Combining OpenCV with Computer Vision

Computer vision is an interdisciplinary field of science that deals with how computers gain high-level understanding from digital images or videos. Computer vision is one of the fastest growing fields in organizations and academia worldwide. The reasons behind the growth include affordability of processing power, open source computer vision algorithms, and of course the availability of cheaper cameras. Computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and take action or provide advice on that information. In addition, computer vision gives machines the ability to perceive, observe, and understand, just as artificial intelligence gives machines the ability to think.

Due to its nature, computer vision is used in multiple industries including energy, utilities, manufacturing, and automotive, and the market is still expanding rapidly. OpenCV is a machine learning library designed to provide a common infrastructure for computer vision applications and facilitate the use of machine perception in commercial products. For those unaware, a library in computer science is a collection of files, programs, routines, scripts, or functions that can be referenced in programming code. OpenCV is designed for computational efficiency, including a strong focus on real-time applications. OpenCV aims to provide standard infrastructure for computer vision applications, giving users access to more than 2,500 traditional and cutting-edge algorithms. These algorithms can be used to recognize faces, remove red eyes, identify objects, extract 3D models of objects, track moving objects, stitch large numbers of frames into high-resolution images, and more.

What is Open Computer Vision Library (OpenCV)?

OpenCV is widely used in image processing. It can read and store pictures, videos, matrix operations, statistics, image processing, etc. It can be used in object tracking, face recognition, texture analysis, dynamic video image processing, etc.

OpenCV provides a simple GUI interface, such as displaying images on the screen, adding sliders on the window, and detecting mouse and keyboard input, which is convenient for us to verify or present the results. However, because OpenCV was originally designed to focus on the processing of algorithms, the support for system hardware and the integrity of interface components are not high, so if you want to develop a complete C/C++ application, you still need to use Qt, application frameworks. The functions provided by OpenCV are convenient for us to deduce more advanced image processing algorithms. This part is like the function of MATLAB, but the execution speed is much faster than that of MATLAB, usually faster than the functions written in C/C++. In addition to C/C++, OpenCV provides support for other programming languages, such as Java or Python, which can support quite a few programs.

Where can OpenCV be Applied?

OpenCV contains several libraries of real-time computer vision functions. Therefore, OpenCV is widely used in image processing, such as real-time face detection and recognition, object recognition, motion recognition, object tracking, dynamic video, etc., after capturing images for recognition. With function libraries related to machine learning and deep learning, Computer Vision, and artificial intelligence (AI), applications are used in life and work, such as automatic parking, assisted driving, lane deviation detection, and other technologies. It is closely related to object detection. More application of this technology in industrial production lines to improve product defect rate, and object detection accuracy has always been the focus of everyone's attention, and it is expected that OpenCV can bring different contributions.

• Image processing.
• Human Machine Interface (HCI).
• Object identification.
• Image cutting.
• Face recognition.
• Motion Detection.
• Scene Refactoring (Structure from Motion).
• Stereo and multi-camera corrections and depth calculations.
• Mobile Robot Vision.

What is Computer Vision?

Computer vision is the process of converting still image or video data into a decision or a new way of expression in the future, so these transformations are all to achieve a certain goal.

The process of separating and extracting a specific area from other parts of an image is image segmentation. Because the image segmentation process distinguishes the foreground object and the background in the image, it is usually called the image binarization process. Image segmentation is important in image analysis, image recognition, image detection, and so on.

In the field of computer vision, segmentation refers to the process of subdividing a digital image into multiple image sub-regions. The purpose of image segmentation is to simplify or change the representation of images to make them easier to understand and analyze. Image segmentation is to fix objects and boundaries in bitmap images. Image segmentation is the process of tagging each pixel in an image so that pixels with the same tag have some common visual property. The result of image segmentation is a collection of sub-regions on the image or a collection of contour lines extracted from the image (edge detection). Each pixel in a subregion is similar under some measure of characteristic or computed characteristic, such as color, brightness, and texture. Adjacent regions are very different under the metric of a certain characteristic.

In the process of image segmentation, the image can be regarded as an ordered set composed of pixels, and image segmentation is the process of dividing this set into several subsets according to certain rules. In the process of image segmentation, the image can be regarded as an ordered set composed of pixels, and image segmentation is the process of dividing this set into several subsets according to certain rules.

Image Segmentation Method:

• Threshold value method: This method is to determine a certain threshold value according to the distribution characteristics of the gray value of the image to perform image segmentation.
• Boundary segmentation method: This method performs image segmentation by detecting the boundary that closes a certain area. In layman's terms, this kind of method cuts out the area enclosed by it along the closed edge line.
• Area extraction method: The characteristic of this type of method is to perform image segmentation according to the difference between a specific area and other background areas.