Semantic segmentation and scene understanding with image color identification

Shiela Watts; Bob Rogers

doi:10.30574/ijsra.2021.2.2.0006

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Semantic segmentation and scene understanding with image color identification

Shiela Watts ^*and Bob Rogers

Department of Electrical and Computer engineering, University of California, San Diego.

Research Article

International Journal of Science and Research Archive, 2021, 02(02), 313-316.

Article DOI: 10.30574/ijsra.2021.2.2.0006

DOI url: https://doi.org/10.30574/ijsra.2021.2.2.0006

Publication history

Received on 10 March 2021; revised on 20 May 2021; accepted on 28 May 2021

Abstract

Semantic segmentation and scene understanding are pivotal in computer vision, particularly for applications like autonomous driving, medical imaging, and robotics. This paper explores how incorporating image color identification can enhance the performance and accuracy of semantic segmentation models. Through a series of experiments, we evaluate the contribution of color-based features in deep learning architectures, illustrating improvements in both object recognition and scene context awareness.

Keywords

Semantic segmentation; Machine learning; Deep learning; Image processing

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How to cite this article

Shiela Watts and Bob Rogers. Semantic segmentation and scene understanding with image color identification. International Journal of Science and Research Archive, 2021, 02(02), 313-316. Article DOI: https://doi.org/10.30574/ijsra.2021.2.2.0006

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