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The discrimination of textures is a critical aspect of identi"cation in digital imagery. Texture features generated by Gabor "lters have been increasingly considered and applied to image analysis. Here, a comprehensive classi"cation and segmentation comparison of di!erent techniques used to produce texture features using Gabor "lters is presented. These(More)
This paper presents a study investigating the potential of artiicial neural networks (ANN's) for the classiication and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artiicial Neural Network (ANN) for the multispectral supervised classiication of MR images.(More)
— A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM's) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference(More)
Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional(More)
A syntactic pattern recognition procedure for classification of brain-stem auditory evoked potential (BSAEP) is presented. A pre-processing stage of zero-phase bandpass filtering enhances the peaks and suppresses the noise. A finite-state grammar was designed to identify the peaks. Attributes of the peaks (latencies and amplitudes) that are identified are(More)
Special thanks to the many teachers, administrators, and students who opened their doors for the site visits and interviews that formed the basis for the case scenarios in this handbook. The schools were Crockett Career and Technical High School On a more systemic level, the following people deserve special recognition for the information they provided:(More)