Neighborhood Rank Order Coding for Robust Texture Analysis and Feature Extraction
This work addresses texture analysis for image processing systems, but it appears incremental as it builds on existing pulse computation schemes.
The paper tackled the problem of robust texture analysis and feature extraction by proposing a pulsed local orientation coding (PLOC) operator based on pulse order encoding, achieving information compaction for subsequent processing stages.
Research into the visual cortex and general neural information processing has led to various attempts to integrate pulse computation schemes in image analysis systems. Of interest is especially the robustness of representing an analogue signal in the phase or duration of a pulsed, quasi-digital signal, as well as the possibility of direct digital interaction, i.e. computation, among these signals. Such a computation can also achieve information compaction for subsequent processing stages. By using a pulse order encoding scheme motivated by dendritic pulse interaction, we will show that a powerful low-level feature and texture extraction operator, called Pulsed Local Orientation Coding (PLOC), can be implemented. Feature extraction results are being presented, and a possible VLSI implementation is detailed.