Machine vision detection image processing system function introduction
Image processing is a kind of image processing and analysis technology based on computer, which is adaptive to various applications. It is an independent theory and technocal field, and also a very important technical support in machine vision.
Machine vision with the function of image processing system is equivalent to people in giving the machine intelligence at the same time, the machine is installed on the eyes, so that the machine can "see", "see accurately", can replace or even better than the human eyes to do measurement and judgement, so that the machine vision system can achieve high resolution and high speed control. Moreover, the machine vision system has no contact with the detected object and is safe and reliable.
Machine vision image processing system on the scene of the digital image signal in accordance with the specific applicaiton requirements of calculation and analysis, according to the results of processing to control the action of the field equipment, its common functions are as follows:
Image acquisition
Image acquisition is the process of obtaining scene images from the work site, which is the first step of machine vision. Most of the acquisition tools are CCD or CMOS camera or video camera. Cameras capture single images, and cameras capture continuous live images, in the case of an image, it is actually a projection of a three-dimensional scene on a two-dimensional image plane, and the color (brightness and chroma) of a certain point in the image is a reflection of the color of the corresponding point in the scene. This is the fundamental basis for the acquisition of images to replace the real scene.
If the camera is analog signal output, the analog image signal needs to be digitized and sent to the computer (including embedded system) for processing. Now most cameras can output digital image signal directly, can eliminate the analog-to-digital conversion this step. Not only that, now the digital output interface of the camera is also standardized, such as USB, VGA, 1394, HDMI, WIFI, Blue Tooth interface, etc., which can be directly fed into the computer for processing, in order to avoid the trouble of adding an image acquisition card between the image output and the computer. Subsequent image processing is often carried out by computer or embedded system in the way of software.
Image processing
Due to the influence of equipment and environmental factors, the collected digital field images are often disturbed to varying degrees, such as noise, geometric deformation, color imbalance, etc., which will hinder the following processing. Therefore, it is necessary to proprocessing the acquired images, common preprocessing includes noise elimination, geometric correction, histogram equialization and so on.
Usually the time domain or frequency domain filtering method is used to remove the noise in the image. The geometric distortion of the image is corrected by geometric transformation. Histogram equalization and homomorphic filtering are used to reduce the color deviation. In short, through this series of image preprocessing technology, the acquisition of images for "processing", for the application of body machine vision to provide "better", "more useful" images.
Image segmentation
Image segmentation is according to the application requirements, the image is divided into each characteristic area, from which to extract the interested target. The common features in images are gray scale, color, texture, edge, corner and so on. For example, the image of automobile assembly line is segmented into background area and workpiece area, which are provided to the subsequent processing unit to process the installation part of the workpiece.
Image segmentation has been a difficult problem in image processing for many years, up to now, there are many kinds of segmentation algorithms, but the effect is often not ideal. Recently, deep learning based on neural network has been used to segment images, and its performance is better than traditional algorithms.
Target recognition and classification
In manufacturing or security and other industries, machine vision is inseparable from the target of the imput image recognition and classification processing, in order to complete the subsequient judgement and operation on this basis, identification and classification techniques have a lot in common, often after the completion of the target identification, the target category is also clear, recently, image recognition technology is surpassing traditional methods, forming intelligent image recognition methods with neural network as the mainstream, such as convolutional neural network, regression neural network and other superior performance methods.
Target positioning and measurement
In intelligent manufacturing, the most common work is to install the target workpiece, but it is often necessary to locate the larget before installation, and it is necessary to measure the target after installation. Both installation and measurement need to maintain high accuracy and speed, such as millimeter accuracy (or even less), millisecond speed. This kind of high precision, high speed positioning and measurement, relying on the usual mechanical or manual method is difficult to achieve. In machine vision, image processing is used to process the image on the installation site, and the complex mapping relationship between the target and the image is processed, so as to quickly and accurately complete the positioning and measurement tasks.
Target detection and tracking
Moving object detection and tracking in image processing is to detect whether there is a moving object in the scene image captured by the camera in real time, and predict its next direction and trend of movement, namely tracking. And timely submit these motiondata to the follow-up analysis and control processing, the formation of the corresponding control action. Image acquisition generally uses a single camera, or two cameras if necessary, to imitate human binocular vision and obtain three-dimensional information of the scene, which is more conducive to target detection and tracking processing.
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