## 2 Related Work
On the other hand, [PERSON] [24] presented sign language recognition that uses PCA as a dimensionality reduction technique and represented the features of the image to provide the orientation of the hand. In [25], PCA characterizes the function of the fingers in a vision-based Chinese SLR system. [PERSON] [26] analyzed the use of PCA for automatic recognition of dynamic gestures of the human arm and the imitation of the robots. In [27], PCA is applied for the recognition of hand gestures in order to represent the alphabet. Furthermore, [PERSON] [28] focused on a skin color model database and a defined threshold, which they compared with a PCA analysis. Segmentation is a crucial step in a system because it provides color information for an image. This project proposes a segmentation technique with the use of two HSV and CIELab color spaces. Some studies employ these color spaces but with different applications; for example, [PERSON] [29] presented a system for automatic transfer function design based on visibility distribution and projective method color mapping. These transfer functions play a key role in representing the volume of medical data. The techniques commonly used in the transfer function design are clustering and segmentation, which divide the volume into a set of clusters or segments. The algorithm uses visibility distribution for the design of a transfer function. For the color transfer function, normalized-cut segmentation algorithm on the IGM (Intensity Gradient Magnitude) histogram is used to divide the histogram into several regions. Then, the CIELab color space is applied to map the center of each region in order to assign a unique color to each region by its projected coordinate. Then, the resulting color transfer function is applied to represent the volume of data and generate the visualization result. Another study with a different application to the previous one but making use of color space was reported by [PERSON] [30]. This article allows the segmentation of regenerated muscle fibers in mice based on superpixels. Superpixels have numerous applications in computer vision including image segmentation. A method called SLIC-MMED (simple linear iterative clustering on multi-channel microscopy with edge detection), which segments muscle fibers in multi-channel microscopy, is proposed. The green channel is processed to extract the nuclei, while the remaining channels (cyan, yellow and red) are used to generate superpixels according to SLIC-MMED algorithm. This work compares the performance of the proposed algorithm with the original SLIC algorithm, the latter operating on color images in the CIELab color space using the components (_l_, \(a\), _b_). However, in SLIC-MMED algorithm, each image channel is used as a component of the feature vector. In the results, the SLIC-MMED algorithm can correctly segment muscle fibers of different sizes with bright and dark regions. The previous works represent that the coloration is widely applied in different fieldsbecause the segmentation allows distinguishing different structures for a better compression of the data, and thus to be able to perform a manipulation of data according to the objective.