that the norm-based optimization of 2D-MSPC for the Lena’s image is robust to the norm type.

[0.25]{} ![Impact of using different norm types for the detection of features in Lena’s image. The image of $\mathcal{M}^*$ and the value of the cost function (calculated for the optimal parameter vector) are achieved from based on 1-, 2-, $\infty-$ and $F-$ norms. []{data-label="fig:lena-norm-opt"}](pc_lena_1norm_Mpm_6or "fig:"){width="\linewidth"}

[0.25]{} ![Impact of using different norm types for the detection of features in Lena’s image. The image of $\mathcal{M}^*$ and the value of the cost function (calculated for the optimal parameter vector) are achieved from based on 1-, 2-, $\infty-$ and $F-$ norms. []{data-label="fig:lena-norm-opt"}](pc_lena_2norm_Mpm_6or "fig:"){width="\linewidth"}

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[0.25]{} ![Impact of using different norm types for the detection of features in Lena’s image. The image of $\mathcal{M}^*$ and the value of the cost function (calculated for the optimal parameter vector) are achieved from based on 1-, 2-, $\infty-$ and $F-$ norms. []{data-label="fig:lena-norm-opt"}](pc_lena_Infnorm_Mpm_6or "fig:"){width="\linewidth"}

[0.25]{} ![Impact of using different norm types for the detection of features in Lena’s image. The image of $\mathcal{M}^*$ and the value of the cost function (calculated for the optimal parameter vector) are achieved from based on 1-, 2-, $\infty-$ and $F-$ norms. []{data-label="fig:lena-norm-opt"}](pc_lena_Fronorm_Mpm_6or "fig:"){width="\linewidth"}

Because the PC 