Machine Recognition in Complex Domain

  • Bipin Kumar TripathiEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 571)


Machine recognition has drawn considerable interest and attention from researches in intelligent system design and computer vision communities over the recent past. Understandably there are a large number of commercial, law enforcement, control and forensic applications to this. We human beings have natural ability to recognize persons at a glance. Motivated by our remarkable ability, a series of attempts [1, 2, 3, 4] have been made to simulate this ability in machines. The development of human recognition system in machines is quite difficult because the natural objects are complex, multidimensional, and corresponds to environmental changes [3, 5, 6, 7]. There are two important issues that need to be addressed in machine recognition: (1) how the features are adopted to represent an object under environmental changes and (2) how we classify an object image based on a chosen representation. Over the years, researches have developed a number of methods for feature extraction and classification. All of these, however, have their own merits and demerits. Most of the work is related to the real domain. The outperformance of complex-valued neuron over conventional neuron has been well established in previous chapters. Few researchers have recently tried multivariate statistical techniques in the complex domain, like complex principal component analysis (PCA) for 2D vector field analysis [8] and complex independent component analysis (ICA) for performing source separation on functional magnetic resonance imaging data [9, 10]. But, no attempts have been made to develop techniques for feature extraction using their concepts. This chapter presents formal procedures for feature extraction using unsupervised learning techniques in complex domain. Efficient learning and better precision in result offered by feature extractor and classifier, considering simulations in complex domain, figure out their technical benefits over conventional methods. Notably, the success of machine recognition is limited by variations in features resulting from the natural environment. These may be due to instrument distortion, acquisition in an outdoor environment, different noises, complex background, occlusion and illumination. A solid set of examples presented in this chapter demonstrate the superiority of feature representation and classification in complex domain.


Feature Extraction Independent Component Analysis Independent Component Analysis Complex Domain False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

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