# Hair Removal Combining Saliency, Shape and Color
## 4 Discussion and Conclusions  
In this paper, we propose the method HR-SSC based on the combined use of saliency, shape, and color. Initially, the computation burden of the hair removal process is lowered optionally by reducing the size of the image. Then, pseudo-hair regions and border/corner components are determined and employed in the successive process of hair mask detection. Successively, the image is restored by an inpainting process. A further contribution of this paper includes the proposal of a method for qualitative and quantitative evaluation of an HR method, and the availability of appropriate datasets to be used for testing and comparing by others. According to the proposed evaluation method, we perform a detailed quantitative and qualitative analysis of the experimental results on these datasets. Specifically, we qualitatively evaluate the performance of the proposed method and six state-of-the-art methods. We quantitatively evaluate the performance of HR methods under examination using a hair simulation technique applied on available dermoscopic image datasets, nine commonly adopted quality measures, area criteria, and FDR/TDR indicators.  
Based on the experimental results and the performance evaluation, HR-SSC detects and removes the hair from the dermoscopic image by preserving the image features for its subsequent image segmentation process. Moreover, HR-SSC has a competitive and satisfactory performance concerning other considered methods as the probability of missing hair regions and/or detecting false hair regions is low. This is visually evident from the evaluation carried out, but it is to a lesser extent if we restrict the analysis to _NH13-data_. Indeed, as also reported in [17], the quantitative results on _H13 GAN-data_ and _H13 Sim-data_ (see Tables 1 and 2) indicate that the method Xie statistically outperforms the other methods under consideration, including HR-SSC. However, this experimental evidence does not match the qualitative/quantitative results obtained on the larger dataset _HSim-data_ and on its sample, which, on the contrary, indicate a better performance of the proposed method. This trend is validated also by the qualitative evaluation based on area and TDR/FDR as reported respectively in Sections 3.3.2 and 3.3.3.  
In summary, according to the performance evaluation, HR-SSC achieves good qualitative and quantitative results with an adequate balance. Moreover, it detects hair regions rapidly by processes with limited complexity. The results have also demonstrated the effectiveness and the utility of the employment of saliency, shape, and color information for hair removal problems. Finally, the implementation does not require any extensive learning based on a high number of parameters and labeled training images, and its execution time is quite fast.  
In future investigations, there is room to extend the comparative studies with other existing methods and to improve this work by applying more efficient and efficacy inpainting methods to increase the performance quality.  
This work was supported by GNCS (Gruppo Nazionale di Calcolo Scientifico) of the INDAM (Istituto Nazionale di Alta Matematica).  
Not applicable.  
The data presented in this study are openly available at the following GitHub link: [[https://github.com/gramella/HR](https://github.com/gramella/HR)]([https://github.com/gramella/HR](https://github.com/gramella/HR)).  
The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **\textless{}FDR\textgreater{}** & **\textless{}TDR\textgreater{}** \\ \hline [PERSON] & 0.503 & 0.497 \\ [PERSON] & 0.990 & 0.010 \\ HR-SSC & 0.360 & **0.640** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average FDR and TDR on the _HSim-data_—best results are in bold.