Quick area picture division with the minimum square method

Qiming Zhang; Qin Huang; Kexin Zhao; Zuhan Liu

doi:10.1117/12.3009260

11 October 2023 Quick area picture division with the minimum square method

Qiming Zhang, Qin Huang, Kexin Zhao, Zuhan Liu

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180O (2023) https://doi.org/10.1117/12.3009260
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Picture division has a special meaning for computer visualization and schema identification. Fast target extraction from deterministic images is an important problem facing real-time picture manipulation. Traditional areal models rely on globally converged messages to achieve fault-minimized segmentation. Its image segmentation is ineffective and takes up a lot of time. To address this problem, this paper proposes a model that Fast Region Image Segmentation of the Least Squares (FRISLS). Specifically, the target as well as the backdrop of the primary picture is approximated by just a pair of constants in order to establish the minimum error function. The weight matrix is used to reduce the influence of the background on image segmentation, and least squares are introduced to achieve fast convergence of the model. Through comparison with other area model-based approaches, it is validated the effectiveness of the study. The results indicate that this method ensures high precision of picture division, and enhances the performance of picture splitting efficiency.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiming Zhang, Qin Huang, Kexin Zhao, and Zuhan Liu "Quick area picture division with the minimum square method", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180O (11 October 2023); https://doi.org/10.1117/12.3009260

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KEYWORDS

Matrices

Image segmentation

Visual process modeling

Image processing

Contour modeling

Head

Machine vision

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