### Literature Review  
The authors in [3] introduced the prototype of the machine vision station developed for quality grading of the star-shaped and rich-golden-color carambola fruits. A Fourier-based shape separation method was developed for shape grading, whereas color recognition was established using multivariate discriminant analysis. The Wilks' lambda analysis was invoked to transform and compress the dataset comprising many interconnected variates to a reduced set of variates. A robust scaling normalization was introduced to achieve invariant geometrical transformations. Figure 1: Flexible production system and product. (**a**) Manufacturing assembly system ICIM 3000. (**b**) Parts of the final assembly composition: 1—a base plate, 2—a pen holder, 3—a pen, 4—thermometer, 5—hydrometer. This study's detailed image analysis and processing consisted of three groups: low-level processing, intermediate processing, and high-level processing. The first group includes image acquisition and preprocessing, such as image enhancement, extraction, and restoration. The second group is concerned with image transformation, such as RGB-to-HSI transformation, segmentation, and filtering. Finally, the third group involves recognition and interpretation. The machine vision system inspected starfruit samples, and the results were compared to human judgment. Overall, the vision system correctly classified 100% of the starfruits for shape and 92% for color. The other fruit quality control system based on machine vision was introduced [4]. They developed a robotic system for the automatic, nondestructive inspection and handling of fruit. The authors reported on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Aggarias for the online estimation of the quality of oranges, peaches, and apples and to evaluate the efficiency of these techniques regarding the following quality attributes: size, color, stem location, and detection of external blemishes. Based on Bayesian discriminant analysis, the segmentation procedure allowed fruits to be precisely distinguished from the background. Thus, the determination of size was solved correctly. The colors of the fruits estimated by the system were well correlated with the colorimetric index values currently used as standards. The image analysis was performed by a specific software application developed at IVIA. The system required previous off-line training. Using recorded images of fruit, an expert selected the different regions on the images and assigned all the pixels in every area to one of the pre-determined classes: background, primary color, secondary color, general damage type 1, general damage type 2, a specific feature, stem, and calyx. The classes were chosen to be used for all kinds of fruit used. The secondary color class was not used to train the system to segment those fruits with a single homogeneous color. The use of two classes for the general damage was justified because, for each fruit species, defects had different colors that could be separated as light and darklemishes. Another pre-defined class was employed for detecting specific features of fruits, such as \"russeting\" for Golden delicious apples. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested online, with apples obtaining a good performance when classifying the fruit in batches and repeatability in blemish detection and size estimation of 86 and 93%, respectively. In [9], the authors described the printed board inspection. It utilized an elimination-subtraction method that directly subtracted the template image from the inspected image and then conducted an elimination procedure to locate defects in the PCB. Each detected defect was subsequently classified into one of the seven defect types by three indices: the type of object detected, the difference in object numbers, and background numbers between the inspected image and the template.