模式识别：第28届DAGM 专题会议/会议录/Pattern recognition 2025 chm pdf kindle rb azw3 下载 115盘

This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.

Image Filtering, Restoration and Segmentation

Ultrasound Image Denoising by Spatially Varying Frequency Compounding

Exploiting Low-Level Image Segmentation for Object Recognition

Wavelet Based Noise Reduction by Identification of Correlations

Template Based Gibbs Probability Distributions for Texture Modeling and Segmentation

Etficient Combination of Probabilistic Sampling Approximations for Robust hnage Segmentation

I)iffusion-Like Reconstruction Schemes fi'om Linear Data Models

Reduction of Ring Artifacts in High Resolution X-Ray Microtomography hnages

A Probabilistic Multi-phase Model for Variational hnage Segmentation

Provably Correct Edgel Linking and Subpixel Boundary Reconstruction

The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images

From Adaptive Averaging to Accelerated Nonlinear Diffusion Filtering

Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration

Shape Analysis and Representation

On-Line, Incremental Learning of a Robust Active Shape Model

Using Irreducible Group Representations for Invariant 3I) Shape Description

Shape Matching by Variational Computation of Geodesics on a Manitbld

A Modification of the Level Set Speed Function to Bridge Gaps in Data

Generation and Initialization of Stable 3D Mass-Spring Models for the Segmentation of the Thyroid Cartilage

Preserving Topological Information in the Windowed Hough Transform for Rectangle Extraction

Recognition, Categorization and Detection

Fast Scalar and Vectorial Grayscale Based Invariant Features tbr 3D Cell Nuclei Localization and Classification

……

Computer Vision and Lmage Retrievel

Anuthor Index

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