Dense learning by high dimensional SOMs composed of input-output fusion vectors for interactive image segmentation

Hotaka Takizawa

doi:10.1117/12.2520491

22 March 2019 Dense learning by high dimensional SOMs composed of input-output fusion vectors for interactive image segmentation

Hotaka Takizawa

Author Affiliations +

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110492L (2019) https://doi.org/10.1117/12.2520491
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

This study proposes an interactive image segmentation method based on high dimensional self organizing maps (SOMs). The proposed method was applied to gray-scale and color images. The experimental results demonstrated that higher dimensional SOMs were able to achieve more accurate segmentation.

Citation Download Citation

Hotaka Takizawa "Dense learning by high dimensional SOMs composed of input-output fusion vectors for interactive image segmentation", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110492L (22 March 2019); https://doi.org/10.1117/12.2520491

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