Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute

2017/10/29 by Birgitta Dresp, Birgitta Dresp-Langley, Dresp-Langley, Birgitta +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Neural Networks and Applications #Neural dynamics and brain function #cs.CV #cs.CY

paper · pdf · doi:10.48550/arxiv.1710.10648

12 pages, 10 Figures

arxiv created 2017/10/29 · openalex publication_date 2017/10/29 · arxiv updated 2017/10/31 · openalex created_date 2022/08/29 · openalex updated_date 2026/07/28

Abstract

The quantization error (QE) from SOM applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images, is proven a reliable indicator of potentially critical changes in image homogeneity. The QE is shown to increase linearly with the variability in spatial contrast contents across time when contrast intensity is kept constant.

Cited by

• Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years

Related

• Scene-and-Process-Dependent Spatial Image Quality Metrics
• Volumetric Calculation of Quantization Error in 3-D Vision Systems
• Probing the State of the Art: A Critical Look at Visual Representation Evaluation
• Variance and Error in One-Step Phase-Retrieval
• Histogram Equalization Of The Image