What you would learn in Computer Vision In Python! Face Detection & Image Processing course?
Computer Vision A multidisciplinary field, computer vision focuses on how computers can gain high-level understanding of digital images and videos. It is an engineering perspective that automates tasks that the human visual system cannot do. Computer vision refers to the automated extraction, analysis, and understanding of useful information using a single image or sequence of images. This involves the creation of a theoretical and algorithmic foundation to enable automatic visual performance. Computer vision, a scientific discipline in computer science, is concerned with artificial systems that extract information out of images. Image data can come in video sequences, views taken from multiple cameras, or multi-dimensional data gathered from a medical scanner. Computer vision is a technical discipline that applies its theories and models to the construction of computer vision systems.
Computer vision is closely related to image processing, image analysis, and machine vision. These fields have a lot in common, both in terms of the techniques and their applications. These overlaps indicate that the fundamental techniques used in these fields are very similar. This can be read as if there is only one field. However, research groups, scientific journals, conferences, and companies may need to market or present themselves as part of one of these areas. Therefore, different characterizations that distinguish each field from others have been provided.
Computer graphics creates image data from the 3D model, while computer vision often generates 3D models from image data. For example, a trend is being made to combine the two disciplines, as shown in augmented reality.
These characterizations are relevant, but they should not be considered universally accepted.
Image processing and image analytics tend to be focused on 2D images. How to transform one embodiment into another by pixel-wise operations like contrast enhancement, noise removal, or geometrical transformations, such as rotating an image. This description implies that image processing/analysis does not require assumptions or produce interpretations of the image content.
Computer vision also includes 3D analysis of 2D images. This is a 3D analysis of the scene projected onto one or more embodiments; It shows how to reconstruct the structure or other information from the 3D scene using one or more images. Computer vision often relies on more complex assumptions about the location in a snap.
Machine vision refers to applying a variety of technologies and methods to provide automated inspection, process control, and robot guidance in industrial applications. Machine vision tends to focus on industrial applications, e.g., vision-based robots, systems for vision-based measurement, and inspection (such as bin picking). Image sensor technologies and control theory are often integrated with the processing and analysis of image data to control robots. Efficient implementations enhance Real-time processing in both hardware and software. This also means that external conditions, such as lighting, can often be better managed in machine vision than in general computer vision. This can allow for the use of different algorithms.
Imaging is another field that focuses on creating images but also processes and analyzes images. Medical imaging, for example, focuses on the analysis and interpretation of images in medical applications.
Pattern recognition, a field that uses various methods to extract information from signals, is mainly based on artificial neural networks and statistical approaches. This field is significant in that it applies these methods to image data.
There are many applications, from industrial machine vision systems that inspect bottles on a production line to research into artificial intelligence, computers, and robots capable of understanding the world around them, such as those mentioned above. There is a lot of overlap between the computer vision and machine visual fields. Computer vision is the core technology behind automated image analysis, which can be used in many areas. Machine vision combines automated image analysis and other technologies to provide robot guidance and inspection in industrial applications. Computer-vision applications are often pre-programmed to perform a specific task. However, learning-based methods are becoming more common. Computer vision systems can be used for:
In manufacturing applications, automatic inspection e.g.
Assisting humans with identification tasks, e.g., a species identification program
Controlling processes, e.g., an industrial robot
Detecting events for visual surveillance and people counting, e.g., in the restaurant industry.
Interaction, e.g., as input to a device that allows for computer-human interaction
Modeling objects and environments, e.g., medical image analysis or topographical modeling;
Navigation, e.g., by an autonomous vehicle, mobile robot;
Organization information, e.g., for indexing images and image sequences databases.
One of the most popular fields of medical computer vision is medical image processing. This involves the extraction of data from images to diagnose patients. This includes detecting tumors, arteriosclerosis, or other malign conditions; measurements of organ dimensions and blood flow, among others. Another example is the use of MRIs. It supports medical research by providing new data, e.g., about the brain's structure or the quality of medical treatment. Computer vision can also be used to enhance images interpreted by humans, such as ultrasonic images and X-ray images, to reduce noise.
Another area of computer vision is in the industry. This is where information is extracted to support a manufacturing process. Quality control is an example of this. Details or final products can be automatically inspected to identify defects. Another example is the measurement of the position, and orientation details picked up using a robot arm. Optical sorting, which is also used heavily in agriculture to remove unwanted food from bulk materials, uses machine vision.
Computer vision is most prevalent in military applications. Examples include detection of enemy vehicles or soldiers and missile guidance. Advanced systems for missile guidance aim the missile at an area rather than a target. Target selection is made based on local image data. Modern military concepts such as "battlefield Awareness" imply that multiple sensors, including image sensors, provide rich information about combat scenes that can be used for strategic decisions. Automatic data processing is used in this instance to reduce complexity and fuse information from multiple sensors to improve reliability.
Autonomous vehicles are a newer area of application. These include submersibles and land-based vehicles (small robots that can be driven by cars, trucks, or vehicles), aerial vehicles, as well as uncrewed aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (uncrewed) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Computer vision is used to navigate fully autonomous vehicles, e.g., determine their location, create a map of the environment (SLAM), and detect obstacles. It can also be used to detect specific tasks, for example, a UAV searching for forest fires. Systems for autonomous landing aircraft and obstacle warning systems in cars are two examples of such supporting systems. Although several car manufacturers have already demonstrated autonomous driving systems, this technology is still not ready to be released on the market. There are many examples of autonomous military vehicles, ranging from advanced missiles to UAVs that can be used for missile guidance or recon missions. Already, space exploration is being done with autonomous vehicles that use computer vision, e.g., NASA's Curiosity and CNSA's Yatu-2 Rover.
Sensors made of silicon and rubber are used for applications like calibrating robotic hands and detecting micro undulations. You can use rubber to make a mold that can fit over your finger. Inside the mold, you will find multiple strain gauges. You could place the finger mold and sensors on top of small sheets of rubber with various rubber pins. The user can wear the finger mold to trace a surface. The strain gauge data can be read by a computer and used to determine if one or more pins are being pushed upward. The computer can detect if a nail is being pushed up and recognize it as an imperfection on the surface. This technology can be used to obtain precise data about defects on large surfaces. A second variation of the finger mold sensor has a silicon camera suspended within it. The silicon forms a protective dome around the camera's outside. Point markers are embedded in the silicon. These cameras can be attached to robotic hands to provide exact tactile data.
The following are other areas of application:
Support for visual effects creation in cinema and broadcast, e.g., Camera tracking (matchmoving).
Driver drowsiness detection
Tracking and counting organisms within the biological sciences
- OpenCV is a tool that allows you to work with image files
- Understanding the basics of image processing and computer vision
- OpenCV and Python can be used to create shapes in images and videos
- OpenCV allows you to manipulate images, such as blurring, smoothing, thresholding, and morphological operations.
- OpenCV Image Manipulation Fundamentals with Python A refresher session in Python basics is also included.
- Open and Stream Video with Python and OpenCV
- OpenCV and Python allow you to detect objects, such as corners, edges, and grids.
- Use Haar Classifier to create Face Detection Software
- A toolbox of powerful Computer Vision models
- Computer Vision Theory
- Develop powerful Computer Vision Applications