## 2 Related Work
### Related Research Involving Visual Extreme Interpretations  
For industrial vision-based applications, a detailed study conducted by [PERSON] et al. [27] represents a lot of different applications which includes automated visual inspection, process control (for tracking different documents) in biomedical, pharmaceutical, metal finishing, corrosion detection in pipes, automotive production of assembly lines. Similarly, part identification is another domain for sorting, grading, and identifying different objects in applications. Moreover, some robotic applications are also backed by visual manipulation for smart guidance systems. All these systems are divided into two different categories which are computer vision (CV) based, and machine learning (ML) based approaches. For visual scene interpretation, CV provides a huge range of methods and algorithms. For such interpretations all the methods at some point must deal with visual extreme where the visual data reaches to extremely low or high light conditions. [PERSON] [31], considered the histogram of visual scenes and identified average difference between the movie frames. The study incorporates the manipulation of spatial coordinates in terms of HSV color model. For automated visual inspection of industrial material, study [32] for component handling image acquisition and enhancement approaches are recommended along with the usage of control systems. This study focuses on circuit board inspection and mentions the possible presence of lightning environments in the workflow. Another study [33], tries to detect dark objects by detecting shadows in digital image. The research mentions the detection of visual extreme, i.e., dark, however, it does not mention the classification of the  
Figure 1: Application Domains involving Visual Extremes. entire scene. The study just tries to detect apart of shadowy regions using high residual model and reduction of mismatching around boundary region of shadowy parts by using edge matting technique. The study is detailed and revolves around the detection of dark regional parts. Similarly, a lot of research is conducted over time in the domain of machine learning. In [34], an automated pedestrian detection system is proposed, the proposed setup of the framework uses movement analysis and a support vector machine together to detect the moving pedestrian in semidark environments. The problem with the conducted research is that it is trained and tested in controlled environment of lightning conditions. No mechanism is given for automated identification of image luminance. In [35], a neural network based approach is used for the semantic segmentation of visual scenarios including daytime and night-time scenes. The proposed model adaptation suggests the progressive adaptation of network to be trained on daytime and night-time scenes. The proposed methodology exploits and transfers knowledge from daytime scenes along with large scale annotations. The study mentions the lightning conditions in detail. However, it only covers the external environment under controlled conditions of twilight time and dusk time. There is no proper functionality as some handler in the form of automated solution as part of the model itself. In [36], underwater images are exploited for the semantic segmentation of the marine biological bodies as the underwater images can be dark in nature. The research employs color correction along with the Deeplab V3+ which is deep learning module for segmentation, the research emphasizes on the use of color correction but does not work on the elimination of the luminance extremes such as dark scenes. The referred study only enhances the incoming image using color correction method UCM. In [37], yet another deep learning based framework is used for image dehazing as it mentions some of the information for the exploitation and manipulation of dark color channel prior to the actual functionality of the framework for the semantic segmentation of the images in presence of foggy environment.