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Computer vision will make use of artificial intelligence to view the world. It also thoroughly analyzes the visual information to make the right decisions that are obtained from a situation or environment. The system can learn the videos and images that are digital using computers. This automates the tasks that human vision can attain. There are different methods embraced to acquire, process, analyze and learn digital images. The data is also extracted from the real world to obtain data. This is a computer science branch that focuses majorly on learning images and videos. The computing branch will try to replicate human vision like how images will process. It is a simple task for a human, but there are a lot of algorithms that are used and data that is consumed by computers to attain this goal.
This includes theory and technology to develop artificial systems to get information from videos and images. Computer vision would be applied to control vehicles, detect events, organize data, model objects, or for medical image analysis.
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A few of the applications of computer vision include:
Autonomous vehicles: Computer vision is used in self-driving cars wherein it is used to learn the environment. This helps you to drive on roads safely without hitting pedestrians and ramming into other cars. The best thing is that it can also read traffic signs.
Facial recognition: It is the most commonly used application of computer vision. This technology will help you to recognize people by matching the images of various people in one go. It will make use of the facial traits of the image and compare this with the ones stored in the database. It is mainly used by law enforcement officers to identify criminals. Facebook also makes use of this technology to identify tags.
Military: Computer vision is also used by military officials to recognize enemy troops who are far away. It boosts the targeting abilities and guides the missile to shoot the target precisely.
Health: Health care is another key area where computer vision technology will be used. Imaging has been used in hospitals to give the right treatment to patients who are suffering from serious illnesses. This is used along with deep learning technology to find out the diseases with which the patients are suffering, especially cancer.
Manufacturing industry: Computer vision will ensure that the manufacturing units in the company are running safely. It is easier to carry out the maintenance with ease. It is widely used for predictive maintenance. You can also closely monitor the system with the help of computer vision. If there are any faults in the system, you can detect the faults in the initial stage using this technology and correct them briskly. This can also be used in packaging. It also identifies defective products, which you can remove easily before packaging.
Agriculture: Drones are being used in agriculture. Using drones along with computer vision technology, it becomes easier for farmers to detect the pests and diseases that are spread on the farm. This also automates the inspection process. It reduces the time to inspect the farms and lets, farmers, focus on other things.
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Here are a few computer vision tools that are widely used:
OpenCV: Open CV is an open-source vision library that does various functions related to computer vision and machine learning. It was developed by Intel. It uses different algorithms to carry out tasks such as recognizing the images, detecting facial features, identifying objects, monitoring the moving objects, tracking camera movements, and eye movements, and recognizing the images that look the same in the database. This tool is supported on various OSs such as Windows, Android, Linux, and Mac OS.
Tensorflow: It is an open-source platform that uses different tools, libraries, and resources related to artificial intelligence and machine learning, which will also have computer vision. Tensorflow can also be used to build as well as train machine learning models that are related to computer vision. These include - identifying objects, recognizing faces, and so on. It supports various languages such as Java, Python, C++, and so on.
Mathlab: It is a numerical computing environment that was created by Mathworks. It offers you a toolbox that provides you with a list of algorithms and various functions related to computer vision. Various applications of this tool include - tracking objects, detecting objects, matching features from an image, calibrating cameras, and so on. It is easy to create and train custom object detectors in Matlab with the help of machine learning algorithms. These run using GPUs and processors briskly.
CUDA: CUDA (Compute Unified Device Architecture) is a platform that is widely used by software engineers for processing using GPU. This also comes with the Nvidia performance primitives library that has various functions such as images, signals, and video processing. Developers can use this tool to program in various languages such as C++, MATLAB, Python, and so on.
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Face detection: It is the key concept of computer vision. This is carried out based on many studies like finding out people and marking critical points on the face.
Haar cascades: We offer assignment help on face detection systems and their technologies such as Haar cascades. XML is being designed using haar cascades to find out the frontal face. This is carried out by offering training on thousands of negative images that are superimposed with positive images. Haar cascade is capable enough to detect various features that are from the source.
Dlib frontal face detector: This is a landmark facial detector that comes with various pre-trained models. Dlib will help you estimate various locations along with 68 coordinates that will map the facial points on the face of the person.
DNN face detector in OpenCV: OpenCV is the latest version that has been released recently. It is the library that allows you to use pre-trained networks using Caffee, PyTorch, Torch, TensorFlow, and so on. It classifies the images and detects the contours in every frame wherever the face is there.
MTCNN (Multi-task cascaded convolutional neural networks): MTCNN will be used along with the pre-trained facet model to put the faces of various participants in an image. There is a joint face detection alignment that will work alongside bounding boxes, five-point facial landmarks as well as detection probabilities.
Dlib HOG-based frontal face detector: This is the main attractive feature that you find in HOG-based models. It is the best frontal face detector that would detect faces that are not perfectly frontal.
Transfer learning for classifying images: TL is a research problem that is in machine learning that focuses majorly on storing knowledge that is gained while resolving the problem and applying this to different problems. For instance, the knowledge gained when learning to recognize different types of cars can be used to apply this knowledge to recognize trucks. With the help of traditional methods, there is a lot of data is collected and used. However, sometimes there will be a lack of data and this is dealt with with the help of transfer learning techniques. It is a method that helps you gain knowledge gained through other tasks to tackle the same kind of problem briskly. It reduces the need for gathering a lot of data when solving similar kinds of tasks. There are many pre-trained models that are available online. Using these you can get various features from the dataset when the data is low.
Optical character recognition (OCR): Main application of artificial intelligence is OCR, which is a technology that allows you to convert different documents such as scanned paper documents, and images that are captured using a digital camera into data that is easier to edit and search.
Gesture recognition: Gesture recognition is a kind of perceptual computing interface that enables systems to capture various gestures and interpret them carefully. The key definition of gesture recognition is the ability of a system to learn gestures and execute commands accordingly. Gesture recognition will use computer vision to get pictures that have human hands with the help of cameras and then use image processing and machine learning to judge and recognize gestures.
Human pose estimation: This is the key problem that uses computer vision technology. This is the major step to learning images and videos of people. Human pose estimation is a problem that localizes human joints in images as well as videos. This helps you to search for a specific pose. This is widely used in animation, gaming, action recognition, and so on. The deep learning app will make use of human pose estimation to thoroughly analyze the movements of a basketball player.
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