## 3 Experimental Data and Results
### Usefulness of Camera-Based Optical Communications in the Detection Process
#### 3.1.3 Projective Exposure of Regressive Transformations by Parameterizable Methods and Histograms  
Therefore, when we need to detect objects and states, we use a computer vision application that provides the ability to observe the environment by performing data extraction and analysis. The specialized literature exposes the aspect that the detection of objects, states or pedestrians is an extremely challenging subject in terms of dynamic environments and the need for a series of parameterizable data on the conditions in which we need adaptable algorithms. The probability of a completely autonomous system that would behave ideally in the case of fluctuations is extremely low because, in the external environment, we have variations of images, light, scale, shaded areas, mixed dynamic background and uneven movement. Studies have shown that detecting objects or recognizing actions at night can be much more difficult than during the day. The most recommended method to perform qualitative detection is to use the concepts of key points which are extremely robust classes that describe specific properties for that image. It also performs changes in pixel intensity and orientation, T-junctions, corner abstraction or minimum size. The key points include a set of specific properties and analyze the intensity or orientation, reaching up to the use of 64 elements that contain unique descriptors. In this case, these unique descriptors are elements or key points from different images, which aim to indicate the same characteristic for the same area of the object. In the analysis, we performed a binary process that represents the conversion of an image that has gray tones into an image with black-and-white characteristics using the thresholding method. The methods are different, but they have separately applicable characteristics and analyze the problem in the substrate. The threshold selection analyzed the described section and each gradient using the Otsu method [71], which outlines a histogram for images that it calculates according to an optimal threshold value. The Otsu method is based on a linear image pixel statistic consisting of two classes to binarize the image. This threshold is calculated using first-order moments, average _ave_ and standard deviation _std_. An aspect is differentiated that an independent result of dimensions is obtained, and the histogram \(H\) is normalized as follows:  
\[H_{i}=\frac{n_{i}}{N}, \tag{1}\]  
where \(n_{i}\) represents the total number of pixels of illumination, the level in the image is denoted by \(i\), and \(N\) is defined by the total number of pixels in the image. In the case of the calculation of the mean and the standard deviation, they can be defined as follows:  
\[ave(k)=\sum_{i=1}^{k}i\times H_{i}\;, \tag{2}\]  
\[std(k)=\sum_{i=1}^{k}H_{i}\;. \tag{3}\]  
Thus, we define \(s^{2}\) for each value assigned to \(k\) = 1, \(n\) to \(n\) + 1, 255, \(s^{2}\), and we can calculate it this way:  
\[s^{2}(k)=std(k)\cdot(1-std(k))\cdot(ave(n+1*255)\cdot std(k)-ave(k))^{2}. \tag{4}\]The value of \(k\) helps to maximize the function \(s\) from the optimal threshold value in the whole binary process dedicated to the image [72]. This is because maximization is achieved by separating the classes resulting from gradient extraction. We can determine the threshold \(k\) as follows:  
\[s^{2}(k)=\text{max}\Big{(}s^{2}(k)\Big{)}. \tag{5}\]  
Gradual exposures must show a projective transformation in order to understand the analysis of the pixels in the image [73]. In the case of gradients and exposures, we established the image in a system of \(x\), \(y\), \(z\) coordinates on which the characteristics were matrived. In Figure 6, the representation of point \(A\) is part of the reference for \(O_{XYZ}\) and \(A^{\prime}\) being the design characteristic in the \(O_{XY\prime YZ}\), the plane for reference, where \(A=TA^{\prime}\).