Aims Pattern recognition and classification are fundamental concepts for understanding living systems and essential for realising artificial intelligent systems. In the past decade, there has been a strong interest in the theory and applications of invariants to both pattern recognition and classification.
Invariants are properties that remain constant under a set of transformations.
A considerable number of novel geometric techniques and mathematical models have been used to extract and predict the existence of invariants. The majority of such techniques have been applied to 2D and 3D Computer Vision, such as affine curvatures, quadratic surfaces, Grassmann-Cayley algebra, linear algebra and tensor theory, Clifford algebra, Fourier descriptor and moment invariants, among many others. Applications of such techniques include shape recovery, 3D modelling, motion analysis, feature extraction, device positioning and calibration, feature recognition, etc. The last decade has also seen a rapidly growing interest in research studying the theory and applications of classifier systems. Many techniques have been explored and proposed as solutions to classification problems to industrial and medical applications.