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Nowadays, optimal and intelligent design approaches are vital in almost all areas of engineering. Scientists and engineers are attempting to make frameworks and models more proficient and intelligent. This paper deals with a detailed investigation on design of various digital filters using optimization algorithms. Generally digital filters are classified into two types which are FIR and IIR filters and are again classified into one dimensional, two dimensional and three dimensional filters for signal, image and video respectively. The design of a digital filter that satisfies all the required conditions perfectly is a challenging factor. So, apart from the conventional mathematical methods, optimization algorithms can be used to design optimal digital filters. IIR Filters are infinite impulse response filter; they have impulse response of infinite duration. FIR Filters are finite impulse response filters; they have impulse response of finite duration. In this paper we have discussed the design of various optimal digital filters based on various optimization algorithms, for processing of signal, image and video. The design of digital filters based on Evolutionary algorithms and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Cuckoo Search Algorithm, Differential Evolution, Gravitational Search, Harmony Search, Spiral Optimization, teaching–learning based optimization, wind driven optimization, hybridization of optimization algorithm are presented.

Indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cell as substrates is recommanded in anti nuclear antibody (ANA) test. These IIF slides are observed under microscope by pathologists to prepare the report. So, the ANA test is subjective and requires systematic automation. This paper proposes a novel algorithm for HEp-2 cellcategorization, which can be implanted in the ANA test automation system. A hybrid descriptor which represents the textural and morphological characters of the objects of interest was used along with Binary tree. The hybrid descriptors were generated by decomposing the image into binary images by using lower and upper thresholds. The performance of the proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset, the results concluded that Hybrid Descriptor with Binary Tree approach achieved the best performance with “97.5%” mean class accuracy.