Lecture 13,14,15 object recognition

Object Recognition Digital Image Processing Lecture 13,14,15 – Object Regconition Lecturer: Ha Dai Duong Faculty of Information Technology I Introduction „ „ The scope covered by out treatment of digital image processing to include recognition of individual image regions, which we called objects or patterns The approaches to pattern recognition are divided into two principal areas: ‰ ‰ Decision-theoretic: This catogory deals with patterns described using quantitative descriptors, such as length, area, texture … Structural: Deals with patterns best described by qualitative descriptors, such as the relational descriptors Digital Image Processing Object Recognition II Patterns and pattern classes „ „ A pattern is an arrangement of descriptors The name feature is used often in pattern recognition to denote a descriptor A pattern class is a family of patterns that share some common properties ‰ ‰ w1, w2, , wK denotes pattern classes, Where K is the number of classes Pattern recognition by machine involves techniques for assining patterns to their respective classes automatically (and with as little human intervention as possible) Digital Image Processing II Patterns and pattern classes „ Three common pattern arrangements used in practice are: ‰ ‰ „ Vectors: for quantitative descriptions Strings and trees: for qualitative descriptions Pattern vectors are represented by bold lowercase letters, such as z, y and z, and take a form or Digital Image Processing Object Recognition II Patterns and pattern classes „ Example: In a classic paper to recognize three types of iris flowers (Setosa, virginica, and versicolor) by measuring the widths and lengths of their petals Digital Image Processing II Patterns and pattern classes „ Another Example: We can form pattern vectors by letting x1=r(θ1),…xn=r(θn) The vectors became points in n-dimensions space Digital Image Processing Object Recognition II Patterns and pattern classes „ „ „ In some applications pattern characteristics are best described by structural relationships For example: fingerprint recognition is based on the interrelationships of print features Together with their relatives sizes and locations, these features are primitive components that describe fingerprint ridge properties, such as abrupt ending, branching, and disconnected segments Recognition problems of this type, in which not only quantitative mearsures about each feature but also the spatial relationships between the features determine class menbership, generally are best solved by structural approachs Digital Image Processing II Patterns and pattern classes „ Example Digital Image Processing Object Recognition II Patterns and pattern classes „ Example Digital Image Processing II Patterns and pattern classes „ Example Digital Image Processing 10 Object Recognition III Recognition Based on DecisionTheoretic Methods „ Decision-theoretic appoaches to recognition are based on the use of decision functions ‰ ‰ ‰ Let x=(x1, x2, , xn)T represent an n-dimensional pattern vector ω1, ω2, , ωW denote W pattern classes The basic problem in decision-theoretic pattern recognition is to find decision functions d1(x), d2(x), , dw(x) with property that, if pattern x belongs to class ωi then: Digital Image Processing 11 III Recognition Based on DecisionTheoretic Methods „ „ „ The decision boundary separating class ωi from ωj is given by values of x for which di(x)=dj(x), or equivalently, by value of x for which: Common practice is to identify the decision boundary between two classes by the single function dij(x)=di(x)-dj(x)=0 Thus dij(x)>0 for pattern of class ωi and dij(x)