of all object predictions, X={x ij } ∀i,j is a vote cast in H by N features, where i represents a feature, j represents a vote from the i th feature and ω={ω i } is a weight attributed to a feature and θ={θ ij } ∀i,j is a weight attributed to a vote.