Signal and Image Processing With Neural Networks: A C++ Sourcebook/Book and 3 1/2 Disk

This unique book/disk set is the only guide you need to learn practical, proven techniques for integrating traditional signal/image-processing methods with modern neural networks. It provides thorough, clear, and highly readable coverage of complex-domain neural networks, which are generally superior to the usual real domain models when processing complex data. Signal and Image Processing with Neural Networks presents the only detailed descriptions available in print of standard multiple-layer feedforward networks generalized to the complex domain. Always stressing the practical and the intuitive, this book/disk set will enable you to perform all relevant techniques and procedures. C++ subroutines are provided for all techniques, and the discussion of data preparation for neural networks focuses on special requirements for signal and image processing. Techniques presented include gabor transforms in one and two dimensions; wavelets, focusing on the Morlet wavelet, in one and two dimensions; object identification based on shape via Fourier analysis of the perimeter and shape moments; image classification via tone and texture variables; fast transforms in the frequency domain; integration of these techniques with modern neural networks; and much more. The first book to offer practical applications of neural networks to solve problems in digital signal processing and imaging. A highly practical book with a minimum of math and a wealth of examples. Disk includes a complete program for training, testing, and using neural networks along with C++ subroutines for all techniques discussed and source for the book's example code. Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)--all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for which complex-domain networks significantly outperform their real-domain counterparts. The accompanying disk includes complete source code for algorithms discussed with full source for program examples. Content: The Role of Neural Networks in Signal and Image Processing Neurons in the Complex Domain Data Preparation for Neural Networks Frequency-Domain Techniques Time/Frequency Localization Time/Frequency Applications Image Processing in the Frequency Domain Moment-Based Image Features Tone/Texture Descriptors Using the MLFN Program Appendix Bibliography Index. Covering practical applications of digital signal and image processing in C++, this text describes how neural networks can be used to aid in the solution of DSP or imaging problems. The author assumes a minimum of maths in all cases and provides examples for the professional software developer.