19.5 A Brief Survey of Speech Enhancement2 19.5.1 Introduction Speech enhancement aims at improving the performance of speech communication systems in noisy environments. Speech enhancement may be applied, for example, to a mobile radio communication system, a speech recognition system, a set of low quality recordings, or to improve the performance of aids for the […]
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Computer Systems
Applications of Machine vision systems
19.4.1 Applications Machine vision systems have uses in a wide variety of disciplines, from medicine to robotics, from automatic inspection to autonomous navigation, and from document analysis to multimedia systems. New machine vision systems are constantly emerging and becoming a part of everyday life. In this section, we present a brief description some of the […]
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Three-Dimensional Object Recognition , Dynamic Vision
19.4.1 Three-Dimensional Object Recognition The real world that we see and touch is primarily composed of three-dimensional solid objects. When an object is viewed for the first time, people typically gather information about that object from many different viewpoints. The process of gathering detailed object information and storing that information is referred to as model […]
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Segmentation , Edge-Based Segmentation , Gradient , Laplacian , Laplacian of Gaussian , Surface Fitting , Edge Linking , Region-Based Segmentation , Region Formation , Split and Merge , Chain Codes , Polygonalization , One-Dimensional Signatures , Boundary Descriptors , Feature Extraction , Critical Points , Interesting Points , Matching , Point Pattern Matching , Template Matching and Hough Transform .
Segmentation An image must be analyzed and its relevant features extracted before more abstract representations and descriptions can be generated. Careful selection of these so-called low-level operations is critical for the success of higher level scene interpretation algorithms. One of the first operations that a machine vision system must perform is the separation of objects […]
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Machine Vision : Introduction , Relationship to Other Fields , Fundamentals of Vision , Image Formation , Imaging Geometry , Image Intensity , Sampling and Quantization , Color Vision , Range Imaging , Imaging Radar , Triangulation , Structured Lighting and Active Vision .
Machine Vision Introduction Machine vision, also known as computer vision, is the scientific discipline whereby explicit, meaningful descriptions of physical objects from the world around us are constructed from their images. Machine vision produces measurements or abstractions from geometrical properties and comprises techniques for estimating features in images, relating feature measurements to the geometry of […]
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Design Example , Genetic Algorithms , Coding and Initialization , Selection and Reproduction , Reproduction and Mutation.
Design Example Consider the design of a simple fuzzy controller for a sprinkler system. The sprinkling time is a function of humidity and temperature. Four membership functions are used for the temperature, three for humidity, and three for the sprinkle time, as shown in Fig. 19.35. Using intuition, the fuzzy table can be developed, as […]
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Recurrent Neural Networks , Hopfield Network , Autoassociative Memory , Bidirectional Associative Memories (BAM) , Fuzzy Systems , Fuzzification , Rule Evaluation and Defuzzification .
Recurrent Neural Networks In contrast to feed forward neural networks, with recurrent networks neuron outputs can be connected with their inputs. Thus, signals in the network can continuously circulate. Until recently, only a limited number of recurrent neural networks were described. Hopfield Network The single-layer recurrent network was analyzed by Hopfield (1982). This network, shown […]
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Learning Algorithms for Neural Networks : Hebbian Learning Rule , Correlation Learning Rule , Instar Learning Rule , Winner Takes All (WTA) , Out star Learning Rule , Widrow-Hoff LMS Learning Rule , Linear Regression , Delta Learning Rule , Error Backpropagation Learning , Special Feed forward Networks , Functional Link Network , Feed forward Version of the Counterpropagation Network and Cascade Correlation Architecture .
Learning Algorithms for Neural Networks Similarly to the biological neurons, the weights in artificial neurons are adjusted during a training procedure. Various learning algorithms were developed, and only a few are suitable for multilayer neuron networks. Some use only local signals in the neurons, others require information from outputs; some require a supervisor who knows […]
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Neural Networks and Fuzzy Systems , Neuron cell and Feedforward Neural Networks.
Neural Networks and Fuzzy Systems New and better electronic devices have inspired researchers to build intelligent machines operating in a fashion similar to the human nervous system. Fascination with this goal started when McCulloch and Pitts (1943) developed their model of an elementary computing neuron and when Hebb (1949) introduced his learning rules. A decade […]
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Process , Select the Right Paradigm and then Automate and Defining Terms .
Process Derived from the combination of steps taken to solve the problems of traditional systems engineering and software development, each DBTF system is defined with built-in quality, built-in productivity and built-in control (like the biological superorganism). The process combines mathematical perfection with engineering precision. Its purpose is to facilitate the “doing things right in the […]
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