However, in most cases, the FOV of a lens is expressed using DFOV or Diagonal Field of View. So, you might have to calculate the DFOV value as well. Let us see how that is done.

The function of the sensor is to collect external information, that is, to capture images. The target object is illuminated by a light source, and the reflected light represents the relevant information of the measured target, which is the same as the principle of human eyes seeing objects. The reflected light enters the camera lens, and the process of imaging by the camera is called capturing the image.

The computer is the core of a PC-based visual system. Its function is to complete the processing of image data and most of the control logic. For detection-type applications, a higher-frequency CPU is usually required, which can reduce the processing time. At the same time, in order to reduce the interference of electromagnetic, vibration, dust, temperature, etc. in industrial sites, industrial-grade computers must be selected.

It belongs to an imaging device. Usually, the visual system is composed of one or more imaging systems. If there are multiple cameras, the image card can be switched to obtain image data, or synchronous control can be used to obtain the food of multiple camera channels at the same time. According to the needs of the application, the camera can output standard monochrome video (RS-170°CCTR), composite signal (YCO.RGB) signal, or non-standard progressive scan signal, line scan signal, high-resolution signal, etc.

Machine vision

In embedded vision – in most cases – the image sensor is chosen first. This would mean that the choice of lens is heavily determined by the sensor you use (since AFOV depends on the sensor size). For a given sensor size, to achieve a wider FOV, you need to go with a short focal length lens and vice versa. However, since the focal length cannot be made shorter beyond a point, increasing the sensor size also helps to achieve a wider FOV.

Cognex scanner

Focal length is the defining property of a lens. It is the distance between the lens and the plane of the sensor, and is determined when the lens focuses the object at infinity. It is usually represented in millimeters. Its value depends on the curvature and the material of the lens.

Machine vision system refers to the process of converting the captured target into image signals and transmitting them to a dedicated image processing system through machine vision products (i.e. image capture devices, which are divided into CMOS and CCD), and converting them into digital signals according to information such as pixel distribution, brightness, and color. The image processing system performs various operations on these signals to extract the characteristics of the target and then controls the equipment on site according to the judgment results.

Usually installed in the computer in the form of a plug-in card, the main job of the image acquisition card is to transmit the image detected by the camera to the computer host, it converts the analog or digital signal from the camera or a certain format of image data stream. At the same time, it can control some parameters of the camera, such as trigger signal, exposure, integration time, shutter speed, etc. Usually, different types of cameras have different hardware structures of image acquisition cards, and also different bus forms, such as PCI. PCI64. Compact PCI, PC104. ISA.

So if you are looking for help in picking and integrating the right camera into your embedded system, please don’t hesitate to reach out to us at camerasolutions@e-consystems.com. Meanwhile, you could browse through our complete portfolio of cameras here.

The inspection system detects defects in pharmaceutical glass bottles (including white bottles, brown bottles and bottles with scales). The inspection information of pharmaceutical glass bottles is shown in Figure 1-3. The height of the inspected bottle is 15~150mm, and the inspection speed is required to be 0~280 per minute.

Prabu is the Chief Technology Officer and Head of Camera Products at e-con Systems, and comes with a rich experience of more than 15 years in the embedded vision space. He brings to the table a deep knowledge in USB cameras, embedded vision cameras, vision algorithms and FPGAs. He has built 50+ camera solutions spanning various domains such as medical, industrial, agriculture, retail, biometrics, and more. He also comes with expertise in device driver development and BSP development. Currently, Prabu’s focus is to build smart camera solutions that power new age AI based applications.

When the glass bottle passes on the conveyor belt, the system uses an external trigger method to accurately capture the images of the four sides and one front of the glass bottle at a fixed position, and then transmits the image to two high-performance processors for processing and analysis, and then summarizes the results on a server for unified control and display.

Industrialmachine vision

Image processing can be divided into preprocessing and measurement processing. Preprocessing is to correct the brightness of the image, extract and filter the color, and convert the binary data, that is, to convert the collected image into information that can be recognized by the computer. Measurement processing first matches or collects information for the part that needs data collection, and then judges the collected data. For example, the detection of size, that is, each size needs to have a tolerance upper and lower limit judgment, which is completed by software, that is, the core measurement processing is to perform measurement judgment processing, which is usually completed by the hardware acquisition card and software.

The visual software completes the image analysis (unless it is only used for monitoring), and then needs to communicate with the external unit to complete the control of the production process. Simple control can directly use the I/O of some image acquisition cards, while relatively complex logic and motion control must rely on additional programmable logic control units and motion control cards to achieve the necessary actions.

Cognex

Machine vision systems are mainly composed of image acquisition units, image information processing and recognition units, result display units, and visual system control units. The image acquisition unit obtains the image information of the target object to be measured and transmits it to the image information processing and recognition unit. Since the machine vision system emphasizes accuracy and speed, the image acquisition unit is required to provide clear images in a timely and accurate manner. Only in this way can the image information processing and recognition unit obtain correct results in a relatively short time. The image acquisition unit is generally composed of a light source, a lens, a digital camera, and an image acquisition card. The acquisition process can be simply described as the digital camera shooting the target object and converting it into an image signal under the condition of illumination provided by the light source, and then transmitting it to the image information processing and recognition unit through the image acquisition card. The image information processing and recognition unit performs various operations on the grayscale distribution, brightness and color of the image, extracts the relevant features of the target object, completes the measurement, recognition and NG (no) judgment of the target object, and provides its judgment conclusion to the visual system control unit. The visual system control unit controls the on-site equipment according to the judgment conclusion to realize the corresponding control operation of the target object.

Information is output to the executor. Since the machine vision system itself belongs to the collector, it does not have the ability to execute, but it is the eyes and judgment basis of the executor, so it will pass the captured information to a third party.

From the above workflow, it can be seen that the machine vision system is a relatively complex system. Most of the monitored objects are moving. Since the matching and coordinated actions of the machine vision system and the moving objects have to be pin-point precise, strict requirements are placed on the action time and processing speed of each part of the system. In some application fields (such as robots, flying object guidance, etc.),this requirement extends to the weight, volume, and power consumption of the vision systems.

To learn everything about choosing the right lens for your embedded vision system, please visit the article How to choose the right lens for your embedded camera application.

Machine vision software is used to complete the processing of input image data, and then obtain the result through certain calculations. The output result may be PASS/FAIL signal, coordinate position, string, etc. Common machine vision software appears in the form of C/C++ image library, ActiveX control, graphical programming environment, etc. It can be dedicated (such as only for LCD detection, BGA detection, template alignment, etc.), or it can be applicable (including positioning, measurement, barcode/character recognition, spot detection, etc.).

Computervisiontutorial

Usually in the form of fiber optic switches, proximity switches, etc., are used to determine the position and state of the object being used. Tell the image sensor to perform correct acquisition.

From this equation, it can be understood that the shorter the focal length, the wider the AFOV, and vice versa. This is clearly depicted in the figure below:

Field of view and focal length are two of the most important concepts when it comes to lenses. While focal length is the defining property of a lens, field of view can vary depending on certain other parameters. And when you select a lens for your embedded vision application, you need to make sure that you pick the right one for your sensor such that the desired field of view is achieved.

Computervision

In this article, we attempt to learn what focal length and field of view are, their differences, and why it is important to understand the two concepts thoroughly when it comes to choosing a lens for your embedded vision application.

e-con Systems has been raising the bar in embedded vision for close to two decades now. With a wide portfolio of MIPI cameras, USB cameras, GMSL2 cameras, FPD-Link III cameras, and GigE cameras, e-con stands true to its vision of enabling machines to see and understand the world better every single day. We have our cameras deployed in more than 300 customer products and have shipped over 2 million cameras globally.

Field Of View is the maximum area of a scene that a camera can focus on/capture. It is represented in degrees. Depending on how you measure it, FOV can be represented either vertically, horizontally, or diagonally as shown in the image below:

An imaging accessory, it often plays a vital role in the quality of imaging. LED lights of various shapes, high-color fluorescent lights, fiber optic halogen lamps, etc. can be used as light sources.

Now DFOV can be calculated by replacing HFOV with it in the above equation. Since AFOV and working distance are known entities, DFOV can be derived using this.

Picking the right lens considering multiple factors can sometimes be overwhelming. And this is where e-con Systems can help. While integrating our camera modules, we work closely with our customers to help them choose the best-fit lens for their application. We also extend lens fixation and lens mount customization services.

Usually, the horizontal dimension (which is nothing but the HFOV) and the working distance are given values. Using these, you would be able to calculate AFOV.