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Pattern Recognition and Machine Learning
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, …
Face Detection and Gesture Recognition for Human-Computer Interaction
Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted …
Connectionist Speech Recognition
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous …
Perceptual Organization and Visual Recognition
COMPUTER VISION is a field of research that encompasses many objectives. A primary goal has been to construct visual sensors that can provide general-purpose robots with the same …
Automatic Speech Recognition
Speech Recognition has a long history of being one of the difficult problems in Artificial Intelligence and Computer Science. As one goes from problem solving tasks such as puzzles …
Optical Character Recognition
Optical character recognition (OCR) is the most prominent and successful example of pattern recognition to date. There are thousands of research papers and dozens of OCR …
Integrating Graphics and Vision for Object Recognition
Integrating Graphics and Vision for Object Recognition serves as a reference for electrical engineers and computer scientists researching computer vision or computer graphics. …
Development and Recognition of the Transformed Cell
The study of the phenotypic and genetic features that characterize the malignant cell is a rapidly growing and changing field. Clearly new insights into the processes involved in …
Human Face Recognition Using Third-Order Synthetic Neural Networks
Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to …
Acoustical and Environmental Robustness in Automatic Speech Recognition
The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more …