Finite automata state transitions are used to extract the features from the symbolised image. Texture image is divided into several partitions, i.e., texture, background of the texture, shadow of the texture, etc. This algorithm is developed based on the symbolic dynamics and finite state automata theory for estimating the state transition of the texture variations. Pattern analysis of the texture image is performed by proposing a symbolic pattern-based algorithm. Keshava ReddyĪddresses: CSE Department, JNTUA College of Engineering, Kalikiri, India ' JNTUA College of Engineering, Anantapur, A.P., India ' Maths Department, JNTUA College of Engineering, Anantapur, IndiaĪbstract: The paper proposes a complete modelling of finite state automata along with the associated classifier for texture classification. Eswara Reddy Ramireddy Obulakonda Reddy E. Title: Pattern analysis and texture classification using finite state automata schemeĪuthors: B. International Journal of Advanced Intelligence Paradigms.Inderscience Publishers - linking academia, business and industry through research The experimental study shows the better efficiency of the proposed system when compared to other existing methods. 99.12% classification accuracy is achieved when compared with other state-of-art techniques. Pattern analysis is performed on the KITH-TIPS dataset for ten varied categories of texture. A binary classifier is designed to classify the texture categories based on the feature extraction from the finite automata theory. FSMs are studied in the more general field of automata theory.Article: Pattern analysis and texture classification using finite state automata scheme Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2019 Vol.14 No.1/2 pp.30 - 45 Abstract: The paper proposes a complete modelling of finite state automata along with the associated classifier for texture classification. This is because a FSM's memory is limited by the number of states it has. The computational power distinction means there are computational tasks that a Turing machine can do but a FSM cannot. The finite state machine has less computational power than some other models of computation such as the Turing machine. Simple examples are vending machines, which dispense products when the proper combination of coins is deposited, elevators, whose sequence of stops is determined by the floors requested by riders, traffic lights, which change sequence when cars are waiting, and combination locks, which require the input of a sequence of numbers in the proper order. The behavior of state machines can be observed in many devices in modern society that perform a predetermined sequence of actions depending on a sequence of events with which they are presented. A deterministic finite-state machine can be constructed equivalent to any non-deterministic one. Finite state machines are of two types – deterministic finite state machines and non-deterministic finite state machines. An FSM is defined by a list of its states, its initial state, and the conditions for each transition. The FSM can change from one state to another in response to some external inputs and/or a condition is satisfied the change from one state to another is called a transition. It is an abstract machine that can be in exactly one of a finite number of states at any given time. (Clicking on each layer gets an article on that subject)Ī finite-state machine ( FSM) or finite-state automaton ( FSA, plural: automata), finite automaton, or simply a state machine, is a mathematical model of computation.