Image Analysis and Computer Vision

Intelligence has been defined in many ways like logic, awareness, reasoning, critical thinking, etc Many researchers insist on the possibility of a Technological Singularity shortly, which may see

Intelligence in cyberspace: the road to cyber singularity

Humans often mimic nature in the development of new machines or systems. The human brain, particularly its faculty for perception and cognition, is the most intriguing model for developing intelligent systems. Human cognitive processes have a great tolerance for imprecision or uncertainty. This is of great value in solving many engineering problems as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either uncertainties arising from the random behavior of physical processes or uncertainties arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing the latter. The methodology of computing with words and the computational theory of perceptions are branches of fuzzy logic that deal with the manipulation of words that act as labels for perceptions expressed in natural language propositions. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems.

Uncertainty in Computational Perception and~Cognition

Human cognitive and perception processes have a great tolerance for imprecision or uncertainty. For this reason, the notions of perception and cognition have great importance in solving many decision making problems in engineering, medicine, science, and social science as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either type one uncertainty arising from the random behavior of physical processes or type two uncertainty arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing type two uncertainty. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems. Fuzzy logic is needed to properly pose and answer queries about quantitatively defining imprecise linguistic terms like middle class, poor, low inflation, medium inflation, and high inflation. Imprecise terms like these in natural languages should be considered to have qualitative definitions, quantitative definitions, crisp quantitative definitions, fuzzy quantitative definitions, type-one fuzzy quantitative definitions, and interval type-two fuzzy quantitative definitions. There can be crisp queries, crisp answers, type-one fuzzy queries, type-one fuzzy answers, interval type-two fuzzy queries, and interval type-two fuzzy answers.

Important New Terms and Classifications in Uncertainty and Fuzzy Logic

Image segmentation allows mapping of similar regions in a scene leading to recognition of distinct objects by high-level vision systems. Therefore an efficient technique of clustering is a natural choice for image segmentation. Recognition of similar patterns embedded in image data is the basis of clustering subregions in an image. Efforts to develop algorithms for adaptive and less computationally com­plex classification of data have led to implementation of statistical classifiers in artificial neural networks (of both supervised and unsupervised categories). Such neural network architectures are ways to achieve autonomous processing of patterns but are not considered to incorporate intelligent decision processes offered by vari­ous models of fuzzy clustering. Integration of fuzzy membership values of samples into neural network processing generates more powerful models for autonomous and intelligent pattern recognition algorithms. Efficient object extraction for image segmentation as well as vector quantization for image coding can be achieved by neuro-fuzzy clustering algorithms. Examples of both for noisy synthetic as well as natural gray-level/color images are demonstrated in the spatial domain and in the wavelet-transformed domain.

Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images

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Sunanda Mitra

Papers

Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing