We can all agree that computers have come a long way since their invention between 1936 and 1938. Since then, the computer has evolved and revolutionized the way of life today for every human being on this planet.
Computer vision development can be defined as simplifying the scientific process of interpreting digital images and videos through the use of artificial intelligence to derive a conclusion from what the computers see. Making it as simple as the human optical system.
This scientific advancement will see the improvement and development of computer vision software that will customize the video and image analysis process. Furthermore, benefiting industries and businesses in an array of fields, including but not limited to manufacturing, healthcare, and consumer electronics.
The continuous R&D in computer vision development has resulted in the improvement of the processes involved in acquiring, analyzing and understanding the information extracted from either a single or a sequence of digital images.
There are a number of processes that go into the optimization of existing methods and even the creation of algorithms. The main aim is to construct a system that possesses both high speeds of processing and low counts of performance errors. The implementation of computer vision development projects has highly involved Artificial Intelligence techniques.
The main focuses of computer vision development are in Object Recognition and Visual Analysis.
The main task of the Computer Vision system is its ability to recognize whether an image contains a specific object within it. The extraction process involves the detection of objects within a region of interest, then further localizing and classifying images so as to determine the location of where each object appears within the image. Deep learning and computer vision techniques are then applied to develop the algorithms for object detection. These algorithms are used in data capturing, object segmentation, facial analysis, image tagging, and more.
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The visual analysis algorithms are frequently used in systems for automated visual surveillance. These algorithms solve tasks like; understanding the scene, tracking of objects, motion detection.
In order to detect an object that lies in the background, a background subtraction algorithm is utilized. The tracking of objects relies on the algorithm detecting an object in one particular frame and relating it to another object in the next frame. Finally, the aim of the scene analysis algorithm is to identify and recognize activities in a scene. Rule-based approaches are applied to identify abnormal behavior in a video frame, and if detected a specific action like alerting the ambulance, is triggered.
The computer vision development algorithms are currently being utilized in:
- In the care of the elderly
- Cancer detection; skin melanoma
- Video surveillance for the purpose of tracking football players while on the field
There are many practical benefits to be derived from the implementation of computer vision development software. These benefits are simpler and faster processes, reduction of costs, higher accuracy of data-based operations and finally, high-quality results.
This is a guest contributor post.