MATLAB Source Code for Shape Context Implementation

This content discusses the MATLAB source code implementation for shape context. To better understand this topic, we recommend first gaining fundamental knowledge about shape context, including its definition, key characteristics, and practical application scenarios. You may also refer to academic papers or open-source projects to deepen your comprehension of the subject. The provided MATLAB implementation demonstrates shape context through two executable demo files. The demo1.m file typically showcases basic shape descriptor extraction using log-polar binning methodology, while demo2.m often illustrates shape matching applications with distance metric comparisons. These demos utilize key functions such as shape point sampling, histogram computation in log-polar space, and chi-square distance calculations for shape similarity assessment. To examine the practical implementation of shape context algorithms, execute both demo1.m and demo2.m files. These demonstrations will help you understand how shape context descriptors are computed and applied in real-world shape recognition tasks, featuring implementation details like coordinate normalization, reference point selection, and histogram normalization techniques.