## 2 Related Work
### Introduction to Transparent Image Analysis  
Object analysis is one of the essential branches in robot vision; particularly, the analysis of transparent images of objects (transparent images) is challenging [16]. In the traditional machine learning method, the multiclass fusion algorithm can only extract the shallow features of a transparent image, and the obtained feature layer is incomplete. Practically, it is difficult for multiclass fusion algorithms to detect transparent objects. For example, home robots cannot see objects at all when they detect transparent glassware. The ClearGrasp machine learning algorithm performs well in analyzing transparent objects [17]. It can estimate the high-precision data of transparent objects from RGB-D transparent images, thereby improving the accuracy of transparent object detection.  
Photoelectric sensors are widely used in industrial automation, mechanization, and intelligence as a necessary technical means for analyzing objects. In [18], it uses the properties of light to detect the position and changes of an object. Sensors and smart systems are used to separate recyclable materials (transparent materials and metals) into different bins, without using manpower.  
There are many transparent objects in the industrial field, such as transparent plastics, colloids, and liquid drops. These transparent objects bring considerable uncertainty to products. For factories to have high-quality products, it is sometimes essential to analyze these transparent objects and control their shapes. However, it is difficult to segment the shapes of transparent objects using morphological methods. For instance, [PERSON] et al. used a genetic algorithm to segment a transparent paste-drop-shape in the industry and obtained good performance [19].  
Segmentation of transparent objects is significantly useful for computer vision applications. However, the foreground of a transparent image is usually similar to its background environment, which leads to commonly used image segmentation methods handling transparent images generally. The light-field image segmentation method can accurately and automatically segment transparent images with a small depth-of-field difference and improve the accuracy of the segmentation with less calculations [20]. Hence, they are widely used for the segmentation of transparent images.  
The correct segmentation of zebrafish in biology has extensively promoted the development of the life sciences. However, zebrafish transparency makes the edges blurred during segmentation. The mean-shift algorithm can enhance the color representation in the image and improve the discrimination of the specimen against the background [21]. This method improves the efficiency and accuracy of zebrafish specimen segmentation.  
Visual object analysis is vital for robotics and computer vision applications. Commonly used statistical analysis methods, such as the bag-of-features, are often applied to image segmentation [22]. The principle is to extract local features of the image for segmentation. However, the foreground transparent objects in transparent images do not have complete features; therefore, it is difficult to accurately segment transparent images. The more popular method is the light field distortion feature [23], which can describe transparent objects without knowing the texture of the scene, thus improving the segmentation accuracy of transparent images.