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Front-End Vision and Multi-Scale Image Analysis: Multi-scale Computer Vision Theory and Applications, written in Mathematica - Softcover
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Front-End Vision and Multi-Scale Image Analysis. The purpose of this book. Scale-space theory is biologically motivated computer vision. This book has been written in Mathematica. Acknowledgements. 1: Apertures and the notion of scale. 2: Foundations of scale-space. 3: The Gaussian Kernel. 4: Gaussian derivatives. 5: Multi-scale derivatives: implementations. 6: Differential structure of images. 7: Natural limits on observations. 8: Differentiation and regulation. 9: The front-end visual system - the retina. 10: A scale-space model for the retinal sampling. 11: The front-end visual system LGN and cortex. 12: The front-end visual system - cortical columns. 13: Deep structure I. Watershed segmentation. 14: Deep structure II. Catastrophe theory. 15: Deep structure III. 16: Deblurring Gaussian blur. 17: Multi-scale optic flow. 18: Color differential structure. 19: Steerable kernels. 20: Scale-time. 21: Geometry-driven diffusion. 22: Epilog. A: Introduction to Mathematica. A.1. Quick overview of using Mathematica. A.2. Quick overview of the most useful commands. A.3. Pure functions. A.4. Pattern matching. A.5. Some special plot forms. A.6. A faster way to read binary 3-D data. A.7. What often goes wrong. A.8. Suggested reading. A.9. Web resources. B: The concept of convolution. B.1. Convolution. B.2. Convolution is a product in the Fourier domain. C: Installing the book and packages. C.1. Content. C.2. Installation for all systems. C.3. Viewing the book in the help browser. C.4. Sources of additional applications. D: First start with Mathematica: Tips and tricks. D.1. Evaluation. D.2. Images. D.3. Programming. D.4. 3-D. References. Index.
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