## 1 Introduction  
The vision system is a crucial sensor implemented in manufacturing processes to enlarge its peripheral abilities and possibilities to the Industry 4.0 concept inclusion [1]. Presently, current research in this field shows that the determination of the object's parameters (with 3D view orientation) is possible with the help of at least two single-camera sensors. The advantage of this principle is the easy implementation to industrial purposes in order to sense, record, and transmit the obtained picture by an interface such as serial interface with the connection of a PC, or an additional processing system. Images that we receive in such a way are subsequently evaluated concerning the necessary information extraction of the relevant sensed product [2]. Usually, after this process, we can subsequently determine and compile the necessary process data of the sensed object. This study investigates the classical approaches and techniques for determining necessary sensing object characteristics when randomly oriented products are consistently moving, for example, by a conveyor at an automated workplace. The principles of calculating the displacements of two captured pictures using the DIC technique is also known. It is essential to note that this method is sensitive to the average gray intensity of the images [3]. However, the intelligent cameras still further support the manufacturing engineering and simplify it because the image analysis is run directly inside the CCD system [4]. Their central part usually consists of the processor and the algorithm setting necessary for an object's properties determination. As part of the CCD, the system also consists of the image sensor that can be described as: \"the high-quality device that can recognize monochromatic pictures in resolution, what we need, and with speed what is necessary\" (60 per sec, for example) [5]. Meanwhile, we can produce sharp pictures that increase algorithms' precision, such as \"edge detection\" and \"pattern recognition\". The next step consists of reliable information providing (target coordinates for robotic arm) that are sent to the advanced control system of the workplace. The final goal is to monitor and ensure the product's correct grasp. Moreover, the vision systems demonstrate their irreplaceability when it is necessary to implement overall correctness for each sensed product (TQM). A good example is implementing an infrared camera system for temperature distribution of the composites [6]. Inspection of the objects, finding the parameters such as color, shape, and so on is their primary purpose, see Figure 1.  
Due to the application attractiveness, many intelligent camera manufacturers have broadly comparable parameters [7]. The gradually increasing deployment of vision systems under the pressure of the emerging market has caused this issue to have been studied widely [8]. Yet, over the past few decades, their complexity and ability to control processes and monitor products in an automated production workplace have become attractive to many global manufacturers [9]. In recent years, the vision systems market has focused mainly on intelligent cameras with a high frame rate, excellent electrical coverage, and a powerful digital signal processor in one compact unit. Their advantage is, in particular, a more straightforward implementation of the application and their increased robustness. Parameters such as flux viscosity, dipping acceleration, dipping time, and flux stable time can be investigated using a high-speed camera system and MATLAB data processing [10]. Unfortunately, there are still few solutions focused on affordability and applicability to the situations where the pallet deployment cannot be used in industry [11]. Our current research is based in this area, where we try to process the acquired image using a comprehensive vision system OMRON F150-3. This system consists of two CCD sensors, which are placed next to each other [12].